Update app.py

app.py

@@ -1,374 +1,386 @@
-
-
-import cv2
-import numpy as np
-import mediapipe as mp
-import torch
-from flask import Flask, request, jsonify
-import torch.nn.functional as F
-
-
-app = Flask(__name__)
-
-mp_pose = mp.solutions.pose
-mp_holistic = mp.solutions.holistic
-pose = mp_pose.Pose(model_complexity=2)  # Improved accuracy
-holistic = mp_holistic.Holistic()  # For refining pose
-
-KNOWN_OBJECT_WIDTH_CM = 21.0  # A4 paper width in cm
-FOCAL_LENGTH = 600  # Default focal length
-DEFAULT_HEIGHT_CM = 152.0  # Default height if not provided
-
-# Load depth estimation model
-def load_depth_model():
-    model = torch.hub.load("intel-isl/MiDaS", "MiDaS_small")
-    model.eval()
-    return model
-
-depth_model = load_depth_model()
-
-def calibrate_focal_length(image, real_width_cm, detected_width_px):
-    """Dynamically calibrates focal length using a known object."""
-    return (detected_width_px * FOCAL_LENGTH) / real_width_cm if detected_width_px else FOCAL_LENGTH
-
-
-
-def detect_reference_object(image):
-    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-    edges = cv2.Canny(gray, 50, 150)
-    contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-    if contours:
-        largest_contour = max(contours, key=cv2.contourArea)
-        x, y, w, h = cv2.boundingRect(largest_contour)
-        focal_length = calibrate_focal_length(image, KNOWN_OBJECT_WIDTH_CM, w)
-        scale_factor = KNOWN_OBJECT_WIDTH_CM / w
-        return scale_factor, focal_length
-    return 0.05, FOCAL_LENGTH
-
-def estimate_depth(image):
-    """Uses AI-based depth estimation to improve circumference calculations."""
-    input_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) / 255.0
-    input_tensor = torch.tensor(input_image, dtype=torch.float32).permute(2, 0, 1).unsqueeze(0)
-    
-    # Resize input to match MiDaS model input size
-    input_tensor = F.interpolate(input_tensor, size=(384, 384), mode="bilinear", align_corners=False)
-
-    with torch.no_grad():
-        depth_map = depth_model(input_tensor)
-    
-    return depth_map.squeeze().numpy()
-
-def calculate_distance_using_height(landmarks, image_height, user_height_cm):
-    """Calculate distance using the user's known height."""
-    top_head = landmarks[mp_pose.PoseLandmark.NOSE.value].y * image_height
-    bottom_foot = max(
-        landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].y,
-        landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value].y
-    ) * image_height
-    
-    person_height_px = abs(bottom_foot - top_head)
-    
-    # Using the formula: distance = (actual_height_cm * focal_length) / height_in_pixels
-    distance = (user_height_cm * FOCAL_LENGTH) / person_height_px
-    
-    # Calculate more accurate scale_factor based on known height
-    scale_factor = user_height_cm / person_height_px
-    
-    return distance, scale_factor
-
-def get_body_width_at_height(frame, height_px, center_x):
-    """Scan horizontally at a specific height to find body edges."""
-    # Convert to grayscale and apply threshold
-    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-    blur = cv2.GaussianBlur(gray, (5, 5), 0)
-    _, thresh = cv2.threshold(blur, 50, 255, cv2.THRESH_BINARY)
-    
-    # Ensure height_px is within image bounds
-    if height_px >= frame.shape[0]:
-        height_px = frame.shape[0] - 1
-    
-    # Get horizontal line at the specified height
-    horizontal_line = thresh[height_px, :]
-    
-    # Find left and right edges starting from center
-    center_x = int(center_x * frame.shape[1])
-    left_edge, right_edge = center_x, center_x
-    
-    # Scan from center to left
-    for i in range(center_x, 0, -1):
-        if horizontal_line[i] == 0:  # Found edge (black pixel)
-            left_edge = i
-            break
-    
-    # Scan from center to right
-    for i in range(center_x, len(horizontal_line)):
-        if horizontal_line[i] == 0:  # Found edge (black pixel)
-            right_edge = i
-            break
-            
-    width_px = right_edge - left_edge
-    
-    # If width is unreasonably small, apply a minimum width
-    min_width = 0.1 * frame.shape[1]  # Minimum width as 10% of image width
-    if width_px < min_width:
-        width_px = min_width
-        
-    return width_px
-
-def calculate_measurements(results, scale_factor, image_width, image_height, depth_map, frame=None, user_height_cm=None):
-    landmarks = results.pose_landmarks.landmark
-
-    # If user's height is provided, use it to get a more accurate scale factor
-    if user_height_cm:
-        _, scale_factor = calculate_distance_using_height(landmarks, image_height, user_height_cm)
-
-    def pixel_to_cm(value):
-        return round(value * scale_factor, 2)
-    
-    def calculate_circumference(width_px, depth_ratio=1.0):
-        """Estimate circumference using width and depth adjustment."""
-        # Using a simplified elliptical approximation: C ≈ 2π * sqrt((a² + b²)/2)
-        # where a is half the width and b is estimated depth
-        width_cm = width_px * scale_factor
-        estimated_depth_cm = width_cm * depth_ratio * 0.7  # Depth is typically ~70% of width for torso
-        half_width = width_cm / 2
-        half_depth = estimated_depth_cm / 2
-        return round(2 * np.pi * np.sqrt((half_width**2 + half_depth**2) / 2), 2)
-
-    measurements = {}
-
-    # Shoulder Width
-    left_shoulder = landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value]
-    right_shoulder = landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value]
-    shoulder_width_px = abs(left_shoulder.x * image_width - right_shoulder.x * image_width)
-    
-    # Apply a slight correction factor for shoulders (they're usually detected well)
-    shoulder_correction = 1.1  # 10% wider
-    shoulder_width_px *= shoulder_correction
-    
-    measurements["shoulder_width"] = pixel_to_cm(shoulder_width_px)
-
-    # Chest/Bust Measurement
-    chest_y_ratio = 0.15  # Approximately 15% down from shoulder to hip
-    chest_y = left_shoulder.y + (landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].y - left_shoulder.y) * chest_y_ratio
-    
-    chest_correction = 1.15  # 15% wider than detected width
-    chest_width_px = abs((right_shoulder.x - left_shoulder.x) * image_width) * chest_correction
-    
-    if frame is not None:
-        chest_y_px = int(chest_y * image_height)
-        center_x = (left_shoulder.x + right_shoulder.x) / 2
-        detected_width = get_body_width_at_height(frame, chest_y_px, center_x)
-        if detected_width > 0:
-            chest_width_px = max(chest_width_px, detected_width)
-    
-    chest_depth_ratio = 1.0
-    if depth_map is not None:
-        chest_x = int(((left_shoulder.x + right_shoulder.x) / 2) * image_width)
-        chest_y_px = int(chest_y * image_height)
-        scale_y = 384 / image_height
-        scale_x = 384 / image_width
-        chest_y_scaled = int(chest_y_px * scale_y)
-        chest_x_scaled = int(chest_x * scale_x)
-        if 0 <= chest_y_scaled < 384 and 0 <= chest_x_scaled < 384:
-            chest_depth = depth_map[chest_y_scaled, chest_x_scaled]
-            max_depth = np.max(depth_map)
-            chest_depth_ratio = 1.0 + 0.5 * (1.0 - chest_depth / max_depth)
-    
-    measurements["chest_width"] = pixel_to_cm(chest_width_px)
-    measurements["chest_circumference"] = calculate_circumference(chest_width_px, chest_depth_ratio)
-    
-
-    # Waist Measurement
-    left_hip = landmarks[mp_pose.PoseLandmark.LEFT_HIP.value]
-    right_hip = landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value]
-
-    # Adjust waist_y_ratio to better reflect the natural waistline
-    waist_y_ratio = 0.35  # 35% down from shoulder to hip (higher than before)
-    waist_y = left_shoulder.y + (left_hip.y - left_shoulder.y) * waist_y_ratio
-
-    # Use contour detection to dynamically estimate waist width
-    if frame is not None:
-        waist_y_px = int(waist_y * image_height)
-        center_x = (left_hip.x + right_hip.x) / 2
-        detected_width = get_body_width_at_height(frame, waist_y_px, center_x)
-        if detected_width > 0:
-            waist_width_px = detected_width
-        else:
-            # Fallback to hip width if contour detection fails
-            waist_width_px = abs(right_hip.x - left_hip.x) * image_width * 0.9  # 90% of hip width
-    else:
-        # Fallback to hip width if no frame is provided
-        waist_width_px = abs(right_hip.x - left_hip.x) * image_width * 0.9  # 90% of hip width
-
-    # Apply 30% correction factor to waist width
-    waist_correction = 1.16  # 30% wider
-    waist_width_px *= waist_correction
-
-    # Get depth adjustment for waist if available
-    waist_depth_ratio = 1.0
-    if depth_map is not None:
-        waist_x = int(((left_hip.x + right_hip.x) / 2) * image_width)
-        waist_y_px = int(waist_y * image_height)
-        scale_y = 384 / image_height
-        scale_x = 384 / image_width
-        waist_y_scaled = int(waist_y_px * scale_y)
-        waist_x_scaled = int(waist_x * scale_x)
-        if 0 <= waist_y_scaled < 384 and 0 <= waist_x_scaled < 384:
-            waist_depth = depth_map[waist_y_scaled, waist_x_scaled]
-            max_depth = np.max(depth_map)
-            waist_depth_ratio = 1.0 + 0.5 * (1.0 - waist_depth / max_depth)
-
-    measurements["waist_width"] = pixel_to_cm(waist_width_px)
-    measurements["waist"] = calculate_circumference(waist_width_px, waist_depth_ratio)
-    # Hip Measurement
-    hip_correction = 1.35  # Hips are typically 35% wider than detected landmarks
-    hip_width_px = abs(left_hip.x * image_width - right_hip.x * image_width) * hip_correction
-    
-    if frame is not None:
-        hip_y_offset = 0.1  # 10% down from hip landmarks
-        hip_y = left_hip.y + (landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].y - left_hip.y) * hip_y_offset
-        hip_y_px = int(hip_y * image_height)
-        center_x = (left_hip.x + right_hip.x) / 2
-        detected_width = get_body_width_at_height(frame, hip_y_px, center_x)
-        if detected_width > 0:
-            hip_width_px = max(hip_width_px, detected_width)
-    
-    hip_depth_ratio = 1.0
-    if depth_map is not None:
-        hip_x = int(((left_hip.x + right_hip.x) / 2) * image_width)
-        hip_y_px = int(left_hip.y * image_height)
-        hip_y_scaled = int(hip_y_px * scale_y)
-        hip_x_scaled = int(hip_x * scale_x)
-        if 0 <= hip_y_scaled < 384 and 0 <= hip_x_scaled < 384:
-            hip_depth = depth_map[hip_y_scaled, hip_x_scaled]
-            max_depth = np.max(depth_map)
-            hip_depth_ratio = 1.0 + 0.5 * (1.0 - hip_depth / max_depth)
-    
-    measurements["hip_width"] = pixel_to_cm(hip_width_px)
-    measurements["hip"] = calculate_circumference(hip_width_px, hip_depth_ratio)
-
-    # Other measurements (unchanged)
-    neck = landmarks[mp_pose.PoseLandmark.NOSE.value]
-    left_ear = landmarks[mp_pose.PoseLandmark.LEFT_EAR.value]
-    neck_width_px = abs(neck.x * image_width - left_ear.x * image_width) * 2.0
-    measurements["neck"] = calculate_circumference(neck_width_px, 1.0)
-    measurements["neck_width"] = pixel_to_cm(neck_width_px)
-
-    left_wrist = landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value]
-    sleeve_length_px = abs(left_shoulder.y * image_height - left_wrist.y * image_height)
-    measurements["arm_length"] = pixel_to_cm(sleeve_length_px)
-
-    shirt_length_px = abs(left_shoulder.y * image_height - left_hip.y * image_height) * 1.2
-    measurements["shirt_length"] = pixel_to_cm(shirt_length_px)
-
-     # Thigh Circumference (improved with depth information)
-    thigh_y_ratio = 0.2  # 20% down from hip to knee
-    left_knee = landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value]
-    thigh_y = left_hip.y + (left_knee.y - left_hip.y) * thigh_y_ratio
-    
-    # Apply correction factor for thigh width
-    thigh_correction = 1.2  # Thighs are typically wider than what can be estimated from front view
-    thigh_width_px = hip_width_px * 0.5 * thigh_correction  # Base thigh width on hip width
-    
-    # Use contour detection if frame is available
-    if frame is not None:
-        thigh_y_px = int(thigh_y * image_height)
-        thigh_x = left_hip.x * 0.9  # Move slightly inward from hip
-        detected_width = get_body_width_at_height(frame, thigh_y_px, thigh_x)
-        if detected_width > 0 and detected_width < hip_width_px:  # Sanity check
-            thigh_width_px = detected_width  # Use detected width
-    
-    # If depth map is available, use it for thigh measurement
-    thigh_depth_ratio = 1.0
-    if depth_map is not None:
-        thigh_x = int(left_hip.x * image_width)
-        thigh_y_px = int(thigh_y * image_height)
-        
-        # Scale coordinates to match depth map size
-        thigh_y_scaled = int(thigh_y_px * scale_y)
-        thigh_x_scaled = int(thigh_x * scale_x)
-        
-        if 0 <= thigh_y_scaled < 384 and 0 <= thigh_x_scaled < 384:
-            thigh_depth = depth_map[thigh_y_scaled, thigh_x_scaled]
-            max_depth = np.max(depth_map)
-            thigh_depth_ratio = 1.0 + 0.5 * (1.0 - thigh_depth / max_depth)
-    
-    measurements["thigh"] = pixel_to_cm(thigh_width_px)
-    measurements["thigh_circumference"] = calculate_circumference(thigh_width_px, thigh_depth_ratio)
-
-
-    left_ankle = landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value]
-    trouser_length_px = abs(left_hip.y * image_height - left_ankle.y * image_height)
-    measurements["trouser_length"] = pixel_to_cm(trouser_length_px)
-
-    return measurements
-    
-@app.route("/upload_images", methods=["POST"])
-def upload_images():
-    if "front" not in request.files:
-        return jsonify({"error": "Missing front image for reference."}), 400
-    
-    # Get user height if provided, otherwise use default
-    user_height_cm = request.form.get('height_cm')
-    print(user_height_cm)
-    if user_height_cm:
-        try:
-            user_height_cm = float(user_height_cm)
-        except ValueError:
-            user_height_cm = DEFAULT_HEIGHT_CM
-    else:
-        user_height_cm = DEFAULT_HEIGHT_CM
-    
-    received_images = {pose_name: request.files[pose_name] for pose_name in ["front", "left_side", "right_side", "back"] if pose_name in request.files}
-    measurements, scale_factor, focal_length, results = {}, None, FOCAL_LENGTH, {}
-    frames = {}
-    
-    for pose_name, image_file in received_images.items():
-        image_np = np.frombuffer(image_file.read(), np.uint8)
-        frame = cv2.imdecode(image_np, cv2.IMREAD_COLOR)
-        frames[pose_name] = frame  # Store the frame for contour detection
-        rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-        results[pose_name] = holistic.process(rgb_frame)
-        image_height, image_width, _ = frame.shape
-        
-        if pose_name == "front":
-            # Always use height for calibration (default or provided)
-            if results[pose_name].pose_landmarks:
-                _, scale_factor = calculate_distance_using_height(
-                    results[pose_name].pose_landmarks.landmark,
-                    image_height,
-                    user_height_cm
-                )
-            else:
-                # Fallback to object detection only if pose landmarks aren't detected
-                scale_factor, focal_length = detect_reference_object(frame)
-        
-        depth_map = estimate_depth(frame) if pose_name in ["front", "left_side"] else None
-        
-        if results[pose_name].pose_landmarks:
-            if pose_name == "front":
-                measurements.update(calculate_measurements(
-                    results[pose_name], 
-                    scale_factor, 
-                    image_width, 
-                    image_height, 
-                    depth_map,
-                    frames[pose_name],  # Pass the frame for contour detection
-                    user_height_cm
-                ))
-    
-    # Debug information to help troubleshoot measurements
-    debug_info = {
-        "scale_factor": float(scale_factor) if scale_factor else None,
-        "focal_length": float(focal_length),
-        "user_height_cm": float(user_height_cm)
-    }
-    
-    return jsonify({ 
-        "measurements": measurements,
-        "debug_info": debug_info
-    })
-
-if __name__ == '__main__':
-    app.run(host='0.0.0.0', port=7860)
+
+
+import cv2
+import numpy as np
+import mediapipe as mp
+import torch
+from flask import Flask, request, jsonify
+import torch.nn.functional as F
+from flask_cors import CORS
+import os
+
+# NEW: Set mediapipe model path to avoid permission issues
+CUSTOM_MODEL_PATH = "/app/models/pose_landmark_heavy.tflite"
+os.environ["MEDIAPIPE_MODEL_PATH"] = CUSTOM_MODEL_PATH
+
+app = Flask(__name__)
+CORS(app)
+
+mp_pose = mp.solutions.pose
+mp_holistic = mp.solutions.holistic
+pose = mp_pose.Pose(
+    model_complexity=2,
+    static_image_mode=True,
+    min_detection_confidence=0.5,
+    min_tracking_confidence=0.5,
+    model_asset_path=CUSTOM_MODEL_PATH  # <-- Modified
+) # Improved accuracy
+holistic = mp_holistic.Holistic()  # For refining pose
+
+KNOWN_OBJECT_WIDTH_CM = 21.0  # A4 paper width in cm
+FOCAL_LENGTH = 600  # Default focal length
+DEFAULT_HEIGHT_CM = 152.0  # Default height if not provided
+
+# Load depth estimation model
+def load_depth_model():
+    model = torch.hub.load("intel-isl/MiDaS", "MiDaS_small")
+    model.eval()
+    return model
+
+depth_model = load_depth_model()
+
+def calibrate_focal_length(image, real_width_cm, detected_width_px):
+    """Dynamically calibrates focal length using a known object."""
+    return (detected_width_px * FOCAL_LENGTH) / real_width_cm if detected_width_px else FOCAL_LENGTH
+
+
+
+def detect_reference_object(image):
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    edges = cv2.Canny(gray, 50, 150)
+    contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    if contours:
+        largest_contour = max(contours, key=cv2.contourArea)
+        x, y, w, h = cv2.boundingRect(largest_contour)
+        focal_length = calibrate_focal_length(image, KNOWN_OBJECT_WIDTH_CM, w)
+        scale_factor = KNOWN_OBJECT_WIDTH_CM / w
+        return scale_factor, focal_length
+    return 0.05, FOCAL_LENGTH
+
+def estimate_depth(image):
+    """Uses AI-based depth estimation to improve circumference calculations."""
+    input_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) / 255.0
+    input_tensor = torch.tensor(input_image, dtype=torch.float32).permute(2, 0, 1).unsqueeze(0)
+    
+    # Resize input to match MiDaS model input size
+    input_tensor = F.interpolate(input_tensor, size=(384, 384), mode="bilinear", align_corners=False)
+
+    with torch.no_grad():
+        depth_map = depth_model(input_tensor)
+    
+    return depth_map.squeeze().numpy()
+
+def calculate_distance_using_height(landmarks, image_height, user_height_cm):
+    """Calculate distance using the user's known height."""
+    top_head = landmarks[mp_pose.PoseLandmark.NOSE.value].y * image_height
+    bottom_foot = max(
+        landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].y,
+        landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value].y
+    ) * image_height
+    
+    person_height_px = abs(bottom_foot - top_head)
+    
+    # Using the formula: distance = (actual_height_cm * focal_length) / height_in_pixels
+    distance = (user_height_cm * FOCAL_LENGTH) / person_height_px
+    
+    # Calculate more accurate scale_factor based on known height
+    scale_factor = user_height_cm / person_height_px
+    
+    return distance, scale_factor
+
+def get_body_width_at_height(frame, height_px, center_x):
+    """Scan horizontally at a specific height to find body edges."""
+    # Convert to grayscale and apply threshold
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    blur = cv2.GaussianBlur(gray, (5, 5), 0)
+    _, thresh = cv2.threshold(blur, 50, 255, cv2.THRESH_BINARY)
+    
+    # Ensure height_px is within image bounds
+    if height_px >= frame.shape[0]:
+        height_px = frame.shape[0] - 1
+    
+    # Get horizontal line at the specified height
+    horizontal_line = thresh[height_px, :]
+    
+    # Find left and right edges starting from center
+    center_x = int(center_x * frame.shape[1])
+    left_edge, right_edge = center_x, center_x
+    
+    # Scan from center to left
+    for i in range(center_x, 0, -1):
+        if horizontal_line[i] == 0:  # Found edge (black pixel)
+            left_edge = i
+            break
+    
+    # Scan from center to right
+    for i in range(center_x, len(horizontal_line)):
+        if horizontal_line[i] == 0:  # Found edge (black pixel)
+            right_edge = i
+            break
+            
+    width_px = right_edge - left_edge
+    
+    # If width is unreasonably small, apply a minimum width
+    min_width = 0.1 * frame.shape[1]  # Minimum width as 10% of image width
+    if width_px < min_width:
+        width_px = min_width
+        
+    return width_px
+
+def calculate_measurements(results, scale_factor, image_width, image_height, depth_map, frame=None, user_height_cm=None):
+    landmarks = results.pose_landmarks.landmark
+
+    # If user's height is provided, use it to get a more accurate scale factor
+    if user_height_cm:
+        _, scale_factor = calculate_distance_using_height(landmarks, image_height, user_height_cm)
+
+    def pixel_to_cm(value):
+        return round(value * scale_factor, 2)
+    
+    def calculate_circumference(width_px, depth_ratio=1.0):
+        """Estimate circumference using width and depth adjustment."""
+        # Using a simplified elliptical approximation: C ≈ 2π * sqrt((a² + b²)/2)
+        # where a is half the width and b is estimated depth
+        width_cm = width_px * scale_factor
+        estimated_depth_cm = width_cm * depth_ratio * 0.7  # Depth is typically ~70% of width for torso
+        half_width = width_cm / 2
+        half_depth = estimated_depth_cm / 2
+        return round(2 * np.pi * np.sqrt((half_width**2 + half_depth**2) / 2), 2)
+
+    measurements = {}
+
+    # Shoulder Width
+    left_shoulder = landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value]
+    right_shoulder = landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value]
+    shoulder_width_px = abs(left_shoulder.x * image_width - right_shoulder.x * image_width)
+    
+    # Apply a slight correction factor for shoulders (they're usually detected well)
+    shoulder_correction = 1.1  # 10% wider
+    shoulder_width_px *= shoulder_correction
+    
+    measurements["shoulder_width"] = pixel_to_cm(shoulder_width_px)
+
+    # Chest/Bust Measurement
+    chest_y_ratio = 0.15  # Approximately 15% down from shoulder to hip
+    chest_y = left_shoulder.y + (landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].y - left_shoulder.y) * chest_y_ratio
+    
+    chest_correction = 1.15  # 15% wider than detected width
+    chest_width_px = abs((right_shoulder.x - left_shoulder.x) * image_width) * chest_correction
+    
+    if frame is not None:
+        chest_y_px = int(chest_y * image_height)
+        center_x = (left_shoulder.x + right_shoulder.x) / 2
+        detected_width = get_body_width_at_height(frame, chest_y_px, center_x)
+        if detected_width > 0:
+            chest_width_px = max(chest_width_px, detected_width)
+    
+    chest_depth_ratio = 1.0
+    if depth_map is not None:
+        chest_x = int(((left_shoulder.x + right_shoulder.x) / 2) * image_width)
+        chest_y_px = int(chest_y * image_height)
+        scale_y = 384 / image_height
+        scale_x = 384 / image_width
+        chest_y_scaled = int(chest_y_px * scale_y)
+        chest_x_scaled = int(chest_x * scale_x)
+        if 0 <= chest_y_scaled < 384 and 0 <= chest_x_scaled < 384:
+            chest_depth = depth_map[chest_y_scaled, chest_x_scaled]
+            max_depth = np.max(depth_map)
+            chest_depth_ratio = 1.0 + 0.5 * (1.0 - chest_depth / max_depth)
+    
+    measurements["chest_width"] = pixel_to_cm(chest_width_px)
+    measurements["chest_circumference"] = calculate_circumference(chest_width_px, chest_depth_ratio)
+    
+
+    # Waist Measurement
+    left_hip = landmarks[mp_pose.PoseLandmark.LEFT_HIP.value]
+    right_hip = landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value]
+
+    # Adjust waist_y_ratio to better reflect the natural waistline
+    waist_y_ratio = 0.35  # 35% down from shoulder to hip (higher than before)
+    waist_y = left_shoulder.y + (left_hip.y - left_shoulder.y) * waist_y_ratio
+
+    # Use contour detection to dynamically estimate waist width
+    if frame is not None:
+        waist_y_px = int(waist_y * image_height)
+        center_x = (left_hip.x + right_hip.x) / 2
+        detected_width = get_body_width_at_height(frame, waist_y_px, center_x)
+        if detected_width > 0:
+            waist_width_px = detected_width
+        else:
+            # Fallback to hip width if contour detection fails
+            waist_width_px = abs(right_hip.x - left_hip.x) * image_width * 0.9  # 90% of hip width
+    else:
+        # Fallback to hip width if no frame is provided
+        waist_width_px = abs(right_hip.x - left_hip.x) * image_width * 0.9  # 90% of hip width
+
+    # Apply 30% correction factor to waist width
+    waist_correction = 1.16  # 30% wider
+    waist_width_px *= waist_correction
+
+    # Get depth adjustment for waist if available
+    waist_depth_ratio = 1.0
+    if depth_map is not None:
+        waist_x = int(((left_hip.x + right_hip.x) / 2) * image_width)
+        waist_y_px = int(waist_y * image_height)
+        scale_y = 384 / image_height
+        scale_x = 384 / image_width
+        waist_y_scaled = int(waist_y_px * scale_y)
+        waist_x_scaled = int(waist_x * scale_x)
+        if 0 <= waist_y_scaled < 384 and 0 <= waist_x_scaled < 384:
+            waist_depth = depth_map[waist_y_scaled, waist_x_scaled]
+            max_depth = np.max(depth_map)
+            waist_depth_ratio = 1.0 + 0.5 * (1.0 - waist_depth / max_depth)
+
+    measurements["waist_width"] = pixel_to_cm(waist_width_px)
+    measurements["waist"] = calculate_circumference(waist_width_px, waist_depth_ratio)
+    # Hip Measurement
+    hip_correction = 1.35  # Hips are typically 35% wider than detected landmarks
+    hip_width_px = abs(left_hip.x * image_width - right_hip.x * image_width) * hip_correction
+    
+    if frame is not None:
+        hip_y_offset = 0.1  # 10% down from hip landmarks
+        hip_y = left_hip.y + (landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].y - left_hip.y) * hip_y_offset
+        hip_y_px = int(hip_y * image_height)
+        center_x = (left_hip.x + right_hip.x) / 2
+        detected_width = get_body_width_at_height(frame, hip_y_px, center_x)
+        if detected_width > 0:
+            hip_width_px = max(hip_width_px, detected_width)
+    
+    hip_depth_ratio = 1.0
+    if depth_map is not None:
+        hip_x = int(((left_hip.x + right_hip.x) / 2) * image_width)
+        hip_y_px = int(left_hip.y * image_height)
+        hip_y_scaled = int(hip_y_px * scale_y)
+        hip_x_scaled = int(hip_x * scale_x)
+        if 0 <= hip_y_scaled < 384 and 0 <= hip_x_scaled < 384:
+            hip_depth = depth_map[hip_y_scaled, hip_x_scaled]
+            max_depth = np.max(depth_map)
+            hip_depth_ratio = 1.0 + 0.5 * (1.0 - hip_depth / max_depth)
+    
+    measurements["hip_width"] = pixel_to_cm(hip_width_px)
+    measurements["hip"] = calculate_circumference(hip_width_px, hip_depth_ratio)
+
+    # Other measurements (unchanged)
+    neck = landmarks[mp_pose.PoseLandmark.NOSE.value]
+    left_ear = landmarks[mp_pose.PoseLandmark.LEFT_EAR.value]
+    neck_width_px = abs(neck.x * image_width - left_ear.x * image_width) * 2.0
+    measurements["neck"] = calculate_circumference(neck_width_px, 1.0)
+    measurements["neck_width"] = pixel_to_cm(neck_width_px)
+
+    left_wrist = landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value]
+    sleeve_length_px = abs(left_shoulder.y * image_height - left_wrist.y * image_height)
+    measurements["arm_length"] = pixel_to_cm(sleeve_length_px)
+
+    shirt_length_px = abs(left_shoulder.y * image_height - left_hip.y * image_height) * 1.2
+    measurements["shirt_length"] = pixel_to_cm(shirt_length_px)
+
+     # Thigh Circumference (improved with depth information)
+    thigh_y_ratio = 0.2  # 20% down from hip to knee
+    left_knee = landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value]
+    thigh_y = left_hip.y + (left_knee.y - left_hip.y) * thigh_y_ratio
+    
+    # Apply correction factor for thigh width
+    thigh_correction = 1.2  # Thighs are typically wider than what can be estimated from front view
+    thigh_width_px = hip_width_px * 0.5 * thigh_correction  # Base thigh width on hip width
+    
+    # Use contour detection if frame is available
+    if frame is not None:
+        thigh_y_px = int(thigh_y * image_height)
+        thigh_x = left_hip.x * 0.9  # Move slightly inward from hip
+        detected_width = get_body_width_at_height(frame, thigh_y_px, thigh_x)
+        if detected_width > 0 and detected_width < hip_width_px:  # Sanity check
+            thigh_width_px = detected_width  # Use detected width
+    
+    # If depth map is available, use it for thigh measurement
+    thigh_depth_ratio = 1.0
+    if depth_map is not None:
+        thigh_x = int(left_hip.x * image_width)
+        thigh_y_px = int(thigh_y * image_height)
+        
+        # Scale coordinates to match depth map size
+        thigh_y_scaled = int(thigh_y_px * scale_y)
+        thigh_x_scaled = int(thigh_x * scale_x)
+        
+        if 0 <= thigh_y_scaled < 384 and 0 <= thigh_x_scaled < 384:
+            thigh_depth = depth_map[thigh_y_scaled, thigh_x_scaled]
+            max_depth = np.max(depth_map)
+            thigh_depth_ratio = 1.0 + 0.5 * (1.0 - thigh_depth / max_depth)
+    
+    measurements["thigh"] = pixel_to_cm(thigh_width_px)
+    measurements["thigh_circumference"] = calculate_circumference(thigh_width_px, thigh_depth_ratio)
+
+
+    left_ankle = landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value]
+    trouser_length_px = abs(left_hip.y * image_height - left_ankle.y * image_height)
+    measurements["trouser_length"] = pixel_to_cm(trouser_length_px)
+
+    return measurements
+    
+@app.route("/upload_images", methods=["POST"])
+def upload_images():
+    if "front" not in request.files:
+        return jsonify({"error": "Missing front image for reference."}), 400
+    
+    # Get user height if provided, otherwise use default
+    user_height_cm = request.form.get('height_cm')
+    print(user_height_cm)
+    if user_height_cm:
+        try:
+            user_height_cm = float(user_height_cm)
+        except ValueError:
+            user_height_cm = DEFAULT_HEIGHT_CM
+    else:
+        user_height_cm = DEFAULT_HEIGHT_CM
+    
+    received_images = {pose_name: request.files[pose_name] for pose_name in ["front", "left_side", "right_side", "back"] if pose_name in request.files}
+    measurements, scale_factor, focal_length, results = {}, None, FOCAL_LENGTH, {}
+    frames = {}
+    
+    for pose_name, image_file in received_images.items():
+        image_np = np.frombuffer(image_file.read(), np.uint8)
+        frame = cv2.imdecode(image_np, cv2.IMREAD_COLOR)
+        frames[pose_name] = frame  # Store the frame for contour detection
+        rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        results[pose_name] = holistic.process(rgb_frame)
+        image_height, image_width, _ = frame.shape
+        
+        if pose_name == "front":
+            # Always use height for calibration (default or provided)
+            if results[pose_name].pose_landmarks:
+                _, scale_factor = calculate_distance_using_height(
+                    results[pose_name].pose_landmarks.landmark,
+                    image_height,
+                    user_height_cm
+                )
+            else:
+                # Fallback to object detection only if pose landmarks aren't detected
+                scale_factor, focal_length = detect_reference_object(frame)
+        
+        depth_map = estimate_depth(frame) if pose_name in ["front", "left_side"] else None
+        
+        if results[pose_name].pose_landmarks:
+            if pose_name == "front":
+                measurements.update(calculate_measurements(
+                    results[pose_name], 
+                    scale_factor, 
+                    image_width, 
+                    image_height, 
+                    depth_map,
+                    frames[pose_name],  # Pass the frame for contour detection
+                    user_height_cm
+                ))
+    
+    # Debug information to help troubleshoot measurements
+    debug_info = {
+        "scale_factor": float(scale_factor) if scale_factor else None,
+        "focal_length": float(focal_length),
+        "user_height_cm": float(user_height_cm)
+    }
+    
+    return jsonify({ 
+        "measurements": measurements,
+        "debug_info": debug_info
+    })
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=8000)