My commit

README.md

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+# Body Measurement & Segmentation using YOLO + Mediapipe
+
+This project detects a person from an image, segments the body, extracts pose keypoints using Mediapipe, and generates measurements such as height, shoulder width, and hip width. The script outputs segmented images with edge detection and overlay results inside an output folder.
+
+---
+
+## Features
+
+- Person segmentation using YOLO
+- Pose keypoints detection using Mediapipe
+- Calculates body measurements (height_px, shoulder, hip)
+- Saves output images automatically:
+  - `segmented.png`
+  - `edges.png`
+  - `overlay.png`
+- No command line args needed (image path inside script)
+
+---
+
+## Project Requirements
+
+**Python Version Required: `3.10+`**
+
+Install project dependencies:
+
+```bash
+uv pip install ultralytics mediapipe==0.10.11 opencv-python numpy

app/AI_Scripts/body_segment.py

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+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 = r.masks.data[0].cpu().numpy()
+        mask = cv2.resize(mask, (img.shape[1], img.shape[0]))
+        mask = (mask * 255).astype("uint8")
+
+        mask3 = cv2.merge([mask, mask, mask])
+        segmented = cv2.bitwise_and(img, mask3)
+        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/projects/body_measurement/app/images/unnamed2.jpg"            #  input image path dalen
+    height_cm = 170                   # Optional (human actual height)
+    output_folder = "Image_output"   # Output save directory
+
+    BodyMeasurement().run(img_path, height_cm, output_folder)

main.py

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+def main():
+    print("Hello from body-measurement!")
+
+
+if __name__ == "__main__":
+    main()

pyproject.toml

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+[project]
+name = "body-measurement"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.8"
+dependencies = []

requirements.txt

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+opencv-python
+mediapipe==0.10.11
+numpy
+ultralytics

yolo11s-seg.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1caa81c0195412efa411b632bcfb8c184939dddb6ae41f6a80c41b211ff257c3
+size 20669228