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| import gradio as gr | |
| import matplotlib.pyplot as plt | |
| import tempfile, cv2, dlib, imutils | |
| import torch | |
| from scipy.spatial import distance | |
| from imutils import face_utils | |
| def eye_aspect_ratio(eye): | |
| A, B, C = [distance.euclidean(eye[i], eye[j]) for i, j in [(1, 5), (2, 4), (0, 3)]] | |
| return (A + B) / (2.0 * C) | |
| def detect_blinks(video_file): | |
| detector = dlib.get_frontal_face_detector() | |
| predictor = dlib.shape_predictor("assets/models/shape_predictor_68_face_landmarks.dat") | |
| cap = cv2.VideoCapture(video_file) | |
| ear_list, counter, total = [], 0, 0 | |
| while cap.isOpened(): | |
| ret, frame = cap.read() | |
| if not ret: | |
| break | |
| gray = cv2.cvtColor(imutils.resize(frame, width=500), cv2.COLOR_BGR2GRAY) | |
| for rect in detector(gray, 0): | |
| shape = face_utils.shape_to_np(predictor(gray, rect)) | |
| left_eye, right_eye = [shape[face_utils.FACIAL_LANDMARKS_IDXS[eye][0]:face_utils.FACIAL_LANDMARKS_IDXS[eye][1]] for eye in ["left_eye", "right_eye"]] | |
| ear = sum(eye_aspect_ratio(eye) for eye in [left_eye, right_eye]) / 2.0 | |
| ear_list.append(ear) | |
| if ear < 0.3: | |
| counter += 1 | |
| elif counter >= 5: | |
| total += 1 | |
| counter = 0 | |
| else: | |
| counter = 0 | |
| cap.release() | |
| plt.figure(figsize=(10, 4)) | |
| plt.plot(ear_list) | |
| plt.title('Eye Aspect Ratio over Time') | |
| plt.xlabel('Frame') | |
| plt.ylabel('EAR') | |
| plt.tight_layout() | |
| temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.png') | |
| plt.savefig(temp_file.name) | |
| plt.close() | |
| return total, temp_file.name | |
| def create_blink_tab(): | |
| with gr.Row(): | |
| with gr.Column(scale=2): | |
| input_video = gr.Video(label="Input Video") | |
| with gr.Row(): | |
| clear_btn = gr.Button("Clear", scale=1) | |
| submit_btn = gr.Button("Analyze", scale=1, elem_classes="submit") | |
| with gr.Column(scale=1): | |
| output_count = gr.Label(label="Blink Count") | |
| output_plot = gr.Image(label="EAR Plot") | |
| submit_btn.click(fn=detect_blinks, inputs=[input_video], outputs=[output_count, output_plot]) | |
| clear_btn.click(lambda: (None, None, None), outputs=[input_video, output_count, output_plot]) | |
| gr.Examples(["./assets/videos/fitness.mp4"], [input_video]) | |