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| 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) | |