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| import gradio as gr | |
| import cv2 | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from app.app_utils import preprocess_frame_and_predict_aus | |
| # Define the AUs associated with stress, anxiety, and depression | |
| STRESS_AUS = [4, 7, 17, 23, 24] | |
| ANXIETY_AUS = [1, 2, 4, 5, 20] | |
| DEPRESSION_AUS = [1, 4, 15, 17] | |
| AU_DESCRIPTIONS = { | |
| 1: "Inner Brow Raiser", | |
| 2: "Outer Brow Raiser", | |
| 4: "Brow Lowerer", | |
| 5: "Upper Lid Raiser", | |
| 7: "Lid Tightener", | |
| 15: "Lip Corner Depressor", | |
| 17: "Chin Raiser", | |
| 20: "Lip Stretcher", | |
| 23: "Lip Tightener", | |
| 24: "Lip Pressor" | |
| } | |
| def normalize_score(score): | |
| return max(0, min(1, (score + 1.5) / 3)) # Adjust the range as needed | |
| def process_video_for_facs(video_path): | |
| cap = cv2.VideoCapture(video_path) | |
| frames = [] | |
| au_intensities_list = [] | |
| while True: | |
| ret, frame = cap.read() | |
| if not ret: | |
| break | |
| processed_frame, au_intensities, _ = preprocess_frame_and_predict_aus(frame) | |
| if processed_frame is not None and au_intensities is not None: | |
| frames.append(processed_frame) | |
| au_intensities_list.append(au_intensities) | |
| cap.release() | |
| if not frames: | |
| return None, None | |
| # Calculate average AU intensities | |
| avg_au_intensities = np.mean(au_intensities_list, axis=0) | |
| # Calculate and normalize emotional state scores | |
| stress_score = normalize_score(np.mean([avg_au_intensities[au-1] for au in STRESS_AUS if au <= len(avg_au_intensities)])) | |
| anxiety_score = normalize_score(np.mean([avg_au_intensities[au-1] for au in ANXIETY_AUS if au <= len(avg_au_intensities)])) | |
| depression_score = normalize_score(np.mean([avg_au_intensities[au-1] for au in DEPRESSION_AUS if au <= len(avg_au_intensities)])) | |
| fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 10)) | |
| # Emotional state scores | |
| states = ['Stress', 'Anxiety', 'Depression'] | |
| scores = [stress_score, anxiety_score, depression_score] | |
| bars = ax1.bar(states, scores) | |
| ax1.set_ylim(0, 1) | |
| ax1.set_title('Emotional State Scores') | |
| for bar in bars: | |
| height = bar.get_height() | |
| ax1.text(bar.get_x() + bar.get_width()/2., height, | |
| f'{height:.2f}', ha='center', va='bottom') | |
| # AU intensities | |
| all_aus = sorted(set(STRESS_AUS + ANXIETY_AUS + DEPRESSION_AUS)) | |
| all_aus = [au for au in all_aus if au <= len(avg_au_intensities)] | |
| au_labels = [f"AU{au}\n{AU_DESCRIPTIONS.get(au, '')}" for au in all_aus] | |
| au_values = [avg_au_intensities[au-1] for au in all_aus] | |
| ax2.bar(range(len(au_labels)), au_values) | |
| ax2.set_xticks(range(len(au_labels))) | |
| ax2.set_xticklabels(au_labels, rotation=45, ha='right') | |
| ax2.set_ylim(0, 1) | |
| ax2.set_title('Average Action Unit Intensities') | |
| plt.tight_layout() | |
| return frames[-1], fig # Return the last processed frame and the plot | |
| def create_facs_analysis_sad_tab(): | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| input_video = gr.Video() | |
| analyze_btn = gr.Button("Analyze") | |
| gr.Examples(["./assets/videos/fitness.mp4"], inputs=[input_video]) | |
| with gr.Column(scale=2): | |
| output_image = gr.Image(label="Processed Frame") | |
| facs_chart = gr.Plot(label="FACS Analysis for SAD") | |
| analyze_btn.click( | |
| fn=process_video_for_facs, | |
| inputs=input_video, | |
| outputs=[output_image, facs_chart], | |
| ) | |
| return input_video, output_image, facs_chart, analyze_btn |