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import gradio as gr | |
import cv2 | |
import numpy as np | |
def analyze_skin_conductance(image): | |
img = image if isinstance(image, np.ndarray) else cv2.imread(image) | |
if img is None: | |
return "Error: Unable to process the image." | |
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) | |
mask = cv2.inRange(hsv, np.array([0, 20, 70], dtype=np.uint8), np.array([20, 150, 255], dtype=np.uint8)) | |
sweat_percentage = (np.count_nonzero(mask) / mask.size) * 100 | |
stress_level = "Low" if sweat_percentage < 10 else "Medium" if sweat_percentage < 30 else "High" | |
return f"Estimated stress/arousal level: {stress_level}\nSweat indicator percentage: {sweat_percentage:.2f}%" | |
def create_skin_conductance_tab(): | |
with gr.Column(): | |
image_input = gr.Image() | |
analyze_button = gr.Button("Analyze") | |
output = gr.Textbox(label="Analysis Results") | |
analyze_button.click(analyze_skin_conductance, inputs=image_input, outputs=output) | |
# Add the Examples component | |
gr.Examples(["./assets/images/fitness.jpg"], inputs=[image_input]) |