Create app.py

app.py

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+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+import mediapipe as mp
+import gradio as gr
+
+# Initialize Mediapipe Pose model
+mp_pose = mp.solutions.pose
+pose = mp_pose.Pose(static_image_mode=True)
+
+# Function to estimate height using dynamic calibration based on shoulder width
+def estimate_real_world_height_and_area(image):
+    height, width, _ = image.shape
+    results = pose.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+
+    if not results.pose_landmarks:
+        print("No landmarks detected.")
+        return None, None, None  # Return None if no landmarks detected
+
+    landmarks = results.pose_landmarks.landmark
+
+    # Reference average shoulder width (in cm)
+    AVERAGE_SHOULDER_WIDTH_CM = 45
+
+    # Points for height (nose to ankle) and shoulder width
+    nose = landmarks[mp_pose.PoseLandmark.NOSE]
+    left_shoulder = landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER]
+    right_shoulder = landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER]
+    left_ankle = landmarks[mp_pose.PoseLandmark.LEFT_ANKLE]
+    right_ankle = landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE]
+
+    # Calculate pixel height between nose (head) and ankle
+    head_y = int(nose.y * height)
+    ankle_y = int((left_ankle.y + right_ankle.y) / 2 * height)
+    pixel_height = abs(ankle_y - head_y)
+
+    # Calculate shoulder width in pixels
+    shoulder_width_pixels = abs(left_shoulder.x - right_shoulder.x) * width
+
+    # Determine the pixel-to-cm ratio using shoulder width
+    pixel_to_cm_ratio = AVERAGE_SHOULDER_WIDTH_CM / shoulder_width_pixels if shoulder_width_pixels != 0 else 0
+
+    # Calculate real-world height using this ratio
+    real_world_height_cm = pixel_height * pixel_to_cm_ratio
+
+    # Estimate torso area in pixels
+    torso_width = abs(left_shoulder.x - right_shoulder.x) * width
+    torso_height = abs(left_shoulder.y - left_ankle.y) * height
+    torso_area_pixels = torso_width * torso_height
+    real_torso_area = torso_area_pixels * (pixel_to_cm_ratio ** 2)
+
+    return real_world_height_cm, real_torso_area, landmarks
+
+# Main Gradio prediction function with scaling and plotting
+def predict(image):
+    try:
+        # Classify Gender
+        gender = classify_gender(image)  # Assuming this function is defined
+
+        # Estimate height and area
+        real_height, real_torso_area, landmarks = estimate_real_world_height_and_area(image)
+
+        if real_height is None or real_torso_area is None:
+            return "No landmarks detected; please use a clearer image.", None, None, None
+
+        # Plot with scale
+        fig, ax = plt.subplots(figsize=(10, 10))
+        ax.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+
+        # Plot landmarks if they exist
+        if landmarks:
+            for landmark in landmarks:
+                ax.scatter(landmark.x * image.shape[1], landmark.y * image.shape[0], color="red", s=10)
+
+        # Add height and area text
+        ax.text(10, 30, f"Predicted Gender: {gender}", color="yellow", fontsize=12, backgroundcolor='black')
+        ax.text(10, 60, f"Estimated Height: {real_height:.2f} cm", color="yellow", fontsize=12, backgroundcolor='black')
+        ax.text(10, 90, f"Estimated Torso Area: {real_torso_area:.2f} cm^2", color="yellow", fontsize=12, backgroundcolor='black')
+
+        # Add grid scale to background
+        ax.set_xticks(np.arange(0, image.shape[1], 50))
+        ax.set_yticks(np.arange(0, image.shape[0], 50))
+        ax.grid(color='white', linestyle='--', linewidth=0.5)
+
+        # Save annotated plot for output
+        plt.savefig("output_plot.png")
+        plt.close(fig)
+
+        return gender, real_height, real_torso_area, "output_plot.png"
+    except Exception as e:
+        print(f"Error encountered: {e}")
+        return f"An error occurred: {e}", None, None, None
+
+# Define Gradio interface
+image_input = gr.Image(label="Input Image")
+text_output = gr.Textbox(label="Predicted Gender")
+height_output = gr.Textbox(label="Estimated Height (cm)")
+area_output = gr.Textbox(label="Estimated Torso Area (cm^2)")
+image_output = gr.Image(label="Annotated Image with Measurements")
+
+# Set up Gradio interface
+gr_interface = gr.Interface(
+    fn=predict,
+    inputs=image_input,
+    outputs=[text_output, height_output, area_output, image_output],
+    title="Gender, Height, and Body Measurement Estimation",
+    description="Upload an image to predict gender, estimate height, and calculate 2D torso area with a scale overlay."
+)
+
+# Launch the Gradio app
+gr_interface.launch()