import warnings import os from tabulate import tabulate warnings.filterwarnings("ignore", category=UserWarning, module="google.protobuf") os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" import argparse import math import cv2 from mediapipe.python.solutions import pose import logging class Landmarker: resized_height = 256 resized_width = 300 def __init__(self) -> None: args = self.parse_args() if args.front_image is None: raise Exception("Front image needs to be passed") if args.side_image is None: raise Exception("Side image needs to be passed") self.front_image = cv2.imread(args.front_image) self.side_image = cv2.imread(args.side_image) self.front_image_resized = cv2.resize( self.front_image, (self.resized_height, self.resized_width)) self.side_image_resized = cv2.resize( self.side_image, (self.resized_height, self.resized_width)) self.person_height = args.person_height self.pixel_height = args.pixel_height self.pose = pose.Pose( static_image_mode=True, min_detection_confidence=args.pose_detection_confidence, min_tracking_confidence=args.pose_tracking_confidence, ) self.landmarks_indices = [ pose.PoseLandmark.LEFT_SHOULDER.value, pose.PoseLandmark.RIGHT_SHOULDER.value, pose.PoseLandmark.LEFT_ELBOW.value, pose.PoseLandmark.RIGHT_ELBOW.value, pose.PoseLandmark.LEFT_WRIST.value, pose.PoseLandmark.RIGHT_WRIST.value, pose.PoseLandmark.LEFT_HIP.value, pose.PoseLandmark.RIGHT_HIP.value, pose.PoseLandmark.LEFT_KNEE.value, pose.PoseLandmark.RIGHT_KNEE.value, pose.PoseLandmark.LEFT_ANKLE.value, pose.PoseLandmark.RIGHT_ANKLE.value, ] def parse_args(self): parser = argparse.ArgumentParser() parser.add_argument("--front", dest="front_image", type=str, help="Front image") parser.add_argument("--side", dest="side_image", type=str, help="Side image") parser.add_argument( "--pose_detection_confidence", dest="pose_detection_confidence", default=0.5, type=float, help="Confidence score for pose detection", ) parser.add_argument( "--pose_tracking_confidence", dest="pose_tracking_confidence", default=0.5, type=float, help="Confidence score for pose tracking", ) parser.add_argument( "--person_height", default=153, dest="person_height", type=int, help="Person height", ) parser.add_argument( "--pixel_height", dest="pixel_height", type=int, help="Pixel height of person", ) return parser.parse_args() def run(self): logging.warning("Person's height: %s", self.person_height) logging.warning("Person's pixel height: %s", self.pixel_height) front_results, side_results = self.process_images() self.get_center_top_point(front_results) self.calculate_distance_for_landmarks(front_results) self.output() self.display_images() self.pose.close() def process_images(self): front_results = self.pose.process( cv2.cvtColor(self.front_image_resized, cv2.COLOR_BGR2RGB)) side_results = self.pose.process( cv2.cvtColor(self.side_image_resized, cv2.COLOR_BGR2RGB)) self.side_image_keypoints = self.side_image_resized.copy() self.front_image_keypoints = self.front_image_resized.copy() if front_results.pose_landmarks: # type: ignore self.draw_landmarks( self.front_image_keypoints, front_results.pose_landmarks, # type: ignore self.landmarks_indices, ) if side_results.pose_landmarks: # type: ignore self.draw_landmarks( self.side_image_keypoints, side_results.pose_landmarks, # type: ignore self.landmarks_indices, ) return front_results, side_results def calculate_distance_for_landmarks(self, landmarks): real_distance = 0 if len(landmarks) > 1: for i in range(0, len(landmarks) - 1): _current = landmarks[self.landmarks_indices[i]] _next = landmarks[self.landmarks_indices[i + 1]] pixel_distance = self.euclidean_distance( _current.x, _current.y, _next.x, _next.y) real_distance += pixel_distance * self.pixel_to_metric_ratio() logging.warning("Total real distance: %s", real_distance) return real_distance def pixel_to_metric_ratio(self): self.pixel_height = self.pixel_distance * 2 pixel_to_metric_ratio = self.person_height / self.pixel_height logging.warning("Pixel to metric ratio: %s", pixel_to_metric_ratio) return pixel_to_metric_ratio def draw_landmarks(self, image, landmarks, indices): for idx in indices: landmark = landmarks[idx] h, w, _ = image.shape cx, cy = int(landmark.x * w), int(landmark.y * h) self.circle(image, cx, cy) def circle(self, image, cx, cy): return cv2.circle(image, (cx, cy), 2, (255, 0, 0), -1) def output(self): table = [["shoulder", 1], ["neck", 2]] output = tabulate(table, headers=["Measurement", "Value"], tablefmt="plain") print(output) def euclidean_distance(self, x1, y1, x2, y2): distance = math.sqrt((x2 - x1)**2 + (y2 - y1)**2) return distance def destroy(self): cv2.destroyAllWindows() def display_images(self): cv2.imshow("front_image_keypoints", self.front_image_keypoints) cv2.imshow("side_image_keypoints", self.side_image_keypoints) cv2.imshow("edges", self.edges) cv2.waitKey(0) def get_center_top_point(self, side_results): gray_image = cv2.cvtColor(self.side_image_keypoints, cv2.COLOR_BGR2GRAY) blurred_image = cv2.GaussianBlur(gray_image, (5, 5), 0) roi = blurred_image[0:int(self.side_image_resized.shape[0] / 2), :] self.edges = cv2.Canny(roi, 50, 150) contours, _ = cv2.findContours(self.edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) xt, yt = None, None self.topmost_point = None if contours: largest_contour = max(contours, key=cv2.contourArea) self.topmost_point = tuple( largest_contour[largest_contour[:, :, 1].argmin()][0]) xt, yt = self.topmost_point self.circle(self.side_image_keypoints, xt, yt) logging.warning("xt: %s", xt) logging.warning("yt: %s", yt) xc, yc = None, None landmarks = side_results.pose_landmarks.landmark if side_results.pose_landmarks: # type: ignore left_hip = landmarks[pose.PoseLandmark.LEFT_HIP.value] right_hip = landmarks[pose.PoseLandmark.RIGHT_HIP.value] center_point = ( (left_hip.x + right_hip.x) / 2, (left_hip.y + right_hip.y) / 2, ) center_point = ( int(center_point[0] * self.side_image_resized.shape[1]), int(center_point[1] * self.side_image_resized.shape[0]), ) xc, yc = center_point logging.warning("xc: %s", xc) logging.warning("yc: %s", yc) self.circle(self.side_image_keypoints, xc, yc) self.pixel_distance = self.euclidean_distance(xc, xt, yc, yt) self.distance = self.pixel_distance * self.pixel_to_metric_ratio() return self.distance l = Landmarker() try: l.run() except Exception as e: print(f"Error: {e}") finally: l.destroy()