diff --git a/.gitattributes b/.gitattributes index c7d9f3332a950355d5a77d85000f05e6f45435ea..b6d9affb573b0b9c0049173bbf6bf7aae069557a 100644 --- a/.gitattributes +++ b/.gitattributes @@ -32,3 +32,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +data/filtered_train_embedding/correct_cls_train.annoy filter=lfs diff=lfs merge=lfs -text +data/filtered_train_embedding/representative_samples_emb.annoy filter=lfs diff=lfs merge=lfs -text +figures/streamlit-instance_exp-2021-12-02-21-12-66.webm filter=lfs diff=lfs merge=lfs -text +figures/test_20210401.png filter=lfs diff=lfs merge=lfs -text +model/keras_model_0422/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text diff --git a/.streamlit/config.toml b/.streamlit/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..3f04096e3b7912128cd617e460e764489b906745 --- /dev/null +++ b/.streamlit/config.toml @@ -0,0 +1,6 @@ +[theme] +primaryColor="#f21111" +backgroundColor="#0e1117" +secondaryBackgroundColor="#31333F" +textColor="#fafafa" +font="sans serif" \ No newline at end of file diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..708c353064f5480140b78c7e8c4ec2ad321b192d --- /dev/null +++ b/Dockerfile @@ -0,0 +1,46 @@ +FROM python:3.9-slim-bullseye + +# install packages +RUN set -x && \ + apt-get update -y && \ + apt-get upgrade -y && \ + apt install -y --no-install-recommends \ + git\ + wget\ + build-essential \ + apt-utils \ + ca-certificates \ + curl \ + software-properties-common \ + libopencv-dev \ + python3-dev \ + python3-pip \ + python3-setuptools \ + python3-venv \ + cmake \ + swig \ + wget \ + unzip \ + screen \ + nano \ + lxc \ + iptables \ + ca-certificates \ + libfftw3-dev \ + expat \ + libgomp1 + +COPY . /app + +WORKDIR /app + +#Install necessary packages from requirements.txt with no cache dir allowing for installation on machine with very little memory on board +RUN pip install --upgrade pip +RUN pip --no-cache-dir install -r requirements.txt + +#Exposing the default streamlit port +EXPOSE 8501 + +#Running the streamlit app +# ENTRYPOINT ["streamlit", "run", "--server.maxUploadSize=5"] +# CMD ["System_Overview.py"] \ No newline at end of file diff --git a/System_Overview.py b/System_Overview.py new file mode 100644 index 0000000000000000000000000000000000000000..36a351ccb249d9bed954b7b8d691ee264d4d910e --- /dev/null +++ b/System_Overview.py @@ -0,0 +1,64 @@ +""" Resources: + 1- https://streamlit-emoji-shortcodes-streamlit-app-gwckff.streamlit.app/ + 2- https://github.com/giswqs/streamlit-geospatial + +""" + +import streamlit as st + + +USER_GROUPS = ["Developer", "Manager", "Practitioner"] + +st.set_page_config(layout="wide") + + +st.sidebar.title("About") +st.sidebar.info( + """ + Web App URL: + GitHub repository: + """ +) + +st.sidebar.title("Contact") +st.sidebar.info( + """ + Sinan Kaplan: + [GitHub](https://github.com/kaplansinan) | [LinkedIn](https://www.linkedin.com/in/kaplansinan) + """ +) + +if 'user_group' not in st.session_state: + index_tmp = 0 +else: + index_tmp = USER_GROUPS.index(st.session_state['user_group']) + + +#Sidebar for USER GROUPS +st.sidebar.title("USER GROUPS") +backend = st.sidebar.selectbox( + "Select User-Group ", USER_GROUPS, index=index_tmp + ) + +st.session_state['user_group'] = backend + +# Customize page title +st.title("AI Framework Applications") + +st.markdown( + """ + This multipage app template demonstrates various interactive web apps created using [streamlit](https://streamlit.io) and [leafmap](https://leafmap.org). It is an open-source project and you are very welcome to contribute to the [GitHub repository](https://github.com/giswqs/streamlit-multipage-template). + """ +) + +st.header("Instructions") + +markdown = """ +1. For the [GitHub repository](https://github.com/giswqs/streamlit-multipage-template) or [use it as a template](https://github.com/giswqs/streamlit-multipage-template/generate) for your own project. +2. Customize the sidebar by changing the sidebar text and logo in each Python files. +3. Find your favorite emoji from https://emojipedia.org. +4. 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file mode 100644 index 0000000000000000000000000000000000000000..cd6e106f83169b6d506e3cae34fe5788baef58aa --- /dev/null +++ b/model/keras_model_0422/saved_model.pb @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ee9eb3ec2cc875889ed67e08cc20cc2042f5634a6a59d87e2ae31578337d1ee6 +size 9588977 diff --git a/model/keras_model_0422/variables/variables.data-00000-of-00001 b/model/keras_model_0422/variables/variables.data-00000-of-00001 new file mode 100644 index 0000000000000000000000000000000000000000..365cdf96a9d8e3ac3b91f86cfc6b23cab1c9c753 --- /dev/null +++ b/model/keras_model_0422/variables/variables.data-00000-of-00001 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:49c01a4ac2f6e881248d263812ff83bc3ac8f524e0b13a4bc385c4957c13a507 +size 210831092 diff --git a/model/keras_model_0422/variables/variables.index b/model/keras_model_0422/variables/variables.index new file mode 100644 index 0000000000000000000000000000000000000000..24c62272dffbf7c24bd9d11154fa3018e017de40 Binary files /dev/null and b/model/keras_model_0422/variables/variables.index differ diff --git "a/pages/1_\360\237\222\276_Data.py" "b/pages/1_\360\237\222\276_Data.py" new file mode 100644 index 0000000000000000000000000000000000000000..f24c5699f52684d689d97dd192902c6e44e08765 --- /dev/null +++ "b/pages/1_\360\237\222\276_Data.py" @@ -0,0 +1,66 @@ +# Read +import streamlit as st +import sys,os + + +sys.path.append(f'{os.getcwd()}/utils') +from utils.data_users import get_developer_page_layout,get_product_dev_page_layout + + +print(os.getcwd()) +# st.write(st.session_state.user_group) +USER_GROUPS = ["Developer", "Manager", "Practitioner"] + +st.set_page_config(layout="wide") + +if 'user_group' not in st.session_state: + index_tmp = 0 +else: + index_tmp = USER_GROUPS.index(st.session_state['user_group']) + +#Sidebar for USER GROUPS +st.sidebar.title("USER GROUPS") +backend = st.sidebar.selectbox( + "Select User-Group ", USER_GROUPS, index=index_tmp + ) + +st.session_state['user_group'] = backend + +# # with st.sidebar: +# st.sidebar.title("🎈Explore Data Panel") + +st.title("🎈Explore Data Panel") + +st.write( + """ + ## + Examining data is the key factor here and it provides deta-centric approach. Thus this module provides details regarding forllowing aspect of the data . + """) + +list_test = """
    +
  • Data preprocessing: asdsdfdf
    • +
  • Data source: asdsdfdf
    • +
  • Data onboarding: asdsdfdf
    • +
""" +st.markdown(list_test, unsafe_allow_html=True) + + +# with st.sidebar.expander("🧩 COMPONENTS", True): + +# # genre = st.radio("",('Data Info', 'Data EDA', 'Data ONboard')) +# status_a = st.checkbox("Test editor", key="a") +# status_b = st.checkbox("Ace editor", key="b") +# if status_a: +# st.write("A checked") +# if status_a and status_b: +# st.write("They arechecked") + +if backend == "Developer": + get_product_dev_page_layout() + # st.checkbox("Ace editor", key="b", disabled=selected) + # page.item("Ace editor", components.ace_editor) + # page.item("Disqus", components.disqus) + # page.item("Elements⭐", components.elements) + # page.item("Pandas profiling", components.pandas_profiling) + # page.item("Quill editor", components.quill_editor) + # page.item("React player", components.react_player) \ No newline at end of file diff --git "a/pages/2_\360\237\223\235_Model.py" "b/pages/2_\360\237\223\235_Model.py" new file mode 100644 index 0000000000000000000000000000000000000000..bee0f81ee431445490151abf700e190a638fdf2e --- /dev/null +++ "b/pages/2_\360\237\223\235_Model.py" @@ -0,0 +1,44 @@ +import streamlit as st +import sys,os + + +sys.path.append(f'{os.getcwd()}/utils') +from utils.model_users import get_product_dev_page_layout + +# st.write(st.session_state.user_group) +USER_GROUPS = ["Developer", "Manager", "Practitioner"] + +st.set_page_config(layout="wide") + +if 'user_group' not in st.session_state: + index_tmp = 0 +else: + index_tmp = USER_GROUPS.index(st.session_state['user_group']) + +#Sidebar for USER GROUPS +st.sidebar.title("USER GROUPS") +backend = st.sidebar.selectbox( + "Select User-Group ", USER_GROUPS, index=index_tmp + ) + +st.session_state['user_group'] = backend + + + +st.title("Explore Model Panel for OCT Image Analysis") +st.write( + """ + ## + Examining model details for the develop models. This section highlights the overview of the developed model for specific user groups. + Developer, model owners and managers can use this section by hemselves. + """) + +list_test = """
    +
  • Dev
    • +
  • Deploy
    • +
  • General
    • +
""" +st.markdown(list_test, unsafe_allow_html=True) + +if backend == "Developer": + get_product_dev_page_layout() \ No newline at end of file diff --git "a/pages/3_\360\237\216\257_Evaluation_Panel.py" "b/pages/3_\360\237\216\257_Evaluation_Panel.py" new file mode 100644 index 0000000000000000000000000000000000000000..50ad202e3c62c0281d5e6427b7caed237fc56b82 --- /dev/null +++ "b/pages/3_\360\237\216\257_Evaluation_Panel.py" @@ -0,0 +1,35 @@ +import streamlit as st +import sys,os + +sys.path.append(f'{os.getcwd()}/utils') + +from utils.eval_users import get_product_dev_page_layout + +# st.write(st.session_state.user_group) +USER_GROUPS = ["Developer", "Manager", "Practitioner"] + +st.set_page_config(layout="wide") + +if 'user_group' not in st.session_state: + index_tmp = 0 +else: + index_tmp = USER_GROUPS.index(st.session_state['user_group']) + +#Sidebar for USER GROUPS +st.sidebar.title("USER GROUPS") +backend = st.sidebar.selectbox( + "Select User-Group ", USER_GROUPS, index=index_tmp + ) + +st.session_state['user_group'] = backend + +st.title("Evaluation Details") + +st.write( + """ + ## + This section gives on averview of the performance metrics and results on test set. + """) + +if backend == "Developer": + get_product_dev_page_layout() \ No newline at end of file diff --git "a/pages/4_\360\237\224\254_Decision_Exploration_Panel.py" "b/pages/4_\360\237\224\254_Decision_Exploration_Panel.py" new file mode 100644 index 0000000000000000000000000000000000000000..03416cfe32840e10b68d190fa543ede8a2b647fc --- /dev/null +++ "b/pages/4_\360\237\224\254_Decision_Exploration_Panel.py" @@ -0,0 +1,454 @@ +import os +import sys +import json +import shutil +import urllib.request +from pathlib import Path +import pathlib +import time +import urllib +from ast import literal_eval +import albumentations as A +import tensorflow as tf + +import cv2 +import numpy as np +import pandas as pd +import plotly.express as px +import matplotlib.pyplot as plt +from PIL import Image + +import streamlit as st + +import seaborn as sns + +sys.path.append(f'{os.getcwd()}/utils') +from utils.eval_users import get_product_dev_page_layout + +# print(os.getcwd()) + +# Hide GPU from visible devices +tf.config.set_visible_devices([], 'GPU') +# Enable GPU memory growth - avoid allocating all memory at start +# gpus = tf.config.experimental.list_physical_devices(device_type='GPU') +# for gpu in gpus: +# tf.config.experimental.set_memory_growth(device=gpu, enable=True) + +from utils.control import show_tsne_vis,show_random_samples + +from utils.annoy_sampling import load_annoy_tree +# from utils import ( +# load_augmentations_config, +# get_arguments, +# get_placeholder_params, +# select_transformations, +# show_random_params, +# ) +# from visuals import ( +# select_image, +# show_credentials, +# show_docstring, +# get_transormations_params, +# ) + +from utils.model_utils import load_model +from utils.model_utils import get_feature_vector, get_feature_extractor_model, get_predictions_and_roi + +sns.set_style('darkgrid') +plt.rcParams['axes.grid'] = False + +st.set_page_config(layout="wide") + +#https://github.com/IliaLarchenko/albumentations-demo/blob/3cb6528a513fe3b35dbb2c2a63cdcfbb9bb2a932/src/utils.py#L149 + +GRAD_CAM_IMAGE_DIR = f'{os.getcwd()}/data/gradcam_vis_data/' +TEST_CSV_FILE = f'{os.getcwd()}/data/test_set_pred_prop.csv' + +annoy_tree_save_path =f'{os.getcwd()}/data/filtered_train_embedding/representative_samples_emb.annoy' +test_emb_path = f'{os.getcwd()}/data/filtered_train_embedding/test_embeddings.npy' +test_emb_id_path =f'{os.getcwd()}/data/filtered_train_embedding/test_ids.npy' + +train_emb_id_path =f'{os.getcwd()}/data/filtered_train_embedding/representative_train_ids.npy' + +repr_id_path =f'{os.getcwd()}/data/filtered_train_embedding/representative_train_ids.npy' +borderline_id_path =f'{os.getcwd()}/data/filtered_train_embedding/borderline_train_ids.npy' + +MODEL_PATH = f'{os.getcwd()}/model/keras_model_0422/' + +ROOT_FIG_DIR = f'{os.getcwd()}/figures/' + +test_emb = np.load(test_emb_path) +test_ids = np.load(test_emb_id_path) +test_id_list = list(test_ids) +test_labels = [_id.split("\\")[1] for _id in test_id_list] +print(" NUmber of test samples: ",len(test_labels)) +test_features = test_emb.reshape(-1,1792) + + +# train embedding list +train_ids = np.load(train_emb_id_path) +train_id_list = list(train_ids) +train_labels = [_id.split("\\")[1] for _id in train_id_list] +print(" NUmber of training samples: ",len(train_labels)) + +annoy_tree = load_annoy_tree(test_features.shape[1],annoy_tree_save_path) +def annoy_matching(annoy_f,query_item, query_index, n=10): + return annoy_f.get_nns_by_vector(query_item, n) + +def get_img(fn ,thumbnail=False): + img = Image.open(fn) + if thumbnail: + img.thumbnail((100,100)) + return img + + +### Within the streamlit.cache() decorator you’ll get better performance +# if you use allow_output_mutation=True because this means Streamlit just uses the same copy of the model in memory, +# rather than reloading when it’s re-run. + +# MODEL_WEIGHTS = f'{DEFAULT_MODEL_BASE_DIR}/hpe_epoch107_.hdf5' +# MODEL_JSON = f'{DEFAULT_MODEL_BASE_DIR}/hpe_hourglass_stacks_04_.json' + +# MODEL_WEIGHTS_DEPLOYMENT_URL = 'https://github.com/robertklee/COCO-Human-Pose/releases/download/v0.1-alpha/hpe_epoch107_.hdf5' +# MODEL_JSON_DEPLOYMENT_URL = 'https://github.com/robertklee/COCO-Human-Pose/releases/download/v0.1-alpha/hpe_hourglass_stacks_04_.json' + +# Constants for sidebar dropdown +SIDEBAR_OPTION_PROJECT_INFO = "Show Panel Info" +SIDEBAR_OPTION_GLOBAL_EXP = "Show Global Explanations" +SIDEBAR_OPTION_INSTANCE_EXP = "Select a Demo Image" +SIDEBAR_OPTION_INSTANCE_AUG= "Apply Augmentations" + + + +SIDEBAR_OPTIONS = [SIDEBAR_OPTION_PROJECT_INFO, SIDEBAR_OPTION_GLOBAL_EXP,SIDEBAR_OPTION_INSTANCE_EXP, SIDEBAR_OPTION_INSTANCE_AUG] + +def plot_n_similar(seed_id,similar_ids, test_path,n=10, scale=5): + f,ax = plt.subplots(1,n+1,figsize=((n+1)*scale,scale)) + # print(os.path.basename(test_labels[seed_id])[:-4]) + title = "SEED ID:{0}\nLabel:{1}".format(seed_id,os.path.basename(test_labels[seed_id])) + # print("path:", test_labels[seed_id].replace("F:/","E:/")) + ax[0].imshow(get_img(test_path.replace("F:/","E:/")),cmap='gray') + ax[0].set_title(title,fontsize=12) + for i in range(len(similar_ids)): + ax[i+1].imshow(get_img(similar_ids[i].replace("F:/","E:/")),cmap='gray') + title = "ID:{0}\nDistance: {1:.3f}\nLabel:{2}".format(i,0.1223,os.path.basename(similar_ids[i])[:-4]) + ax[i+1].set_title(title,fontsize=10) + f.suptitle("Images similar to seed_id {0}".format(seed_id),fontsize=18) + plt.subplots_adjust(top=0.5) + return f + +def load_image(filename,change_url=True): + # if change_url: + print(filename) + print(os.path.exists(filename)) + img = cv2.imread(filename) + return img + +@st.cache(allow_output_mutation=True) +def get_model(model_path): + new_model = tf.keras.models.load_model(model_path) + # keras_model = load_model(model_path) + return new_model + +@st.cache(allow_output_mutation=True) +def get_feature_vector_model(keras_model): + feature_mod = get_feature_extractor_model(keras_model) + return feature_mod + +def load_pd_data_frame(df_csv_path): + return pd.read_csv(df_csv_path) + +def get_path_list_from_df(df_data): + return list(df_data['path']) + +def get_class_probs_from_df(df_data): + return list(df_data['class_probs']) + + +def visualize_bar_plot(df_data): + fig = px.bar(df_data, x="probability", y="class", orientation='h') + return fig + +# def get_file_content_as_string(path): +# """[summary] + +# Args: +# path (string): path to instructions.md + +# Returns: +# url (url): url format +# """ + +# #url = 'https://raw.githubusercontent.com/streamlit/demo-self-driving/master/' + path +# path = os.path.abspath(path) +# url=pathlib.Path(path).as_uri() +# #url = path +# response = urllib.request.urlopen(url) +# return response.read().decode("utf-8") + +def run_instance_exp(img_path, img_path_list,prob_list,grad_vis_path_list): + + st.subheader('Instance Exploration') + # st.columns((1,1,1)) with row4_2: + LABELS = ['CNV', 'DRUSEN', 'DME', 'NORMAL'] + + left_column, middle_column, right_column = st.columns((1,1,1)) + display_image = load_image(img_path) + # fig = px.imshow(display_image) + # left_column.plotly_chart(fig, use_container_width=True) + left_column.image(cv2.resize(display_image, (180,180)),caption = "Selected Input") + + # get class probabilities + indx = img_path_list.index(img_path) + print(img_path) + prb_tmp = prob_list[indx] + print(f"{prb_tmp[1:-1]}") + clss_probs = literal_eval('"'+prb_tmp[1:-1]+'"') + print(clss_probs[1:-1].split(' ')) + prob_cls = [float(p) for p in clss_probs[1:-1].split(' ')] + tmp_df = pd.DataFrame.from_dict({'class':LABELS,'probability':prob_cls}) + print(tmp_df.head()) + fig = plt.figure(figsize=(15, 13)) + sns.barplot(x='probability', y='class', data=tmp_df) + middle_column.pyplot(fig) + # st.caption('Predictions') + + tmp_grad_img = GRAD_CAM_IMAGE_DIR + img_path.split("\\")[-2] +'/'+img_path.split("\\") [-1] + + display_image = load_image(tmp_grad_img,replace=False) + # left_column.plotly_chart(fig, use_container_width=True) + right_column.image(display_image,caption = "ROI") + + # seed_id = 900 + seed_id = test_id_list.index(img_path) + query_item = test_features[seed_id] + print(query_item.shape) + closest_idxs = annoy_matching(annoy_tree,query_item, seed_id, 10) + closest_fns = [train_ids[close_i] for close_i in closest_idxs] + st.subheader('Top-10 Similar Samples from Gallery Set') + st.pyplot(plot_n_similar(seed_id,closest_fns, img_path,n=10, scale=4)) + + # ax = sns.barplot(x='totalCount', y='name', data=df) + # plt_fig = visualize_bar_plot(tmp_df) + # middle_column.plotly_chart(plt_fig, use_container_width=True) + +def run_instance_exp_keras_model(img_path, new_model, feature_extractor_model): + + st.subheader('Instance Exploration') + # st.columns((1,1,1)) with row4_2: + LABELS = ['CNV', 'DRUSEN', 'DME', 'NORMAL'] + + left_column, middle_column, right_column = st.columns((1,1,1)) + print(img_path) + org_img_path = img_path + + img_path = f'{os.getcwd()}/data/oct2017/test/' + img_path.split("\\")[-2] +'/'+img_path.split("\\") [-1] + # img_path.replace("F:/XAI/data/OCT2017/","/home/hodor/dev/Learning/XAI/streamlit_demo/multipage-app/data/xai_framework_data/") + display_image = load_image(img_path) + # fig = px.imshow(display_image) + # left_column.plotly_chart(fig, use_container_width=True) + left_column.image(cv2.resize(display_image, (180,180)),caption = "Selected Input") + + + roi_img, probs = get_predictions_and_roi(img_path, new_model) + + ## probs + print(np.asarray(probs)) + print(probs.shape) + prob_cls =np.asarray(probs)[0] + print(prob_cls) + tmp_df = pd.DataFrame.from_dict({'class':LABELS,'probability':prob_cls}) + fig = plt.figure(figsize=(5, 4)) + sns.barplot(x='probability', y='class', data=tmp_df) + middle_column.pyplot(fig) + # middle_column.write("Probabilities") + + # grad img + right_column.image(roi_img, caption = "Decision ROI") + + # seed_id = 900 + seed_id = test_id_list.index(org_img_path) + query_item = get_feature_vector(img_path,feature_extractor_model) + query_item = query_item.reshape(-1,1792) + print(query_item.shape) + closest_idxs = annoy_matching(annoy_tree,query_item[0,:], seed_id, 10) + closest_fns = [train_ids[close_i] for close_i in closest_idxs] + + closest_fns_tmp = [f'{os.getcwd()}/data/oct2017/train/' + each_fn.split("\\")[-2] +'/'+each_fn.split("\\") [-1] + for each_fn in closest_fns] + print(closest_fns) + st.subheader('Top-10 Similar Samples from Gallery Set') + st.pyplot(plot_n_similar(seed_id,closest_fns_tmp, img_path,n=10,scale=3)) + + + +def run_augmentations(transforms,image): + data = None + try: + # apply the transformation to the image + data = A.Compose(transforms)(image=image) + error = 0 + except ValueError: + error = 1 + + return data, error + +@st.cache +def get_sample(): + print("running!") + +def main(): + + st.sidebar.warning('\ + Please upload SINGLE single...') + st.sidebar.write(" ------ ") + st.sidebar.title("Explore the Instance Predixtions and Explanations") + new_model = get_model(MODEL_PATH) + feature_extractor_model = get_feature_vector_model(new_model) + + app_mode = st.sidebar.selectbox("Please select from the following", SIDEBAR_OPTIONS) + + if app_mode == SIDEBAR_OPTION_PROJECT_INFO: + st.sidebar.write(" ------ ") + st.sidebar.success("Project information showing on the right!") + # st.write(get_file_content_as_string("README.md")) + + elif app_mode == SIDEBAR_OPTION_INSTANCE_EXP: + st.sidebar.write(" ------ ") + + DF_TEST_PROP = load_pd_data_frame(TEST_CSV_FILE) + IMG_PATH_LISTS = get_path_list_from_df(DF_TEST_PROP) + IMG_CLSS_PROBS_LIST = get_class_probs_from_df(DF_TEST_PROP) + grad_vis_path_list = None + + # new_model = load_model(MODEL_PATH) + option = st.sidebar.selectbox('Please select a sample image, then click Explain Me button', IMG_PATH_LISTS) + pressed = st.sidebar.button('Explain ME') + if pressed: + st.empty() + st.sidebar.write('Please wait for the magic to happen! This may take up to a minute.') + run_instance_exp_keras_model(option, new_model,feature_extractor_model) + # run_instance_exp(option, IMG_PATH_LISTS,IMG_CLSS_PROBS_LIST,grad_vis_path_list) + + elif app_mode == SIDEBAR_OPTION_GLOBAL_EXP: + borderline_cases = np.load(borderline_id_path) + representative_cases = np.load(repr_id_path) + borderline_id_list = list(borderline_cases) + # print(borderline_id_list) + + borderline_id_list = [f'{os.getcwd()}/data/oct2017/train/' + each_fn.split("\\")[-2] +'/'+each_fn.split("\\") [-1] + for each_fn in borderline_id_list] + representative_id_list = list(representative_cases) + representative_id_list = [f'{os.getcwd()}/data/oct2017/train/' + each_fn.split("\\")[-2] +'/'+each_fn.split("\\") [-1] + for each_fn in representative_id_list] + #upload = st.empty() + #with upload: + st.sidebar.info('GLOABAL EXPLANATION!! ') + option = st.sidebar.selectbox('Please select Global Samples, then click Explain Me button', ["Representative Samples","Borderline Cases"]) + if option: + clss = st.sidebar.selectbox('Select A Class', ["CNV","DME", "NORMAL", "DRUSEN"]) + side_1, side_2 = st.sidebar.columns(2) + # cls1, cls2, cls3, cls4 = st.sidebar.columns(4) + # st.sidebar.title("Choose A Class:") + # with cls1: + # st.radio('', ["CNV"]) + # with cls2: + # st.radio('', ["DRUSEN"]) + # with cls3: + # st.radio('', ["DME"]) + # with cls4: + # st.radio('', ["NORMAL"]) + with side_1: + check_emb = st.checkbox('Embdedding Space Visuzalization') + + with side_2: + check_samp = st.checkbox('Random Sample Visuzalization') + + if check_emb and check_samp: + st.write("Emb and vis") + if option.startswith("Rep"): + filter_lst = list(filter(lambda k: clss in k, representative_id_list)) + show_random_samples(filter_lst,clss) + show_tsne_vis("./figures/tsne_representative.png", title="Representative") + else: + filter_lst = list(filter(lambda k: clss in k, borderline_id_list)) + show_random_samples(filter_lst,clss) + show_tsne_vis(f"{ROOT_FIG_DIR}/tsne_borderline.png", title="Borderline") + elif check_emb: + st.write("embedding vis") + if option.startswith("Rep"): + show_tsne_vis(f"{ROOT_FIG_DIR}/tsne_representative.png", title="Representative") + else: + show_tsne_vis(f"{ROOT_FIG_DIR}/tsne_borderline.png", title="Borderline") + elif check_samp: + st.write("rand vis") + if option.startswith("Rep"): + filter_lst = list(filter(lambda k: clss in k, representative_id_list)) + show_random_samples(filter_lst,clss) + else: + filter_lst = list(filter(lambda k: clss in k, borderline_id_list)) + show_random_samples(filter_lst,clss) + + + # f = st.sidebar.file_uploader("Please Select to Upload an Image", type=['png', 'jpg', 'jpeg', 'tiff', 'gif']) + # if f is not None: + # tfile = tempfile.NamedTemporaryFile(delete=True) + # tfile.write(f.read()) + # st.sidebar.write('Please wait for the magic to happen! This may take up to a minute.') + # run_app(tfile) + # elif app_mode == SIDEBAR_OPTION_INSTANCE_AUG: + # DF_TEST_PROP = load_pd_data_frame(TEST_CSV_FILE) + # IMG_PATH_LISTS = get_path_list_from_df(DF_TEST_PROP) + # get_sample() + # option = st.sidebar.selectbox('Please select a sample image, then choose augmentations', IMG_PATH_LISTS) + + # side_1, side_2 = st.columns(2) + # with side_2: + # st.title("Testing!!") + # with side_1: + # st.title("Demo of Albumentations") + # image_box = st.empty() + # image =load_image(option.replace("F:/","E:/")) + + # # proceed only if everything is ok + + # image_box.image(image, caption="Original image") + # # image was loaded successfully + # placeholder_params = get_placeholder_params(image) + + # # load the config + # augmentations = load_augmentations_config( + # placeholder_params, "configs/augs.json" + # ) + + # # get the list of transformations names + # transform_names = select_transformations(augmentations, interface_type="Simple") + + # # get parameters for each transform + # transforms = get_transormations_params(transform_names, augmentations) + + # data, error = run_augmentations(transforms, image) + + # if error == 0: + # augmented_image = data["image"] + # # show title + # # st.title("Demo of Albumentations") + # # show the images + # width_transformed = int( + # image.shape[1] / image.shape[1] * augmented_image.shape[1] + # ) + # image_box.image( + # augmented_image, + # caption="Transformed image", + # ) + # # side_1.image(augmented_image, caption="Original image") + + else: + raise ValueError('Selected sidebar option is not implemented. Please open an issue on Github: https://github.com/kaplansinan/MLOps') + + # st.plotly_chart(fig) + +# new_model = load_model(MODEL_PATH) +main() +expander_faq = st.expander("More About Our Project") +expander_faq.write("Hi there! If you have any questions about our project, or simply want to check out the source code, please visit our github repo: https://github.com/kaplansinan/MLOPS") diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..8edf4ba68883a6ad24e00c5359c7bb7383cba876 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,17 @@ +tf-explain==0.3.0 +tensorflow==2.5.1 +streamlit==1.17.0 +seaborn==0.11.1 +scipy +plotly==5.11.0 +Pillow==9.0.0 +pandas==1.2.1 +opencv-python==4.5.5.64 +numpy==1.19.* +matplotlib==3.3.3 +albumentations==0.5.2 +annoy==1.17.1 +easydict==1.9 +tqdm==4.54.1 +scikit-image +scikit-learn \ No newline at end of file diff --git a/utils/__pycache__/annoy_sampling.cpython-38.pyc b/utils/__pycache__/annoy_sampling.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..55f7dc23bc816b89de809eae7bd66465a0d4aae2 Binary files /dev/null and b/utils/__pycache__/annoy_sampling.cpython-38.pyc differ diff --git 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b/utils/annoy_sampling.py new file mode 100644 index 0000000000000000000000000000000000000000..71375e300b349b0ff4e59931567f55d17e220674 --- /dev/null +++ b/utils/annoy_sampling.py @@ -0,0 +1,278 @@ +import numpy as np +import pandas as pd +import os +import glob +import random +import matplotlib.pyplot as plt +import cv2 +import plotly.express as px +from annoy import AnnoyIndex +from PIL import Image +from tqdm import tqdm + +# https://github.com/erikbern/ann-presentation/blob/master/cifar.py +# https://www.slideshare.net/erikbern/approximate-nearest-neighbor-methods-and-vector-models-nyc-ml-meetup + +# https://erikbern.com/2015/10/01/nearest-neighbors-and-vector-models-part-2-how-to-search-in-high-dimensional-spaces.html +# t-SNE space + +def get_top_n_dissimilar_samples_path(embeddings,embeddings_id_list,test_size_ratio =0.1, annoy_path=None): + if annoy_path is None: + build_annoy_tree(embeddings, embeddings_id_list,annoy_path, n_trees=50) + + annoy_tree = load_annoy_tree(embeddings.shape[1],annoy_path) + dist_map = create_distance_map(annoy_tree,embeddings_id_list) + sorted_isolation_values, sorted_indices = get_isolated_elements_from_distance_map(dist_map,embeddings_id_list) + _, test_paths = splitListByIsolationValues(embeddings_id_list, sorted_indices, test_size_ratio) + return test_paths + +def build_annoy_tree(embeddings, embeddings_id_list,save_filename, n_trees=50): + tree = AnnoyIndex(embeddings.shape[1], 'euclidean') + ntree = n_trees + # add all items + for path, vector in zip(list(range(len(embeddings_id_list))),embeddings): + tree.add_item(path, vector) + + # build tree + tree.build(ntree) + tree.save(save_filename) + +def load_annoy_tree(embeddings_dim,annoy_fn): + a = AnnoyIndex(embeddings_dim, 'euclidean') + a.load(annoy_fn) + return a + +def create_distance_map(annoy_tree,embeddings_id_list): + # generate distance map + distance_map = np.zeros((len(embeddings_id_list),len(embeddings_id_list)),np.float32) + for i in tqdm(range(len(embeddings_id_list))): + for j in range(len(embeddings_id_list)): + distance_map[i,j] = annoy_tree.get_distance(i,j) + return distance_map + +def get_isolated_elements_from_distance_map(distance_map,embeddings_id_list): + # Now, sample n percent of the ones with maximum distances to closest neighbors. Isolated ones. + test_samples = np.where(distance_map == 0, 500, distance_map) + + isolation_values = np.min(test_samples,1) + # get results in descending order + sorted_isolation_values, sorted_indices = zip(*sorted(zip(isolation_values, list(range(len(embeddings_id_list)))),reverse=True)) + + return sorted_isolation_values, sorted_indices + +def splitListByIsolationValues(lst, sorted_indices, test_part=0.1): + + # TEST_SIZE = 0.05 # Percentage of test data from all + # train_paths, test_paths = splitListByIsolationValues(train_id_list, sorted_indices, TEST_SIZE) + # print(len(train_paths)) + # print(len(test_paths)) + n_test = int(len(lst)*test_part) + indices_test = sorted_indices[:n_test] + indices_train = sorted_indices[n_test:] + lst_train = [lst[ind] for ind in indices_train] + lst_test = [lst[ind] for ind in indices_test] + return lst_train, lst_test + + +# tree = AnnoyIndex(train_tsne_2d.shape[1], 'euclidean') +# ntree = 50 + +# # add all items +# for path, vector in zip(list(range(len(train_id_list))),train_tsne_2d): +# tree.add_item(path, vector) + +# # build tree +# _ = tree.build(ntree) + + +# # generate distance map +# distance_map = np.zeros((len(train_id_list),len(train_id_list)),np.float32) +# for i in tqdm(range(len(train_id_list))): +# for j in range(len(train_id_list)): +# distance_map[i,j] = tree.get_distance(i,j) + + +# # Now, sample n percent of the ones with maximum distances to closest neighbors. Isolated ones. +# test_samples = np.where(distance_map == 0, 500, distance_map) + +# isolation_values = np.min(test_samples,1) +# # get results in descending order +# sorted_isolation_values, sorted_indices = zip(*sorted(zip(isolation_values, list(range(len(train_id_list)))),reverse=True)) + +# print(sorted_isolation_values[:5],sorted_indices[:5]) + +# #Plot some of the images and compare them to rest of the set to see if there are any similar samples. +# for isolated_id in sorted_indices[:10]: +# plot_n_similar(isolated_id,4) +# plt.show() + +# TEST_SIZE = 0.05 # Percentage of test data from all +# train_paths, test_paths = splitListByIsolationValues(train_id_list, sorted_indices, TEST_SIZE) +# print(len(train_paths)) +# print(len(test_paths)) + + + +# def build(fn, f, fun): # lol @ parameters :) +# a = annoy.AnnoyIndex(f, 'euclidean') +# i = 0 +# for pixels, label in read_cifar(): +# a.add_item(i, fun(pixels)) +# i += 1 +# if i % 1000 == 0: +# print i, '...' + +# a.build(100) +# a.save(fn) + +# def build_annoy_tree(): +# annoy_fn = 'mnist.annoy' +# data_fn = 'mnist.pkl.gz' + +# if not os.path.exists(annoy_fn): +# if not os.path.exists(data_fn): +# print 'downloading' +# urlretrieve('http://deeplearning.net/data/mnist/mnist.pkl.gz', data_fn) + +# a = annoy.AnnoyIndex(784, 'euclidean') +# for i, pic in util.get_vectors(data_fn): +# a.add_item(i, pic) + +# print 'building' +# a.build(10) +# a.save(annoy_fn) + +def scatter_thumbnails_train_test(data, image_paths, train_labels, test_paths, zoom=0.3, + colors=None, xlabel='PCA dimension 1', + ylabel='PCA dimension 2'): + # assert len(data) == len(image_paths) + + # reduce embedding dimensions to 2 + # x = PCA(n_components=2).fit_transform(data) #if len(data[0]) > 2 else data + x = data + tmp_colors = ['y', 'g', 'b', 'c'] + f = plt.figure(figsize=(22, 15)) + ax = plt.subplot(aspect='equal') + np_label = np.array(train_labels) + cls_categories = ['CNV', 'DRUSEN', 'DME', 'NORMAL'] + for cls,clr in zip(cls_categories,tmp_colors): + indices = np_label==cls + ax.scatter(data[indices,0],data[indices,1], c=clr, label = cls ,alpha=0.5, s=4) + _ = ax.axis('tight') + ax.set_xlabel(xlabel, fontsize=14) + ax.set_ylabel(ylabel, fontsize=14) + ax.legend(fontsize='large', markerscale=2) + + # create a scatter plot. + # f = plt.figure(figsize=(22, 15)) + # ax = plt.subplot(aspect='equal') + # sc = ax.scatter(x[:,0], x[:,1], s=4) + # #_ = ax.axis('off') + # _ = ax.axis('tight') + # ax.set_xlabel(xlabel, fontsize=14) + # ax.set_ylabel(ylabel, fontsize=14) + + # add thumbnails :) + from matplotlib.offsetbox import OffsetImage, AnnotationBbox + for i in range(len(image_paths)): + isTest = image_paths[i] in test_paths + if isTest: + image = get_img(image_paths[i].replace("F:/","E:/"), thumbnail=True) + if not (len(image.shape))==1: + im = OffsetImage(image, cmap='gray',zoom=zoom if isTest else zoom-0.2) + bboxprops = dict(edgecolor= 'red' if isTest else 'gray') + ab = AnnotationBbox(im, x[i], xycoords='data', + frameon=(bboxprops is not None), + pad=0.0, + bboxprops=bboxprops) + ax.add_artist(ab) + return ax + +# _ = scatter_thumbnails_train_test(train_tsne_2d, train_id_list, test_paths, +# zoom=0.2, xlabel="Dimension 1", ylabel="Dimension 2") +# plt.title('2D t-SNE Visualization of Sampled Data (OCT2017 Train) - RGB=Picked') +# plt.show() + +def splitListByIsolationValues(lst, sorted_indices, test_part=0.1): + n_test = int(len(lst)*test_part) + indices_test = sorted_indices[:n_test] + indices_train = sorted_indices[n_test:] + lst_train = [lst[ind] for ind in indices_train] + lst_test = [lst[ind] for ind in indices_test] + return lst_train, lst_test + +def plot_random_samples(paths, n=5): + f, ax = plt.subplots(1,5,figsize=(20,5)) + for i in range(n): + rand_index = random.randint(0,len(paths)-1) + ax[i].imshow(plt.imread(paths[rand_index])) + +def get_img(fn ,thumbnail=False): + img = Image.open(fn) + if thumbnail: + img.thumbnail((100,100)) + #print(img.size) + return np.array(img)[:,:] + + +def plot_n_similar(annoy_tree,train_id_list,train_labels,seed_id,n, scale=5): + ids, dists = annoy_tree.get_nns_by_item(seed_id, n+1, search_k=-1, include_distances=True) + f,ax = plt.subplots(1,n+1,figsize=((n+1)*scale,scale)) + for i,_id in enumerate(ids): + img_id = _id if i != 0 else seed_id + ax[i].imshow(get_img(train_id_list[img_id]),cmap='gray') + title = "ID:{0}\nDistance: {1:.3f}\nLabel:{2}".format(img_id,dists[i],train_labels[img_id]) if i != 0 else "SEED ID:{0}\nLabel:{1}".format(img_id,train_labels[img_id]) + ax[i].set_title(title,fontsize=12) + f.suptitle("Images similar to seed_id {0}".format(seed_id),fontsize=18) + plt.subplots_adjust(top=0.97) + +# plot_n_similar(5) + +# def match_gallery_2_query(save_dir): +# gallery_emb = np.load(os.path.join(save_dir, 'gallery_embedding.npy')) +# query_emb = np.load(os.path.join(save_dir, 'query_embedding.npy')) +# gallery_ids = np.load(os.path.join(save_dir, 'gallery_ids.npy')) +# query_ids = np.load(os.path.join(save_dir, 'query_ids.npy')) + +# query_results = [] +# get_closest = None + +# if matching_method == 'annoy': +# annoy_metric = 'hamming' if gallery_emb.dtype == np.bool else 'angular' +# annoy_f = AnnoyIndex(gallery_emb.shape[1], annoy_metric) +# for i in range(gallery_emb.shape[0]): +# annoy_f.add_item(i, gallery_emb[i]) + +# annoy_f.build(10) # number of trees + +# def annoy_matching(query_item, query_index, n=10): +# return annoy_f.get_nns_by_vector(query_item, n) + +# get_closest = annoy_matching + +# elif matching_method == 'knn': +# #distances = distance.cdist(query_emb, gallery_emb, 'cosine') +# #sorted_dist = np.argsort(distances, axis=1) + +# def knn_matching(query_item, query_index, n=10): +# distances = distance.cdist((query_emb[query_index]).reshape(1,-1), gallery_emb, 'cosine') +# sorted_dist = np.argsort(distances, axis=1) +# return sorted_dist[0,:n] + +# get_closest = knn_matching + +# else: +# raise Exception(f'{FLAGS.matching_method} not implemented in matching') +# for i, query_item in tqdm(enumerate(query_emb),'Finding matches...'): +# closest_idxs = get_closest(query_item, i, 10) +# closest_fns = [gallery_ids[close_i] for close_i in closest_idxs] +# beginning = f'{query_ids[i]},' + '{' +# line = ','.join(closest_fns) +# end = '}' +# query_results.append(beginning + line + end) + +# sub_fn = os.path.join(save_dir, 'submission.csv') +# with open(sub_fn, 'w') as f: +# f.writelines("%s\n" % l for l in query_results) + +# plot_submission(sub_fn, FLAGS.testdata_dir, save_dir) \ No newline at end of file diff --git a/utils/augmentation_ex.py b/utils/augmentation_ex.py new file mode 100644 index 0000000000000000000000000000000000000000..b04c4ed87b25fc958a142dd3e73eff1c59d45abd --- /dev/null +++ b/utils/augmentation_ex.py @@ -0,0 +1,90 @@ + +import streamlit as st +from matplotlib import pyplot as plt +import albumentations as A +import cv2 + +# https://github.com/phrasenmaeher/image_augmentations_visualization/blob/master/Start.py +def create_pipeline(transformations: list): + pipeline = [] + for index, transformation in enumerate(transformations): + if transformation: + pipeline.append(index_to_transformation(index)) + + return pipeline + + +def load_image(filename): + img = cv2.imread(filename) + return cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + +def index_to_transformation(index: int): + if index == 0: + return A.GaussNoise(p=1.0, var_limit=(0.25, 0.5)) + elif index == 1: + return A.HorizontalFlip(p=1.0) + elif index == 2: + return A.VerticalFlip(p=1.0) + elif index == 3: + return A.RandomBrightness(p=1.0, limit=(0.5, 1.5)) + # elif index == 4: + # return A.AdvancedBlur(p=1.0, blur_limit=3) + elif index == 4: + return A.ChannelShuffle(p=1.0) + elif index == 5: + return A.ChannelDropout(p=1.0) + elif index == 6: + return A.RandomContrast(p=1.0, limit=(0.5, 1.5)) + +placeholder = st.empty() +placeholder2 = st.empty() +placeholder.markdown( + "# Visualize an image augmentation pipeline\n" + "### Select the components of the pipeline in the sidebar.\n" + "Once you have chosen the augmentation techniques, select or upload an image.\n" + "Then click 'Apply' to start!\n" + ) +placeholder2.markdown( + "After clicking start, the individual steps of the pipeline are visualized. The ouput of the previous step is the input to the next step." + ) + # placeholder.write("Create your audio pipeline by selecting augmentations in the sidebar.") +st.markdown("Choose the transformations here:") +gaussian_noise = st.sidebar.checkbox("GaussianNoise") + +horizontal_flip = st.sidebar.checkbox("HorizontalFlip") +vertical_flip = st.sidebar.checkbox("VerticalFlip") +random_brightness = st.sidebar.checkbox("RandomBrightness") +# advanced_blur = st.sidebar.checkbox("AdvancedBlur") +channel_shuffle = st.sidebar.checkbox("ChannelShuffle") +channel_dropout = st.sidebar.checkbox("ChannelDropout") +random_contrast = st.sidebar.checkbox("RandomContrast") + +st.markdown("---") +st.markdown("(Optional) Upload an image file here:") +file_uploader = st.sidebar.file_uploader(label="", type=[".png", ".jpg", ".jpeg"]) +st.markdown("Or select a sample file here:") + + +st.markdown("---") +transformations = [ + gaussian_noise, + horizontal_flip, + vertical_flip, + random_brightness, + # advanced_blur, + channel_shuffle, + channel_dropout, + random_contrast, + +] + +pipeline = A.Compose(create_pipeline(transformations)) +print(pipeline) +tmp_img = load_image("/home/hodor/dev/Learning/XAI/streamlit_demo/multipage-app/figures/cnv.png") +# apply the transformation to the image +data = pipeline(image=tmp_img)["image"] +# modified_image = individual_transformation(image=tmp_img)["image"] +st.image(data) + + + diff --git a/utils/control.py b/utils/control.py new file mode 100644 index 0000000000000000000000000000000000000000..df5814d014166f74790a4e2a910ac8fc0cfbef44 --- /dev/null +++ b/utils/control.py @@ -0,0 +1,122 @@ +import streamlit as st +import random +import matplotlib.pyplot as plt +import numpy as np +import cv2 + +# TODO: rename and refactor everything + +def show_tsne_vis(img_path,title="Representative"): + st.write(f'T-SNE Visualization of {title} Cases') + st.image(cv2.imread(img_path),caption = "T-SNE Figure") + +def show_random_samples(image_list,class_name,n=10): + rnd_list = random.sample(image_list,n) + st.pyplot(visualize_samples(rnd_list,class_name)) + + +def visualize_samples(sample_list,class_name,columns=5): + f = plt.figure(figsize=(20,10)) + columns = 5 + plt.suptitle("Class {0}".format(class_name)) + for i, image in enumerate(sample_list): + plt.subplot(len(sample_list) / columns + 1, columns, i + 1) + plt.imshow(cv2.imread(image.replace("F:/","E:/"))) + return f + +def select_num_interval( + param_name: str, limits_list: list, defaults, n_for_hash, **kwargs +): + st.sidebar.subheader(param_name) + min_max_interval = st.sidebar.slider( + "", + limits_list[0], + limits_list[1], + defaults, + key=hash(param_name + str(n_for_hash)), + ) + return min_max_interval + + +def select_several_nums( + param_name, subparam_names, limits_list, defaults_list, n_for_hash, **kwargs +): + st.sidebar.subheader(param_name) + result = [] + assert len(limits_list) == len(defaults_list) + assert len(subparam_names) == len(defaults_list) + + for name, limits, defaults in zip(subparam_names, limits_list, defaults_list): + result.append( + st.sidebar.slider( + name, + limits[0], + limits[1], + defaults, + key=hash(param_name + name + str(n_for_hash)), + ) + ) + return tuple(result) + + +def select_min_max( + param_name, limits_list, defaults_list, n_for_hash, min_diff=0, **kwargs +): + assert len(param_name) == 2 + result = list( + select_num_interval( + " & ".join(param_name), limits_list, defaults_list, n_for_hash + ) + ) + if result[1] - result[0] < min_diff: + diff = min_diff - result[1] + result[0] + if result[1] + diff <= limits_list[1]: + result[1] = result[1] + diff + elif result[0] - diff >= limits_list[0]: + result[0] = result[0] - diff + else: + result = limits_list + return tuple(result) + + +def select_RGB(param_name, n_for_hash, **kwargs): + result = select_several_nums( + param_name, + subparam_names=["Red", "Green", "Blue"], + limits_list=[[0, 255], [0, 255], [0, 255]], + defaults_list=[0, 0, 0], + n_for_hash=n_for_hash, + ) + return tuple(result) + + +def replace_none(string): + if string == "None": + return None + else: + return string + + +def select_radio(param_name, options_list, n_for_hash, **kwargs): + st.sidebar.subheader(param_name) + result = st.sidebar.radio("", options_list, key=hash(param_name + str(n_for_hash))) + return replace_none(result) + + +def select_checkbox(param_name, defaults, n_for_hash, **kwargs): + st.sidebar.subheader(param_name) + result = st.sidebar.checkbox( + "True", defaults, key=hash(param_name + str(n_for_hash)) + ) + return result + + +# dict from param name to function showing this param +param2func = { + "num_interval": select_num_interval, + "several_nums": select_several_nums, + "radio": select_radio, + "rgb": select_RGB, + "checkbox": select_checkbox, + "min_max": select_min_max, +} \ No newline at end of file diff --git a/utils/data_users.py b/utils/data_users.py new file mode 100644 index 0000000000000000000000000000000000000000..d857f7b521459041e08a1240b019f3c49c6bd584 --- /dev/null +++ b/utils/data_users.py @@ -0,0 +1,140 @@ +import streamlit as st +import os + +ROOT_FIG_DIR = f'{os.getcwd()}/figures/' +def get_product_dev_page_layout(): + # st.title("Data Details") + + # st.write( + # """ + # ## + # Examining data is the key factor here and it provides deta-centric approach to test any idea. + # """) + + # list_test = """
    + #
  • Target Group: Developer, Model Owners, Product Managers
    • + #
""" + # st.markdown(list_test, unsafe_allow_html=True) + # LAYING OUT THE MIDDLE SECTION OF THE APP WITH THE MAPS + # row4_1, row4_2, row4_3 = st.columns((1,1,1)) + + row4_1, row4_2, row4_3 = st.tabs(["Data Source Information", "Exploratory Data Stats", "Data Onboarding"]) + + with row4_1: + # st.write("**Data Source Info**") + st.subheader('Data Source Information') + # new_title = '

Data set infor:

' + # st.markdown(new_title, unsafe_allow_html=True) + # where the data comes and how it is collected, what the data includes, what are the details of the data, how the data is used. + # answers four different questions + st.write(" Data set consists of OCT images from CNV, DME, DRUSEN and NORMAL cases...(from 4686 adult patients) a") + + st.caption('Source') + st.write("[Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification](https://data.mendeley.com/datasets/rscbjbr9sj/3)") + + with st.expander('Data Collection Details(Click for more info)'): + st.write("""As stated OCT images are clloected from the Shiley Eye Institute of the University of California San Diego, + the California Retinal Research Foundation, + Medical Center Ophthalmology Associates, the Shanghai First People’s Hospital, and Beijing Tongren Eye Center between + July 1, 2013 and March 1, 2017.""") + st.caption('Case Samples') + # https://www.aao.org/eye-health/ask-ophthalmologist-q/choroidal-neovascularization-definition-treatment + st.image('./figures/oct_details.png') + + list_test = """
    Case explanations: +
  • CNV: t) choroidal neovascularization (CNV) with neovascular membrane (white arrowheads) and associated subretinal fluid (arrows
    • +
  • DRUSEN: Multiple drusen (arrowheads) present in early AMD
    • +
  • DME: ) Diabetic + macular edema (DME) with retinal-thickening-associated intraretinal fluid
    • +
  • Normal
    • +
""" + st.markdown(list_test, unsafe_allow_html=True) + + + st.caption('License:') + with st.expander('License: CC BY 4.0 license'): + st.write(""" + The files associated with this dataset are licensed under a Creative Commons Attribution 4.0 International license. What does this mean? + You can share, copy and modify this dataset so long as you give appropriate credit, + provide a link to the CC BY license, and indicate if changes were made, but you may not do + so in a way that suggests the rights holder has endorsed you or your use of the dataset. + Note that further permission may be required for any content within the dataset + that is identified as belonging to a third party. More details about the licences can be found + [here](https://creativecommons.org/about/cclicenses/). + """) + + # st.write("Open to be used for researh.") + + with row4_2: + st.subheader('Exploratory Data Stats') + with st.expander('Training Data Info'): + col1, col2 = st.columns((1,1)) + st.write("**Raw Data Details**") + with col1: + st.caption('Number of Images per Category') + st.image(f'{ROOT_FIG_DIR}/train_raw_category_dist.png') + with col2: + st.caption('Aspect ratio') + st.image(f'{ROOT_FIG_DIR}/train_raw_aspectratio_dist.png') + st.caption('Image Samples') + st.image(f'{ROOT_FIG_DIR}/train_samples.png') + + with st.expander('Test Data Info'): + st.write("**Raw Data Details**") + st.caption('Number of Images per Category') + st.image(f'{ROOT_FIG_DIR}/test_category_dist.png') + st.caption('Aspect ratio') + st.image(f'{ROOT_FIG_DIR}/test_aspectratio_dist.png') + st.caption('Image Samples') + st.image(f'{ROOT_FIG_DIR}/train_samples.png') + + with row4_3: + # st.write("**Post Processesd Data Details**") + st.subheader('Pre-Processing Details') + st.write( + """ + ## + Since the training set has a problem of class imbalanced, we need to solve this issue. To do so, representative sampling strategy is used with hierarchical clustering. + """) + # st.caption('') + new_title = '
Post Processing Steps:
' + st.markdown(new_title, unsafe_allow_html=True) + code = '''def representative_sampling(): + for each_class in category_list: + embeddings = get_resnet50_embeddings(each_class) + n_cluster = run_hierarchical_clustering(embeddings) + samples = get_representative_n_samples_within_each_cluster(n_cluster)''' + st.code(code, language='python') + + with st.expander('Training Data Info after Representative Sampling'): + # new_title = '
Training Set Info after Representative Sampling:
' + # st.markdown(new_title, unsafe_allow_html=True) + col1, col2 = st.columns((1,1)) + with col1: + st.caption('Number of Images per Category') + st.image(f'{ROOT_FIG_DIR}/train_filter_category_dist.png') + with col2: + st.caption('Aspect ratio') + st.image(f'{ROOT_FIG_DIR}/train_filter_aspectratio_dist.png') + st.caption('Image Samples') + st.image(f'{ROOT_FIG_DIR}/train_samples.png') + st.write("Model Input Size Resizing: 180x180x3") + # st.caption('Post Processing Steps:') + # code = '''def representative_sampling(): + # ... for class_i in category_list: + # ... print("Hello, Streamlit!")''' + # st.code(code, language='python') + # st.code('for class_i in category_list: hiearhical_cluster(class_i)') + + +def get_developer_page_layout(): + st.header("Developer") + + markdown = """ + 1. For the [GitHub repository](https://github.com/giswqs/streamlit-multipage-template) or [use it as a template](https://github.com/giswqs/streamlit-multipage-template/generate) for your own project. + 2. Customize the sidebar by changing the sidebar text and logo in each Python files. + 3. Find your favorite emoji from https://emojipedia.org. + """ + + st.markdown(markdown) + diff --git a/utils/datagenerator.py b/utils/datagenerator.py new file mode 100644 index 0000000000000000000000000000000000000000..d5800cb2d99ee43aa3c89dd24950cfc5ff528b59 --- /dev/null +++ b/utils/datagenerator.py @@ -0,0 +1,305 @@ +import numpy as np +import os,sys +import PIL +import PIL.Image +import tensorflow as tf +#import tensorflow_datasets as tfds +import random + +from functools import partial +import matplotlib.pyplot as plt +import math +import pathlib +import cv2 +from albumentations import ( + Compose, augmentations,RandomBrightness, JpegCompression, HueSaturationValue, RandomContrast, HorizontalFlip, + Rotate +) + +sys.path.append('./') +from utils.tf_image_preprocessing import get_preprocess_func +from utils.train_config import config + +print(tf.__version__) +# assert tf.__version__>'2.2.1', "tensorflow version mismatch" + +#tf autotune for performance utilization +AUTOTUNE = tf.data.experimental.AUTOTUNE + +CLASS_NAMES = config.DATA.class_names + +def get_class_names(root_path): + # the structure of the data set can be used to get class names + data_dir = pathlib.Path(root_path) + class_names = np.array(sorted([item.name for item in data_dir.glob('*') if item.name != "LICENSE.txt"])) + return class_names + +# model specific preprocessing function +def get_model_agnostic_preprocess_func(config): + """ Get preprocessing function based on model type (resnet, efficientnet etc) + + Args: + model_type ([type]): [description] + + Returns: + [type]: [description] + """ + #https://www.tensorflow.org/api_docs/python/tf/keras/applications/resnet/preprocess_input + # https://www.tensorflow.org/api_docs/python/tf/keras/applications/efficientnet/preprocess_input + # return tf.keras.applications.resnet.preprocess_input + return get_preprocess_func(config) + # return tf.keras.applications.efficientnet.preprocess_input + +def set_shapes(img, label, img_shape=(180,180,3),num_classes=5): + img.set_shape(img_shape) + # label.set_shape([]) #int + label.set_shape([num_classes]) #one hot encoded + return img, label + + +# split the data into validation and training set +def split_ds_train_val(list_ds,image_count): + val_size = int(image_count * 0.0) + train_ds = list_ds.skip(val_size) + val_ds = list_ds.take(val_size) + # # Let's now split our dataset in train and validation. + # train_dataset = dataset.take(round(image_count * 0.8)) + # val_dataset = dataset.skip(round(image_count * 0.8)) + # get length of each data set if necessary + print(tf.data.experimental.cardinality(train_ds).numpy()) + print(tf.data.experimental.cardinality(val_ds).numpy()) + return train_ds, val_ds + +def get_label_int(file_path): + # convert the path to a list of path components + parts = tf.strings.split(file_path, os.path.sep) + # The second to last is the class-directory + one_hot = parts[-2] == CLASS_NAMES + # Integer encode the label + return tf.argmax(one_hot) + +def get_label_one_hot_coded(file_path): + # part_list = tf.strings.split(path, "/") + # part_list = tf.strings.split(path, os.path.sep) + part_list = tf.strings.split(file_path, os.path.sep) + # list_classes = list(config.DATA.label_encoder.keys()) + label = part_list[-2]==CLASS_NAMES + lbl = tf.where(label)[0] + #lbl = label_tmp_dict[part_list[-2].numpy()] + label_tf = tf.one_hot(lbl, depth=len(CLASS_NAMES))[0] + return label_tf + +# tf2 decode image +def decode_img(img,img_size): + # convert the compressed string to a 3D uint8 tensor + img = tf.image.decode_jpeg(img, channels=3) + # resize the image to the desired size + return tf.image.resize(img, [img_size[0],img_size[1]]) + +preprocessing_func = get_preprocess_func(config) + +def process_path(file_path, img_size): + label = get_label_one_hot_coded(file_path) + # load the raw data from the file as a string + img = tf.io.read_file(file_path) + img = decode_img(img,img_size) + # normalize + pp_img = tf.numpy_function(func=preprocessing_func, inp=[img], Tout=tf.float32) + return pp_img, label + +def get_albu_transformations(): + + transforms = Compose([ + # # IMPORTANT - KEEP THESE RESIZE & PAD FUNCTIONS -> + # augmentations.transforms.LongestMaxSize( + # max_size=max(config.MODEL.win, config.MODEL.hin), + # interpolation=cv2.INTER_LINEAR, + # always_apply=True + # ), + # augmentations.transforms.PadIfNeeded( + # min_height=config.MODEL.hin, + # min_width=config.MODEL.win, + # border_mode=cv2.BORDER_CONSTANT, + # value=(0,0,0), + # always_apply=True + # ), + # <- IMPORTANT - KEEP THESE RESIZE & PAD FUNCTIONS + Rotate(limit=60), + RandomBrightness(limit=0.3), + JpegCompression(quality_lower=85, quality_upper=100, p=0.8), + HueSaturationValue(hue_shift_limit=20, sat_shift_limit=30, val_shift_limit=20, p=0.7), + RandomContrast(limit=0.2, p=0.7), + HorizontalFlip(), + ]) + return transforms + +# https://www.programcreek.com/python/example/120573/albumentations.Resize + +ALBU_TRANSFORMS = get_albu_transformations() + +def aug_fn(image, img_size): + data = {"image":image} + aug_data = ALBU_TRANSFORMS(**data) + aug_img = aug_data["image"] + # aug_img = tf.cast(aug_img/255.0, tf.float32) + aug_img = tf.image.resize(aug_img, [img_size[0],img_size[1]]) + # aug_img = tf.image.resize(aug_img, size=[img_size, img_size]) + return aug_img + + +def process_path_with_albu(file_path, img_size): + label = get_label_one_hot_coded(file_path) + # load the raw data from the file as a string + img = tf.io.read_file(file_path) + img = tf.image.decode_jpeg(img, channels=3) + # apply augmentations + aug_img = tf.numpy_function(func=aug_fn, inp=[img, img_size], Tout=tf.float32) + # normalize + pp_img = tf.numpy_function(func=preprocessing_func, inp=[aug_img], Tout=tf.float32) + return pp_img, label + + +def configure_for_performance(ds,batch_size): + ds = ds.cache() + ds = ds.shuffle(buffer_size=100) + ds = ds.batch(batch_size) + ds = ds.prefetch(buffer_size=AUTOTUNE) + return ds + +def _denorm(img, min_image_val, max_image_val): + """ Denormalize image by a min and max value """ + if min_image_val < 0: + if max_image_val > 1: # [-127,127] + img = (img + 127) / 255. + else: # [-1,1] + img = (img + 1.) / 2. + elif max_image_val > 1: # no scaling + img /= 255. + else: # [0,1] + pass + return np.clip(img, 0,1) + +def debug_batch(tf_data): + for image, label in tf_data.take(1): + image = image.numpy() + print("Image shape: ", image.shape) + print("Label: ", label.numpy()) + print("max img val: ", np.max(image)) + print("min img val: ", np.min(image)) + +#### GENERATE TRAIN AND VAL SET ################################# +def get_tf_training_validation_data(df,batch_size=32): + IMG_WIDTH = config.MODEL.win + IMG_HEIGHT = config.MODEL.hin + img_shape = (IMG_HEIGHT, IMG_WIDTH, 3) + images_list = list(df.path) + print("Length of training image list: {:d}.".format(len(images_list))) + list_ds = tf.data.Dataset.list_files(images_list) + ds_train_v3 = list_ds.map(partial(process_path_with_albu, img_size=(IMG_HEIGHT,IMG_WIDTH)), num_parallel_calls=AUTOTUNE) + # set shapes with three parameters + set_shapes_from_config = partial(set_shapes, img_shape=img_shape, num_classes=len(CLASS_NAMES)) + ds_alb = ds_train_v3.map(set_shapes_from_config, num_parallel_calls=AUTOTUNE) + # ds_alb = ds_train_v3.map(set_shapes, num_parallel_calls=AUTOTUNE) # this is necessary for tf.keras models + # ds_train_v4 = configure_for_performance(ds_alb,batch_size) + # divide train and validation sets + image_count = len(images_list) + train_ds, val_ds = split_ds_train_val(ds_alb,image_count) + # configure each set for performance + train_ds = configure_for_performance(train_ds,batch_size) + val_ds = configure_for_performance(val_ds,batch_size) + return train_ds, val_ds #ds_train_v4 + +def get_tf_test_data(df, batch_size=4): + IMG_WIDTH = config.MODEL.win + IMG_HEIGHT = config.MODEL.hin + img_shape = (IMG_HEIGHT, IMG_WIDTH, 3) + images_list = list(df.path) + print("Length of training image list: {:d}.".format(len(images_list))) + list_ds = tf.data.Dataset.list_files(images_list) + ds_test_v1 = list_ds.map(partial(process_path, img_size=(IMG_HEIGHT,IMG_WIDTH)), num_parallel_calls=AUTOTUNE) + # set shapes with three parameters + set_shapes_from_config = partial(set_shapes, img_shape=img_shape, num_classes=len(CLASS_NAMES)) + ds_test_v2 = ds_test_v1.map(set_shapes_from_config, num_parallel_calls=AUTOTUNE) + test_ds = configure_for_performance(ds_test_v2,batch_size) + return test_ds + +def get_eyeball_set_from_tf_data(tf_data): + # get batch of data + vis_image, vis_label = next(iter(tf_data)) # extract 1 batch from the dataset + vis_image = vis_image.numpy() + vis_label = vis_label.numpy() + return vis_image, vis_label + + +#### PLOT ####################################################### +def plot_samples(tf_data): + # custom visualize (notice the visualization difference) + image_batch, label_batch = next(iter(tf_data)) + print(image_batch.shape) + plt.figure(figsize=(10, 10)) + for i in range(9): + ax = plt.subplot(3, 3, i + 1) + # plt.imshow(image_batch[i].numpy().astype("uint8")) + img = image_batch[i].numpy() #.astype("uint8") + print(np.max(img),np.min(img)) + rgb = img[...,::-1].copy() + dnr = _denorm(rgb, np.min(img), np.max(img)) + plt.imshow(dnr[:,:,::-1]) # bgr to rgb + label = label_batch[i] + plt.title(class_names[np.where(label==1)]) # convert one hot encode to labels + plt.axis("off") + +def plot_batch_samples(ds, max_samples=9,save_file_path=None, figscale:int=2): + """ Plot batch samples from a dataset and save to fn """ + if hasattr(ds,'element_spec'): # ad-hoc test to assess if ds is tf.dataset or not... + # draw a batch from tf.data dataset + images, labels = next(iter(ds)) + images = images.numpy() + labels = labels.numpy() + else: + # draw a batch from ImageDataGenerator-based dataset + images, labels = ds.next() + # images, labels = ds.take(1) + + # limit the number of shown images + n_samples = min(len(images), max_samples) + assert n_samples > 0 + root_n = int(math.ceil(math.sqrt(n_samples))) + + f, axs = plt.subplots(root_n, root_n, figsize=(root_n*figscale, root_n*figscale)) + if root_n > 1: + axs = axs.flat + # find denormalization parameters + min_image_val = min([np.array(img).min() for img in images]) + max_image_val = max([np.array(img).max() for img in images]) + + for i in range(n_samples): + img = _denorm(images[i], min_image_val, max_image_val) + # bgr to rgb + # rgb = img[...,::-1].copy() + ax = axs[i] if root_n > 1 else axs + ax.imshow(img) + ax.set_title(labels[i]) + ax.set_axis_off() + # hide rest of the axes + for j in range(i, root_n*root_n): + axs[j].set_axis_off() + plt.show() + if save_file_path: + plt.savefig(os.path.join(save_file_path,'batch_sample.png')) + plt.close() + # plt.savefig(fn, transparent=False) + #plt.close() + +# helper function to visualize images +def view_image(ds): + image, label = next(iter(ds)) # extract 1 batch from the dataset + image = image.numpy() + label = label.numpy() + + fig = plt.figure(figsize=(22, 22)) + for i in range(20): + ax = fig.add_subplot(4, 5, i+1, xticks=[], yticks=[]) + # ax.imshow(image[i]) + ax.imshow(_denorm(image[i], np.min(image[i]), np.max(image[i]))[...,::-1]) # denorm-> rgb to bgr (preprocess_func) + ax.set_title(f"Label: {label[i]}") \ No newline at end of file diff --git a/utils/eval_users.py b/utils/eval_users.py new file mode 100644 index 0000000000000000000000000000000000000000..96d5e4515f691868a86c62e49349b65205f541b1 --- /dev/null +++ b/utils/eval_users.py @@ -0,0 +1,48 @@ +import streamlit as st +import os +ROOT_FIG_DIR = f'{os.getcwd()}/figures/' + +def get_product_dev_page_layout(): + # row6_1, row6_2, = st.columns((1,1)) + row6_1, row6_2,row6_3 = st.tabs(["Evaluation Metrics", "Performance Evaluation", "Issues and Limitations"]) + + with row6_1: + # st.write("**Performance Metrics**") + st.subheader('Performance Metrics') + st.write('Following metrics are used for evaluation:') + st.image(f'{ROOT_FIG_DIR}/evaluation_template.png') + list_test = """
    +
  • Accuracy: it is a ratio of correctly predicted observation to the total observations..
    • +
""" + st.markdown(list_test, unsafe_allow_html=True) + # st.latex(r''' Accuracy=\frac{TP + TN}{TP+TN+FP+FN}''') + list_test = """
    +
  • Precision: It is the ratio of correctly predicted positive observations to the total predicted positive observations
    • +
""" + st.markdown(list_test, unsafe_allow_html=True) + # st.latex(r''' Precision=\frac{TP}{TP+FP}''') + list_test = """
    +
  • Recall: It is the ratio of correctly predicted positive observations to the all observations in actual class.
    • +
""" + st.markdown(list_test, unsafe_allow_html=True) + # st.latex(r''' Recall=\frac{TP}{TP+FN}''') + # with st.expander('Test Set Confusion Matrix'): + # # st.caption('Test Set Results:') + # st.image('./figures/test_confmat_20210404.png') + + with row6_2: + # st.write("**Prediction Samples**") + # with st.expander('Test Set Confusion Matrix'): + # # st.caption('Test Set Results:') + st.subheader('Test Set Confusion Matrix') + st.image(f'{ROOT_FIG_DIR}/test_confmat_20210404.png') + st.subheader('Prediction Samples') + st.caption('Correctly Classified sample predictions:') + st.image(f'{ROOT_FIG_DIR}/pred_stats.png') + + st.caption('Miss Classified sample predictions:') + st.image(f'{ROOT_FIG_DIR}/pred_stats.png') + + st.subheader("Class-wise Prediction Distributions") + st.image(f'{ROOT_FIG_DIR}/training_prob_stats.png') + diff --git a/utils/model_users.py b/utils/model_users.py new file mode 100644 index 0000000000000000000000000000000000000000..ba1db35463cf0f1d0ef67173f60f79ddc5400881 --- /dev/null +++ b/utils/model_users.py @@ -0,0 +1,109 @@ +import streamlit as st +import os + +ROOT_FIG_DIR = f'{os.getcwd()}/figures/' + +def get_product_dev_page_layout(): + model_details ={ + "Model Description": "EfficientNet is used for transfer learning.", + "Model Type": "Convolutional Neural Nets", + } + + dev_details = { + "Training Framework": "Tensorflow Keras", + "Backbone Architeture":"EfficientNetB4", + "Number of classes":4, + "Number of training epochs": 10, + "Dropout rate": 0.2, + "batch_size": 8, + "learning_rate":0.001, + "early_stopping_epochs":10, + "reduce_learning_rate_patience":3, + "source_code":"https://github.com/kaplansinan/MLOps", + + } + + production_details ={ + "Model size": "26MB", + "Model Input": "(N,180,180,3)", + "Modeul Output":"(N,4)", + "Framework":"ONNXRuntime", + + } + + + hardware_details ={ + "Os System": "Ubuntu 20.14", + "GPU Card": "NVIDIA GeForce 3060 6GB", + } + row2_1, row2_2, row2_3= st.tabs(["General Info", "Development Info", "Production Info"]) + + with row2_1: + # st.write("**Architectural Details**") + st.subheader('Architectural Details') + + list_test = """
    +
  • Model Type: Convolutional Neural Nets
    • +
  • Model Description: An architecture from EfficientNet family is used for transfer learning.
    • +
""" + st.markdown(list_test, unsafe_allow_html=True) + # st.json(model_details) + + st.caption('Architeture Visualization') + st.image(f'{ROOT_FIG_DIR}/model_diagram.png') + + with st.expander('License: CC BY 4.0 license(Click for details)'): + st.write(""" + The files associated with this dataset are licensed under a Creative Commons Attribution 4.0 International license. What does this mean? + You can share, copy and modify this dataset so long as you give appropriate credit, + provide a link to the CC BY license, and indicate if changes were made, but you may not do + so in a way that suggests the rights holder has endorsed you or your use of the dataset. + Note that further permission may be required for any content within the dataset + that is identified as belonging to a third party. More details about the licences can be found + [here](https://creativecommons.org/about/cclicenses/). + """) + + # with st.expander('Click for More Info'): + + + with row2_2: + st.subheader('Model Development Details') + # with st.expander('Click for More Info'): + # st.write(['Framework Details:', 'Tensorflow is used for training and testing']) + # st.caption('Framework Details:') + # st.subheader('Subheader Framework Details:') + # st.text('Fixed width text') + # st.markdown('_Markdown_') # see * + + st.write( + """ + ## + Training pipeline is implemented in Python. Tensorflow framework is used for training. + """) + new_title = '
Training Hardware Info:
' + st.markdown(new_title, unsafe_allow_html=True) + st.json(hardware_details) + new_title = '
Training Hyperparameters:
' + # list_test = """
    + #
  • Model Framework: First item
    • + #
  • really important: Second item
    • + #
  • Model Framework: First item
    • + #
  • Fourth item
    • + #
""" + # st.markdown(list_test, unsafe_allow_html=True) + st.markdown(new_title, unsafe_allow_html=True) + # st.write("Tensorflow is used for training and testing") + # st.metric(label="Temp", value="273 K", delta="1.2 K") + # st.write("**Develooment Details**") + st.json(dev_details) + + with row2_3: + # st.write("**Production Details**") + st.subheader('Production Details') + list_test = """
    +
  • Model Size: 26MB
    • +
  • Model Input: (180x180x3)
    • +
  • Model Output: (1x4)
    • +
  • Model Framework: ONNXRuntime
    • +
""" + st.markdown(list_test, unsafe_allow_html=True) \ No newline at end of file diff --git a/utils/model_utils.py b/utils/model_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..c46255b5e514554232a3bdb3e3a2154f9c93b8d2 --- /dev/null +++ b/utils/model_utils.py @@ -0,0 +1,101 @@ +import pandas as pd +from sklearn.model_selection import StratifiedKFold +import os +import glob +import numpy as np +import random +from PIL import Image +import matplotlib.pyplot as plt +import seaborn as sns +import sys +import cv2 +import tensorflow as tf + +sys.path.append('./') +from utils.train_config import config +from utils.datagenerator import _denorm +from utils.datagenerator import get_tf_test_data +from utils.datagenerator import plot_batch_samples,view_image, debug_batch, get_eyeball_set_from_tf_data +from utils.tfexplain_utils import get_tfexplain_callbacks, get_post_gradcam_heatmap, get_peak_location +import utils.vis as vis_utl +from utils.tfexplain_utils import get_thresholded_img_contours + +# preprocessing function +pre_func = tf.keras.applications.efficientnet.preprocess_input + +IMG_WIDTH = config.MODEL.win +IMG_HEIGHT = config.MODEL.hin +img_shape = (IMG_HEIGHT, IMG_WIDTH, 3) + +def load_model(model_path): + new_model = tf.keras.models.load_model(model_path) + return new_model + +def get_feature_extractor_model(keras_model): + feature_extractor = tf.keras.Model(keras_model.inputs,keras_model.layers[-3].output) + return feature_extractor + +def open_gray(fn): + img = cv2.cvtColor(cv2.imread(fn), cv2.COLOR_BGR2GRAY) + img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) + return img + +def read_image_3_channel_v2(path): + img = open_gray(path) + img = cv2.resize(img, (IMG_WIDTH, IMG_HEIGHT), cv2.INTER_LINEAR) + # img = img/255. + # Apply model-specific preprocessing function + img = pre_func(img).astype(float) + return img + +def get_predictions_and_roi(tmp_path, new_model): + LABELS = ['CNV', 'DRUSEN', 'DME', 'NORMAL'] + img3 = read_image_3_channel_v2(tmp_path) + # pre_procc_img_2 = read_image_3_channel_v2(row['path']) + pre_procc_img_2 = np.expand_dims(img3,axis=0) + class_prob = new_model.predict(pre_procc_img_2) + pred_lbl = LABELS[np.argmax(class_prob)] + cls_index = np.argmax(class_prob) + heat_map = get_post_gradcam_heatmap(new_model,img3,class_index=cls_index) + # get thresholded and bounding boox + clos_img,cv2_bbox = get_thresholded_img_contours(heat_map) + # tmp_img = _denorm(tmp_img, np.min(tmp_img), np.max(tmp_img)) + tmp_img = open_gray(tmp_path) + tmp_img = cv2.resize(tmp_img, (IMG_WIDTH, IMG_HEIGHT), cv2.INTER_LINEAR) + #merge map and frame + tmp_img = cv2.addWeighted(heat_map.astype(float), 0.4, tmp_img.astype(float), 1, 0) + tmp_img = vis_utl.draw_bbox(tmp_img,[cv2_bbox[0],cv2_bbox[1],cv2_bbox[0]+cv2_bbox[2],cv2_bbox[1]+cv2_bbox[3]]) + return _denorm(tmp_img, np.min(tmp_img), np.max(tmp_img)), class_prob + +def get_feature_vector(tmp_path, feature_extractor): + img3 = read_image_3_channel_v2(tmp_path) + pre_procc_img_2 = np.expand_dims(img3,axis=0) + # new_model = tf.keras.Model(new_model.inputs, new_model.layers[-3].output) + feature = feature_extractor.predict(pre_procc_img_2) + emb = feature.astype(np.float16) + return emb + + + +# LABELS = ['CNV', 'DRUSEN', 'DME', 'NORMAL'] +# img3 = read_image_3_channel_v2(tmp_path.replace("F:/","E:/")) +# heat_map = get_post_gradcam_heatmap(new_model,img3,class_index=0) +# # pre_procc_img_2 = read_image_3_channel_v2(row['path']) +# pre_procc_img_2 = np.expand_dims(img3,axis=0) +# class_prob = new_model.predict(pre_procc_img_2) +# print("done") +# # new_model = tf.keras.Model(new_model.inputs, new_model.layers[-3].output) +# feature = feature_extractor.predict(pre_procc_img_2) +# print(feature.astype(np.float16).shape) +# pred_prob_max = np.max(class_prob) +# pred_lbl = LABELS[np.argmax(class_prob)] +# print(np.argmax(class_prob)) +# print(pred_lbl) +# # get thresholded and bounding boox +# clos_img,cv2_bbox = get_thresholded_img_contours(heat_map) +# # tmp_img = _denorm(tmp_img, np.min(tmp_img), np.max(tmp_img)) +# tmp_img = open_gray(tmp_path.replace("F:/","E:/")) +# tmp_img = cv2.resize(tmp_img, (IMG_WIDTH, IMG_HEIGHT), cv2.INTER_LINEAR) +# #merge map and frame +# tmp_img = cv2.addWeighted(heat_map.astype(float), 0.4, tmp_img.astype(float), 1, 0) +# tmp_img = vis_utl.draw_bbox(tmp_img,[cv2_bbox[0],cv2_bbox[1],cv2_bbox[0]+cv2_bbox[2],cv2_bbox[1]+cv2_bbox[3]]) diff --git a/utils/tf_image_preprocessing.py b/utils/tf_image_preprocessing.py new file mode 100644 index 0000000000000000000000000000000000000000..32708952bbf9ad8c9ef3837ceb138669473e5737 --- /dev/null +++ b/utils/tf_image_preprocessing.py @@ -0,0 +1,73 @@ +import tensorflow as tf +import numpy as np + +def get_preprocess_func(config): + arch = config.MODEL.backbone_arch + + if arch == 'mobilenetv2': + return tf.keras.applications.mobilenet.preprocess_input + elif 'resnet' in arch: + return tf.keras.applications.resnet.preprocess_input + elif 'eff' in arch: + return tf.keras.applications.efficientnet.preprocess_input + elif 'dense' in arch: + return tf.keras.applications.densenet.preprocess_input + elif arch == 'xception': + return tf.keras.applications.xception.preprocess_input + else: + raise Exception(f'{arch} is not yet implemented') + +def get_unpreprocess_func(config): + """ + Returns function that processes input to 0-1 float range + """ + arch = config.MODEL.backbone_arch + + def tensor_to_numpy(x): + return x.numpy() + + def clip(x): + return np.clip(x, 0.,1.) + + def none_mode(x): + x = tensor_to_numpy(x) + x /= 255. + return clip(x) + + def caffe_mode(x): + mean = [103.939, 116.779, 123.68] + x = tensor_to_numpy(x) + x[..., 0] += mean[0] + x[..., 1] += mean[1] + x[..., 2] += mean[2] + # 'BGR'->'RGB' + x = x[..., ::-1] + x /= 255. + return clip(x) + + def tf_mode(x): + x = tensor_to_numpy(x) + x = (x + 0.5) / 2.0 + return clip(x) + + def torch_mode(x): + mean=[0.485, 0.456, 0.406] + std=[0.229, 0.224, 0.225] + x = tensor_to_numpy(x) + x[..., 0] = (x[..., 0] * std[0]) + mean[0] + x[..., 1] = (x[..., 1] * std[1]) + mean[1] + x[..., 2] = (x[..., 2] * std[2]) + mean[2] + return clip(x) + + if arch == 'mobilenetv2': + return tf_mode + elif 'resnet' in arch: + return caffe_mode + elif 'eff' in arch: + return none_mode + elif 'dense' in arch: + return torch_mode + elif arch == 'xception': + return tf_mode + else: + raise Exception(f'{arch} is not yet implemented') diff --git a/utils/tfexplain_utils.py b/utils/tfexplain_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..f02ec92714f499463c880eced91f3c7ce10f7874 --- /dev/null +++ b/utils/tfexplain_utils.py @@ -0,0 +1,111 @@ +import sys +import tf_explain +import numpy as np +import matplotlib.pyplot as plt +from scipy.ndimage.filters import maximum_filter +import cv2 + +sys.path.append('./') +from utils.datagenerator import _denorm + +print("loading modules") +def get_tfexplain_callbacks(vis_image, vis_label): + + validation_class_zero = (np.array([ + el for el, label in zip(vis_image, vis_label) + if np.all(np.argmax(label) == 0) + ][0:5]), None) + + validation_class_one = (np.array([ + el for el, label in zip(vis_image, vis_label) + if np.all(np.argmax(label) == 1) + ][0:5]), None) + + validation_class_two = (np.array([ + el for el, label in zip(vis_image, vis_label) + if np.all(np.argmax(label) == 2) + ][0:5]), None) + + validation_class_three = (np.array([ + el for el, label in zip(vis_image, vis_label) + if np.all(np.argmax(label) == 3) + ][0:5]), None) + + # class four validation + # validation_class_four = (np.array([ + # el for el, label in zip(vis_image, vis_label) + # if np.all(np.argmax(label) == 4) + # ][0:5]), None) + + callbacks = [ + tf_explain.callbacks.GradCAMCallback(validation_class_zero, class_index=0), + tf_explain.callbacks.GradCAMCallback(validation_class_one, class_index=1), + tf_explain.callbacks.GradCAMCallback(validation_class_two, class_index=2), + tf_explain.callbacks.GradCAMCallback(validation_class_three, class_index=3), + tf_explain.callbacks.ActivationsVisualizationCallback( + validation_class_zero, layers_name=["top_activation"] + ) + ] + + return callbacks + + +def get_post_gradcam_heatmap(model,img,class_index=1, layer_name="top_conv"): + data = ([img], None) + # Start explainer + # https://docs.opencv.org/master/d3/d50/group__imgproc__colormap.html + explainer = tf_explain.core.grad_cam.GradCAM() + grid = explainer.explain(data, model, class_index=class_index,layer_name=layer_name,image_weight=0.0) + return grid + + +def vis_hatmap_over_img(img, heatmap_grid): + fig, ax = plt.subplots(1,1) + ax.imshow(_denorm(img, np.min(img), np.max(img)), cmap='gray') + ax.imshow(heatmap_grid, alpha=0.5, interpolation='bilinear') + return ax + +def get_peak_location(heatmap): + # https://arvrjourney.com/human-pose-estimation-using-openpose-with-tensorflow-part-2-e78ab9104fc8 + heat_gray = cv2.cvtColor(heatmap, cv2.COLOR_RGB2GRAY) + heat_gray_norm = heat_gray/255. + part_candidates = heat_gray_norm*(heat_gray_norm == maximum_filter(heat_gray_norm,footprint=np.ones((9,9)))) + row,col = np.where(part_candidates==np.max(part_candidates)) + xc = col[0] + yc = row[0] + return xc,yc + +def get_thresholded_img_contours(heat_map): + # convert gray + heat_gray = cv2.cvtColor(heat_map, cv2.COLOR_RGB2GRAY) + # decide thr value + thr = np.max(heat_gray) - np.max(heat_gray)/5 + # thr = 175 + # binary thresholding - simple one and can be substitued by adaptive thresholding + # ret,thresh1 = cv2.threshold(heat_gray,thr,np.max(heat_gray) ,cv2.THRESH_BINARY) + ret,thresh1 = cv2.threshold(heat_gray,thr,255 ,cv2.THRESH_BINARY) + # thresh1 = cv2.adaptiveThreshold(heat_gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\ + # cv2.THRESH_BINARY,11,2) + # morphological ops + kernel_ellps = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5)) + # kernel = np.ones((3,3),np.uint8) + clos_img = cv2.morphologyEx(thresh1, cv2.MORPH_CLOSE, kernel_ellps) + # find contours for rectangle + contours, _ = cv2.findContours(clos_img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + # based on contour area, get the maximum ones + cnt_max = max(contours, key=cv2.contourArea) + # get rectangle + x,y,w,h = cv2.boundingRect(cnt_max) + + return clos_img,[x,y,w,h] + +def get_max_area_contour(contours): + # find largest area contour + max_area = -1 + cnt = None + for i in range(len(contours)): + area = cv2.contourArea(contours[i]) + if area>max_area: + cnt = contours[i] + max_area = area + return cnt \ No newline at end of file diff --git a/utils/train_config.py b/utils/train_config.py new file mode 100644 index 0000000000000000000000000000000000000000..08c25114d927a3a358b55fe2c28acc660277ff9a --- /dev/null +++ b/utils/train_config.py @@ -0,0 +1,51 @@ +# Configuration file for training the insect classifier. +# Based on the example by Joni Juvonen / Sinan Kaplan, train_config.py. +# MM 12.11.2020 + +from easydict import EasyDict as edict +import numpy as np + +config = edict() +config.TRAIN = edict() +config.MODEL = edict() +config.DATA = edict() + +##################################################### +# TRAIN +##################################################### +# Unfreeze the base model +config.TRAIN.base_model_trainable = True + +##################################################### +# MODEL +##################################################### +# Backbone archs that are currently implemented in model/__init__.py +# 'resnet50' 'resnet101' 'mobilenetv2' 'effnetb0' up to 'effnetb7' 'densenet121' 'xception' +config.MODEL.backbone_arch = "effnetb4" # 'resnet50' 'mobilenetv2' 'effnetb0' +config.MODEL.base_name = 'resnet50' #'vgg16', 'resnet50' 'mobilenetv2' 'efficientnetb2' +config.MODEL.type = "classification" #'embedding' #'classification' # "feature_extractor" +config.MODEL.emb_size = 512 +config.MODEL.is_train_backbone= False +config.MODEL.dropout_rate = 0.2 +config.MODEL.l2_regularization = True +config.MODEL.pretrained_dir= None +config.MODEL.pretrained_h5= None +config.MODEL.backbone_pretrained = True +config.MODEL.pretrained_model_fully_trainable = False # if loading weights from a previous model where arch_trainable was True, set this to True +config.MODEL.num_classes = 4 +config.MODEL.hin = 180 #75#180 +config.MODEL.win = 180 #75#180 +##################################################### +# DATA +##################################################### +config.DATA.training_csv = "F:/XAI/data/processed/OCT2017/20210819_wholeset_train.csv" # "F:/XAI/data/processed/OCT2017/df_gradcam_region_cut.csv" # "F:/XAI/data/processed/OCT2017/filtered_train_whole_set.csv" +config.DATA.testing_csv = "F:/XAI/data/processed/OCT2017/test.csv" +config.DATA.val_csv = "F:/XAI/data/processed/OCT2017/val.csv" +config.DATA.data_path = "D:/data/XAI/OCT2017/val/" +config.DATA.label_encoder = {'Anbormal':0, 'Normal':1} # {'Not_insect':0, 'Maybe_insect':1, 'Yes_insect':2} +config.DATA.save_csv_path = "D:/data/XAI//processed/OCT2017/val_win.csv" +config.DATA.output_directory = "D:/Learning/anomaly-detection-wms/data/crops/" +config.DATA.save_path= "D:/Learning/anomaly-detection-wms/data/crops/" +config.DATA.n_cv_folds = 5 +config.DATA.image_size = 180 #75#180 +config.DATA.class_names = ['CNV', 'DRUSEN', 'DME', 'NORMAL'] \ No newline at end of file diff --git a/utils/vis.py b/utils/vis.py new file mode 100644 index 0000000000000000000000000000000000000000..646fedb7d58e1d8485eff9b0470ca472ae9ce098 --- /dev/null +++ b/utils/vis.py @@ -0,0 +1,347 @@ +import matplotlib.pyplot as plt +import numpy as np +import cv2 +import math +import itertools +from sklearn.metrics import confusion_matrix, balanced_accuracy_score, precision_score, recall_score +from matplotlib import cm +from mpl_toolkits.mplot3d import Axes3D +from matplotlib.ticker import LinearLocator + +def plot_batch(ds, fn, max_samples=9, figscale:int=2): + """ Plot batch samples from a dataset and save to fn """ + if hasattr(ds,'element_spec'): # ad-hoc test to assess if ds is tf.dataset or not... + # draw a batch from tf.data dataset + images, labels = next(iter(ds)) + images = images.numpy() + labels = labels.numpy() + else: + # draw a batch from ImageDataGenerator-based dataset + images, labels = ds.next() + # images, labels = ds.take(1) + + # limit the number of shown images + n_samples = min(len(images), max_samples) + assert n_samples > 0 + root_n = int(math.ceil(math.sqrt(n_samples))) + + f, axs = plt.subplots(root_n, root_n, figsize=(root_n*figscale, root_n*figscale)) + if root_n > 1: + axs = axs.flat + + # find denormalization parameters + min_image_val = min([np.array(img).min() for img in images]) + max_image_val = max([np.array(img).max() for img in images]) + + for i in range(n_samples): + img = _denorm(images[i], min_image_val, max_image_val) + ax = axs[i] if root_n > 1 else axs + ax.imshow(img) + ax.set_title(labels[i]) + ax.set_axis_off() + + # hide rest of the axes + for j in range(i, root_n*root_n): + axs[j].set_axis_off() + + plt.savefig(fn, transparent=False) + plt.close() + +def plot_batch_preds( + images, + labels, + preds, + fn, + gradcams=None, + title="", + max_samples=9, + figscale:int=2 + ): + """ + Plot batch samples with preds and labels + + Arguments: + images (list): images from dataset batch + labels (list): labels from dataset batch + preds (list): predictions for items + fn (str): filepath to save figure to + gradcams (grid): gradcam grid (optional) + title (str): plot title + max_samples (int): max number of samples to plot + figscale (int): figure scaling factor. 2=regular, 3=large, 4=larger, etc. + """ + # limit the number of shown images + n_samples = min(len(images), max_samples) + assert n_samples > 0 + root_n = int(math.ceil(math.sqrt(n_samples))) + + # leave some space vertically for the labels + fig, axs = plt.subplots(root_n, root_n, figsize=(root_n*figscale, (1 + root_n)*figscale)) + if root_n > 1: + axs = axs.flat + + fig.suptitle(title,fontsize=20) + + # find denormalization parameters + min_image_val = min([np.array(img).min() for img in images]) + max_image_val = max([np.array(img).max() for img in images]) + + # unpack concatenated gradcam grid + if gradcams is None: + gradcam_list = [None for _ in range(n_samples)] + else: + gradcam_list = [] + rolling_x = 0; rolling_y = 0 + H,W = images[0].shape[:2] + orig_h, orig_w = gradcams.shape[:2] + while rolling_y < orig_h: + while rolling_x < orig_w: + gradcam_list.append(gradcams[rolling_y:rolling_y+H, rolling_x:rolling_x+W]) + rolling_x += W + rolling_x = 0 + rolling_y += H + + for i in range(n_samples): + img = _denorm(images[i], min_image_val, max_image_val) + ax = axs[i] if root_n > 1 else axs + + # plot a single channel of the image + ax.imshow(img[:,:,0], cmap='gray') + + # overlay gradcam activations + if gradcam_list[i] is not None: + H,W = img.shape[:2] + ax.imshow(gradcam_list[i], alpha=0.5, extent=(0,H,W,0), interpolation='bilinear') + + ax.set_title(f'GT:{labels[i]}\nPred:{preds[i]}') + ax.set_axis_off() + + # hide rest of the axes + for j in range(i, root_n*root_n): + axs[j].set_axis_off() + + plt.savefig(fn, transparent=False) + plt.close() + +def _denorm(img, min_image_val, max_image_val): + """ Denormalize image by a min and max value """ + if min_image_val < 0: + if max_image_val > 1: # [-127,127] + img = (img + 127) / 255. + else: # [-1,1] + img = (img + 1.) / 2. + elif max_image_val > 1: # no scaling + img /= 255. + else: # [0,1] + pass + return np.clip(img, 0,1) + + +def plot_history(history, fn, figscale=5): + metric_keys = history.history.keys() + # metric keys contain val_ prefixes for validation set, we want to plot these in the same graph with train + train_keys = [key for key in metric_keys if 'val_' not in key] + fig, axs = plt.subplots(len(train_keys), 1, figsize=(figscale, figscale*len(train_keys))) + + for i, train_key in enumerate(train_keys): + val_key = 'val_' + train_key + assert val_key in metric_keys + + train_metric = history.history[train_key] + val_metric = history.history[val_key] + + ax = axs[i] if len(train_keys) > 1 else axs + + ax.plot(train_metric, label=f'Training {train_key}') + ax.plot(val_metric, label=f'Validation {train_key}') + ax.legend() + ax.set_ylabel(f'{train_key}') + ax.set_title(f'Training and Validation {train_key}') + + plt.savefig(fn, transparent=False) + plt.close() + +def plot_confusion_matrix(trues, + preds, + target_names, + title='Confusion matrix', + cmap=None, + normalize=True, + figsize=(10, 10)): + """ + given a sklearn confusion matrix (cm), make a nice plot + + Arguments + --------- + trues: Ground truth array + + preds: Predicted array + + target_names: given classification classes such as [0, 1, 2] + the class names, for example: ['high', 'medium', 'low'] + + title: the text to display at the top of the matrix + + cmap: the gradient of the values displayed from matplotlib.pyplot.cm + see http://matplotlib.org/examples/color/colormaps_reference.html + plt.get_cmap('jet') or plt.cm.Blues + + normalize: If False, plot the raw numbers + If True, plot the proportions + + figsize: tuple of matplotlib figure size + + """ + cm = confusion_matrix(trues, preds) + + accuracy = np.trace(cm) / np.sum(cm).astype('float') + balanced_acc = balanced_accuracy_score(trues, preds) + misclass = 1 - accuracy + + # calculate precision and recall for each class + class_scores = '\n\n' + for i, cl in enumerate(target_names): + cl_trues = np.where(np.array(trues) == i, 1, 0) + cl_preds = np.where(np.array(preds) == i, 1, 0) + precision = precision_score(cl_trues, cl_preds) + recall = recall_score(cl_trues, cl_preds) + class_scores += cl + ' precision {:.3f}, recall {:.3f}'.format(precision, recall) + '\n' + + if cmap is None: + cmap = plt.get_cmap('Blues') + + plt.figure(figsize=figsize) + plt.imshow(cm, interpolation='nearest', cmap=cmap) + plt.title(title + class_scores) + plt.colorbar() + + if target_names is not None: + tick_marks = np.arange(len(target_names)) + plt.xticks(tick_marks, target_names, rotation=45) + plt.yticks(tick_marks, target_names) + + if normalize: + cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] + + thresh = cm.max() / 1.5 if normalize else cm.max() / 2 + for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): + if normalize: + plt.text(j, i, "{:0.4f}".format(cm[i, j]), + horizontalalignment="center", + color="white" if cm[i, j] > thresh else "black") + else: + plt.text(j, i, "{:,}".format(cm[i, j]), + horizontalalignment="center", + color="white" if cm[i, j] > thresh else "black") + + + plt.tight_layout() + plt.ylabel('True label') + plt.xlabel('Predicted label\naccuracy={:0.4f}; misclass={:0.4f}; balanced accuracy={:0.4f}'.format(accuracy, misclass, balanced_acc)) + +def plot_surface_v1(heat_map): + # create the x and y coordinate arrays (here we just use pixel indices) + heat_gray = cv2.cvtColor(heat_map, cv2.COLOR_BGR2GRAY) + lena = heat_gray + xx, yy = np.mgrid[0:lena.shape[0], 0:lena.shape[1]] + + # create the figure + fig = plt.figure() + ax = fig.gca(projection='3d') + # ax2 = fig.add_subplot(1,2,2,projection='3d') + surf = ax.plot_surface(xx, yy, lena ,rstride=1, cstride=1, cmap=plt.cm.jet, + linewidth=0) + # ax.invert_zaxis() + # ax.view_init(20,60) + ax.view_init(30,60) + fig.colorbar(surf, shrink=0.5, aspect=5) + + # show it + plt.show() + + +def plot_surface(fig,heat_map,cnt): + # create the x and y coordinate arrays (here we just use pixel indices) + heat_gray = cv2.cvtColor(heat_map, cv2.COLOR_BGR2GRAY) + lena = heat_gray + # lena[:60,100:] = heat_gray[:60,:80] + xx, yy = np.mgrid[0:lena.shape[0], 0:lena.shape[1]] + + # create the figure + # fig = plt.figure() + # ax = fig.gca(projection='3d') + ax = fig.add_subplot(6,5,cnt,projection='3d') + surf = ax.plot_surface(xx, yy, lena ,rstride=1, cstride=1, cmap=plt.cm.jet, + linewidth=0) + # ax.invert_zaxis() + ax.view_init(30,60) + fig.colorbar(surf, shrink=0.5, aspect=5) + +def visualize(anchor, positive, negative): + """Visualize a few triplets from the supplied batches.""" + + def show(ax, image): + ax.imshow(image) + ax.get_xaxis().set_visible(False) + ax.get_yaxis().set_visible(False) + + fig = plt.figure(figsize=(9, 9)) + + axs = fig.subplots(3, 3) + for i in range(3): + show(axs[i, 0], anchor[i]) + show(axs[i, 1], positive[i]) + show(axs[i, 2], negative[i]) + +def draw_cv2_bbox(img,bboxes,CLASSES,scores=None): + LABELS = ["Background","Bed","Chair","Wheelchair"] + COLORS = [(0,0,0),(255, 0, 0),(255, 128, 0),(0,255,00)] + font = cv2.FONT_HERSHEY_SIMPLEX + fontScale = 0.5 + thickness = 1 + # CLASSES = CLASSES + # boxes = request_response['boxes'] + # masks = np.asarray(request_response['masks']) + # print(bboxes) + for i, box in enumerate(bboxes): + class_id = CLASSES[i] + class_text = LABELS[class_id] + # pts = results['detection_boxes'][0][i3] + xmin = int(round(box[0])) + ymin = int(round(box[1])) + xmax = int(round(box[2])) + ymax = int(round(box[3])) + # print(xmin,ymin,xmax,ymax) + color = COLORS[class_id] + img = cv2.rectangle(img,(xmin,ymin),(xmax,ymax),color,1) + if not scores: + score = scores[i] + tmp_txt = f'{score:.3f}' + img = cv2.putText(img,tmp_txt, (xmin,ymin+5), font, fontScale, color, thickness, cv2.LINE_AA, False) + + +def draw_bbox(img,bbox, color=(255,0,00)): + # pts = results['detection_boxes'][0][i3] + xmin = bbox[0] + ymin = bbox[1] + xmax = bbox[2] + ymax = bbox[3] + img = cv2.rectangle(img,(xmin,ymin),(xmax,ymax),color,1) + return img + +def consctruct_bbox(xc,yc,width=40,height=30): + # tmp_ar = 30 + xmin = xc - width+10 + xmax = xc + width+10 + ymin = yc - height + ymax = yc + height + return [xmin,ymin,xmax,ymax] + +# def draw_bbox(img,bbox, color=(255,0,00)): +# # pts = results['detection_boxes'][0][i3] +# xmin = bbox[0] +# ymin = bbox[1] +# xmax = bbox[2] +# ymax = bbox[3] +# img = cv2.rectangle(img,(xmin,ymin),(xmax,ymax),color,1) +# return img \ No newline at end of file