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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 ----
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)
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