import cv2
import mediapipe as mp
import numpy as np
import math
import os
from ultralytics import YOLO

class BodyMeasurement:
    def __init__(self, model_path="yolo11s-seg.pt"):
        self.model = YOLO(model_path)
        self.pose = mp.solutions.pose

    # ---------------- PERSON SEGMENTATION -----------------
    def segment(self, img):
        results = self.model(img, verbose=False)
        r = results[0]

        if r.masks is None:
            print("❌ No person detected!")
            return img

        # ---- mask ----
        mask = r.masks.data[0].cpu().numpy()
        mask = cv2.resize(mask, (img.shape[1], img.shape[0]))
        mask = (mask * 255).astype("uint8")

        # ---- segmented image ----
        mask3 = cv2.merge([mask, mask, mask])
        segmented = cv2.bitwise_and(img, mask3)

        # ---- FIND CONTOURS (boundary) ----
        contours, _ = cv2.findContours(
            mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
        )

        # ---- DRAW GREEN BOUNDARY ----
        cv2.drawContours(
            segmented,
            contours,
            -1,
            (0, 255, 0),   # green color
            3              # thickness
        )

        return segmented


    # ---------------- EDGE DETECTION -----------------
    def edges(self, rgb):
        gray = cv2.cvtColor(rgb, cv2.COLOR_RGB2GRAY)
        gray = cv2.GaussianBlur(gray, (5,5), 0)
        edges = cv2.Canny(gray,50,150)
        return cv2.cvtColor(edges, cv2.COLOR_GRAY2RGB)

    # ---------------- KEYPOINTS -----------------
    def keypoints(self,rgb):
        h,w,_ = rgb.shape
        pts = {}
        with self.pose.Pose(static_image_mode=True) as p:
            res = p.process(rgb)
            if not res.pose_landmarks: return pts
            for i,lm in enumerate(res.pose_landmarks.landmark):
                x,y = int(lm.x*w), int(lm.y*h)
                pts[self.pose.PoseLandmark(i).name] = (x,y)
        return pts

    # ---------------- MEASUREMENTS -----------------
    def dist(self,a,b): return math.dist(a,b)

    def measure(self,pts,img_h,height_cm=None):
        M={}; G=lambda k: pts.get(k)
        LS,RS = G("LEFT_SHOULDER"), G("RIGHT_SHOULDER")
        LH,RH = G("LEFT_HIP"), G("RIGHT_HIP")
        LA,RA = G("LEFT_ANKLE"), G("RIGHT_ANKLE")
        nose = G("NOSE")

        if LS and RS: M["shoulder_px"] = self.dist(LS,RS)
        if LH and RH: M["hip_px"] = self.dist(LH,RH)

        if nose and LA and RA:
            mid=((LA[0]+RA[0])//2,(LA[1]+RA[1])//2)
            height_px=self.dist(nose,mid)
        else:
            height_px=img_h

        M["height_px"]=height_px

        if height_cm:
            scale=height_cm/height_px
            M["scale_cm/px"]=scale
            if "shoulder_px" in M: M["shoulder_cm"]=M["shoulder_px"] * scale
            if "hip_px" in M: M["hip_cm"]=M["hip_px"] * scale

        return M

    # ---------------- PIPELINE -----------------
    def run(self, img_path, height_cm=None, output_folder="results"):
        if not os.path.exists(output_folder):
            os.makedirs(output_folder)

        img=cv2.imread(img_path)
        if img is None: 
            raise FileNotFoundError("Image not found!")

        seg=self.segment(img)
        rgb=cv2.cvtColor(seg, cv2.COLOR_BGR2RGB)
        edge=self.edges(rgb)
        pts=self.keypoints(rgb)
        M=self.measure(pts, rgb.shape[0], height_cm)

        overlay=cv2.addWeighted(rgb,0.7,edge,0.3,0)
        for x,y in pts.values():
            cv2.circle(overlay,(x,y),3,(0,255,0),-1)

        cv2.imwrite(f"{output_folder}/segmented.png", seg)
        cv2.imwrite(f"{output_folder}/edges.png", cv2.cvtColor(edge, cv2.COLOR_RGB2BGR))
        cv2.imwrite(f"{output_folder}/overlay.png", cv2.cvtColor(overlay, cv2.COLOR_RGB2BGR))

        print("\n Output Saved In Folder:", output_folder)
        print("Files:")
        print(" - segmented.png\n - edges.png\n - overlay.png")

        print("\n Measurements:")
        for k,v in M.items(): print(k,":",round(v,2))

        return M


# ---------------- RUN WITHOUT CLI -----------------
if __name__=="__main__":
    img_path = "/home/abhishek/Desktop/body_measurement /body_measurement/app/images/unnamed.jpg"            #  input image path dalen
    height_cm = 180                   # Optional (human actual height)
    output_folder = "Image_output"   # Output save directory

    BodyMeasurement().run(img_path, height_cm, output_folder)