Update app.py

app.py

@@ -129,25 +129,121 @@ def angle_between_the_legs(image):
         return annotated_image_rgb, left_angle_deg, right_angle_deg, angle_between_legs
 
 
+# Define the function to calculate the tilt of the body
+def tilt_of_body(image):
+    """
+    Calculate the tilt angle of the body by measuring the angle between a vertical line (y-axis)
+    and a line connecting the shoulders.
+
+    Args:
+        image: Input image in BGR format.
+
+    Returns:
+        A tuple containing:
+        - The annotated image with visualization
+        - Tilt angle in degrees
+    """
+    # Initialize MediaPipe Pose
+    mp_pose = mp.solutions.pose
+    mp_drawing = mp.solutions.drawing_utils
+
+    # Convert the image to RGB (MediaPipe requires RGB images)
+    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+    # Initialize the Pose model
+    with mp_pose.Pose(static_image_mode=True, model_complexity=2, enable_segmentation=False) as pose:
+        # Process the image
+        results = pose.process(image_rgb)
+
+        # Check if pose landmarks were detected
+        if not results.pose_landmarks:
+            print("No pose landmarks detected.")
+            return image, None
+
+        # Create a copy of the image for annotation
+        annotated_image = image.copy()
+
+        # Get landmark coordinates
+        landmarks = results.pose_landmarks.landmark
+
+        # Get image dimensions for converting normalized coordinates
+        h, w, _ = annotated_image.shape
+
+        # Get relevant landmarks
+        # Left shoulder point
+        left_shoulder = (int(landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].x * w),
+                         int(landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y * h))
+
+        # Right shoulder point
+        right_shoulder = (int(landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value].x * w),
+                          int(landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value].y * h))
+
+        # Draw the vertical line (y-axis) from mid-hip
+        vertical_top = (w // 2, 0)
+        vertical_bottom = (w // 2, h)
+        cv2.line(annotated_image, vertical_top, vertical_bottom, (0, 255, 255), 2)
+
+        # Draw the line connecting the shoulders
+        cv2.line(annotated_image, left_shoulder, right_shoulder, (255, 0, 0), 2)
+
+        # Calculate vectors for angle measurement
+        vertical_vector = np.array([0, 1])  # Vertical direction (down is positive in image coordinates)
+        shoulder_vector = np.array([right_shoulder[0] - left_shoulder[0],
+                                   right_shoulder[1] - left_shoulder[1]])
+
+        # Normalize shoulder vector
+        if np.linalg.norm(shoulder_vector) > 0:
+            shoulder_vector = shoulder_vector / np.linalg.norm(shoulder_vector)
+
+        # Calculate the angle between the vertical line and the shoulder line
+        dot_product = np.dot(vertical_vector, shoulder_vector)
+        cross_product = np.cross(np.array([vertical_vector[0], vertical_vector[1], 0]),
+                                np.array([shoulder_vector[0], shoulder_vector[1], 0]))[2]
+
+        # Calculate angle using arccos
+        angle_rad = np.arccos(np.clip(dot_product, -1.0, 1.0))
+        angle_deg = np.degrees(angle_rad)
+
+        # Determine the direction of tilt
+        if cross_product < 0:
+            angle_deg = -angle_deg
+
+        # Add text annotation with tilt angle value
+        cv2.putText(annotated_image, f"Tilt angle: {angle_deg:.1f}°",
+                    (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
+
+        # Add markers for key points
+        cv2.circle(annotated_image, left_shoulder, 5, (0, 255, 0), -1)  # Green for left shoulder
+        cv2.circle(annotated_image, right_shoulder, 5, (0, 255, 0), -1)  # Green for right shoulder
+
+        # Draw pose landmarks on the image
+        mp_drawing.draw_landmarks(
+            annotated_image,
+            results.pose_landmarks,
+            mp_pose.POSE_CONNECTIONS,
+            landmark_drawing_spec=mp_drawing.DrawingSpec(color=(0, 255, 0), thickness=2, circle_radius=2),
+            connection_drawing_spec=mp_drawing.DrawingSpec(color=(255, 0, 0), thickness=2))
+
+        # Convert the annotated image back to RGB for display
+        annotated_image_rgb = cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB)
+
+        return annotated_image_rgb, angle_deg
+
 # Streamlit app
-st.title("Leg Angle Calculation using MediaPipe")
+st.title("Body and Leg Pose Analysis")
 
-# Image uploader
-uploaded_file = st.file_uploader("Upload an Image", type=["png", "jpg", "jpeg"])
+# Upload image
+uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
 
 if uploaded_file is not None:
-    # Read the image from the uploaded file
+    # Load the image using OpenCV
     image = np.array(bytearray(uploaded_file.read()), dtype=np.uint8)
     image = cv2.imdecode(image, cv2.IMREAD_COLOR)
 
-    # Get the leg angle analysis
-    annotated_image_rgb, left_angle, right_angle, total_angle = angle_between_the_legs(image)
-
-    if annotated_image_rgb is not None:
-        # Display the annotated image
-        st.image(annotated_image_rgb, caption=f"Leg Angle Analysis: Total = {total_angle:.1f}°", use_column_width=True)
+    # Analyze angles between legs and tilt of the body
+    annotated_image_legs, left_angle, right_angle, total_angle = angle_between_the_legs(image)
+    annotated_image_tilt, tilt_angle = tilt_of_body(image)
 
-        # Print the results
-        st.write(f"Left leg angle: {left_angle:.1f}°")
-        st.write(f"Right leg angle: {right_angle:.1f}°")
-        st.write(f"Total angle between legs: {total_angle:.1f}°")
+    # Show the annotated images
+    st.image(annotated_image_legs, caption=f"Leg Angles (Total: {total_angle:.1f}°)", use_column_width=True)
+    st.image(annotated_image_tilt, caption=f"Body Tilt Angle: {tilt_angle:.1f}°", use_column_width=True)