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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting utools.py\n"
]
}
],
"source": [
"%%writefile utools.py\n",
"import tflite_runtime.interpreter as tflite \n",
"import tflite_runtime\n",
"import numpy as np\n",
"ROWS_PER_FRAME=543\n",
"def load_relevant_data_subset(df):\n",
" data_columns = ['x', 'y', 'z']\n",
" data=df[data_columns]\n",
" n_frames = int(len(data) / ROWS_PER_FRAME)#单个文件的总帧数\n",
" data = data.values.reshape(n_frames, ROWS_PER_FRAME, len(data_columns))\n",
" return data.astype(np.float32)\n",
"\n",
"def mark_pred(model_path_1,aa):\n",
" interpreter = tflite.Interpreter(model_path_1)\n",
" found_signatures = list(interpreter.get_signature_list().keys())\n",
" prediction_fn = interpreter.get_signature_runner(\"serving_default\")\n",
" output_1 = prediction_fn(inputs=aa)\n",
" return output_1\n",
"\n",
"def softmax(x, axis=None):\n",
" x_exp = np.exp(x - np.max(x, axis=axis, keepdims=True))\n",
" return x_exp / np.sum(x_exp, axis=axis, keepdims=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting model.py\n"
]
}
],
"source": [
"%%writefile model.py\n",
"import pandas as pd\n",
"import numpy as np\n",
"import os\n",
"import shutil\n",
"from datetime import datetime\n",
"from timeit import default_timer as timer\n",
"from utools import load_relevant_data_subset,mark_pred\n",
"from utools import softmax\n",
"import mediapipe as mp\n",
"import cv2\n",
"import json\n",
"N=3\n",
"\n",
"ROWS_PER_FRAME=543\n",
"with open('sign_to_prediction_index_map_cn.json', 'r') as f:\n",
" person_dict = json.load(f)\n",
"inverse_dict=dict([val,key] for key,val in person_dict.items())\n",
"\n",
"\n",
"def r_holistic(video_path):\n",
" mp_drawing = mp.solutions.drawing_utils\n",
" mp_drawing_styles = mp.solutions.drawing_styles\n",
" mp_holistic = mp.solutions.holistic\n",
" frame_number = 0\n",
" frame = []\n",
" type_ = []\n",
" index = []\n",
" x = []\n",
" y = []\n",
" z = []\n",
" cap=cv2.VideoCapture(video_path)\n",
" frame_width = int(cap.get(3))\n",
" frame_height = int(cap.get(4))\n",
" fps = int(cap.get(cv2.CAP_PROP_FPS))\n",
" frame_size = (frame_width, frame_height)\n",
" fourcc = cv2.VideoWriter_fourcc(*\"VP80\") #cv2.VideoWriter_fourcc('H.264')\n",
" output_video = \"output_recorded_holistic.webm\"\n",
" out = cv2.VideoWriter(output_video, fourcc, int(fps/N), frame_size)\n",
" with mp_holistic.Holistic(min_detection_confidence=0.5,min_tracking_confidence=0.5) as holistic:\n",
" n=0\n",
" while cap.isOpened():\n",
" frame_number+=1\n",
" n+=1\n",
" ret, image = cap.read()\n",
" if not ret:\n",
" break\n",
" if n%N==0:\n",
" image.flags.writeable = False\n",
" image = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)\n",
" #mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=RGB_frame)\n",
" results = holistic.process(image)\n",
"\n",
" # Draw landmark annotation on the image.\n",
" image.flags.writeable = True\n",
" image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
" mp_drawing.draw_landmarks(\n",
" image,\n",
" results.face_landmarks,\n",
" mp_holistic.FACEMESH_CONTOURS,\n",
" landmark_drawing_spec=None,\n",
" connection_drawing_spec=mp_drawing_styles\n",
" .get_default_face_mesh_contours_style())\n",
" mp_drawing.draw_landmarks(\n",
" image,\n",
" results.pose_landmarks,\n",
" mp_holistic.POSE_CONNECTIONS,\n",
" landmark_drawing_spec=mp_drawing_styles\n",
" .get_default_pose_landmarks_style())\n",
" # Flip the image horizontally for a selfie-view display.\n",
" #if cv2.waitKey(5) & 0xFF == 27:\n",
" out.write(image)\n",
" \n",
" if(results.face_landmarks is None):\n",
" for i in range(468):\n",
" frame.append(frame_number)\n",
" type_.append(\"face\")\n",
" index.append(ind)\n",
" x.append(None)\n",
" y.append(None)\n",
" z.append(None)\n",
" else:\n",
" for ind,val in enumerate(results.face_landmarks.landmark):\n",
" frame.append(frame_number)\n",
" type_.append(\"face\")\n",
" index.append(ind)\n",
" x.append(val.x)\n",
" y.append(val.y)\n",
" z.append(val.z)\n",
" #left hand\n",
" if(results.left_hand_landmarks is None):\n",
" for i in range(21):\n",
" frame.append(frame_number)\n",
" type_.append(\"left_hand\")\n",
" index.append(ind)\n",
" x.append(None)\n",
" y.append(None)\n",
" z.append(None)\n",
" else:\n",
" for ind,val in enumerate(results.left_hand_landmarks.landmark):\n",
" frame.append(frame_number)\n",
" type_.append(\"left_hand\")\n",
" index.append(ind)\n",
" x.append(val.x)\n",
" y.append(val.y)\n",
" z.append(val.z)\n",
" #pose\n",
" if(results.pose_landmarks is None):\n",
" for i in range(33):\n",
" frame.append(frame_number)\n",
" type_.append(\"pose\")\n",
" index.append(ind)\n",
" x.append(None)\n",
" y.append(None)\n",
" z.append(None)\n",
" else:\n",
" for ind,val in enumerate(results.pose_landmarks.landmark):\n",
" frame.append(frame_number)\n",
" type_.append(\"pose\")\n",
" index.append(ind)\n",
" x.append(val.x)\n",
" y.append(val.y)\n",
" z.append(val.z)\n",
" #right hand\n",
" if(results.right_hand_landmarks is None):\n",
" for i in range(21):\n",
" frame.append(frame_number)\n",
" type_.append(\"right_hand\")\n",
" index.append(ind)\n",
" x.append(None)\n",
" y.append(None)\n",
" z.append(None)\n",
" else:\n",
" for ind,val in enumerate(results.right_hand_landmarks.landmark):\n",
" frame.append(frame_number)\n",
" type_.append(\"right_hand\")\n",
" index.append(ind)\n",
" x.append(val.x)\n",
" y.append(val.y)\n",
" z.append(val.z)\n",
" #break\n",
" cap.release()\n",
" out.release()\n",
" cv2.destroyAllWindows()\n",
" df1 = pd.DataFrame({\n",
" \"frame\" : frame,\n",
" \"type\" : type_,\n",
" \"landmark_index\" : index,\n",
" \"x\" : x,\n",
" \"y\" : y,\n",
" \"z\" : z\n",
" })\n",
" aa=load_relevant_data_subset(df1)\n",
" model_path_1='model_1.tflite'\n",
" model_path_2='model_2.tflite'\n",
" model_path_3='model_3.tflite'\n",
" #interpreter = tflite.Interpreter(model_path_1)\n",
" #found_signatures = list(interpreter.get_signature_list().keys())\n",
" #prediction_fn = interpreter.get_signature_runner(\"serving_default\")\n",
" output_1 = mark_pred(model_path_1,aa)\n",
" output_2 = mark_pred(model_path_2,aa)\n",
" output_3 = mark_pred(model_path_3,aa)\n",
" output=softmax(output_1['outputs'])+softmax(output_2['outputs'])+softmax(output_3['outputs'])\n",
" sign = output.argmax()\n",
" lb = inverse_dict.get(sign)\n",
" yield output_video,lb"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting app.py\n"
]
}
],
"source": [
"%%writefile app.py\n",
"\n",
"import gradio as gr\n",
"from model import r_holistic\n",
"\n",
"title='手语动作分类'\n",
"description = \"此分类模型可以识别250个[ASL](https://www.lifeprint.com/)手语动作\\\n",
" 并将其转化为特定的标签, 标签列表见链接[sign_to_prediction_index_map.json](sign_to_prediction_index_map.json), \\\n",
" 大家可以使用示例视频进行测试, 也可以根据列表下载或模拟相应的手语视频测试输出.\\\n",
" \\n工作流程:\\\n",
" \\n 1. landmark提取, 我使用了[ MediaPipe Holistic Solution](https://ai.google.dev/edge/mediapipe/solutions/vision/holistic_landmarker)进行landmark提取.\\\n",
" \\n 2. 利用landmark进行手语识别, 我使用了自己搭建并训练的模型, 主体框架为cnn和transform，此模型在测试数据集上精度在90%以上.\"\n",
"\n",
"output_video_file = gr.Video(label=\"landmark输出\")\n",
"output_text=gr.Textbox(label=\"手语预测结果\")\n",
"slider_1=gr.Slider(0,1,label='detection_confidence')\n",
"slider_2=gr.Slider(0,1,label='tracking_confidence')\n",
"\n",
"iface = gr.Interface(\n",
" fn=r_holistic,\n",
" inputs=[gr.Video(sources=None, label=\"手语视频片段\")],\n",
" outputs= [output_video_file,output_text],\n",
" title=title, \n",
" description=description,\n",
" examples=['book.mp4','book2.mp4','chair1.mp4','chair2.mp4'],\n",
" #cache_examples=True,\n",
" ) #[\"hand-land-mark-video/01.mp4\",\"hand-land-mark-video/02.mp4\"]\n",
" \n",
"\n",
"iface.launch(share=True)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "myenv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.6"
}
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"nbformat": 4,
"nbformat_minor": 2
}
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