app.py

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
import shutil

# --- Monkey Patch: Override model path before using mediapipe.pose ---
from mediapipe.python.solutions import pose as mp_pose_module

CUSTOM_MODEL_PATH = "/app/models/pose_landmark_heavy.tflite"

def monkey_patch_pose_path(model_complexity):
    model_complexity_map = {
        0: "pose_landmark_lite",
        1: "pose_landmark_full",
        2: "pose_landmark_heavy"
    }
    if model_complexity in model_complexity_map:
        file_name = model_complexity_map[model_complexity] + ".tflite"
        mp_pose_module.MODEL_PATHS[model_complexity] = CUSTOM_MODEL_PATH
    else:
        raise ValueError(f"Unsupported model complexity: {model_complexity}")

# Monkey patch before creating Pose instance
monkey_patch_pose_path(model_complexity=2)
  
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
) # 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)