import warnings import os from tabulate import tabulate import math import argparse import cv2 from mediapipe.python.solutions import pose import logging warnings.filterwarnings("ignore", category=UserWarning, module="google.protobuf") os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" class Landmarker: resized_height = 256 resized_width = 300 def __init__(self) -> None: args = self.parse_args() if args.front_image == None: raise Exception("front image needs to be passed") if args.side_image == 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.distances = {} 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_to_calculate = [] 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 of person", ) parser.add_argument( "--pixel_height", # default=216, 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_betn_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# type: ignore self.landmarks_indices, ) return front_results, side_results 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.landmark[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 = [] for landmark, distance in self.distances.items(): table.append([landmark.replace("_", " "), distance]) output = tabulate(table, headers=["measurement", "value"], tablefmt="grid") print(output) def calculate_distance_betn_landmarks(self, front_results, landmarks=[]): if not front_results.pose_landmarks: return landmarks = front_results.pose_landmarks.landmark leg_landmarks = [ pose.PoseLandmark.LEFT_HIP, pose.PoseLandmark.LEFT_KNEE, pose.PoseLandmark.LEFT_ANKLE, ] hand_landmarks = [ pose.PoseLandmark.LEFT_SHOULDER, pose.PoseLandmark.LEFT_ELBOW, pose.PoseLandmark.LEFT_WRIST, ] self.landmarks_to_calculate = leg_landmarks + hand_landmarks # self.landmarks_to_calculate = [ # pose.PoseLandmark.LEFT_SHOULDER, # pose.PoseLandmark.LEFT_ELBOW, # pose.PoseLandmark.LEFT_WRIST, # ] table = [] for idx, l in enumerate(self.landmarks_to_calculate): if idx < len(self.landmarks_to_calculate) - 1: _current = landmarks[l.value] _nextl = self.landmarks_to_calculate[idx + 1] _next = landmarks[_nextl.value] pixel_distance = self.euclidean_distance( _current.x * self.resized_width, _current.y * self.resized_height, _next.x * self.resized_width, _next.y * self.resized_height) real_distance = pixel_distance * self.pixel_to_metric_ratio() table.append([l.name, _nextl.name, real_distance]) output = tabulate( table, headers=["Landmark 1", "Landmark 2", "Distance (cm)"], tablefmt="grid") print(output) # for l in self.landmarks_to_calculate: # real_distance = 0 # for idx, l in enumerate(self.landmarks_to_calculate): # if idx < len(self.landmarks_to_calculate) - 1: # _current = landmarks[l.value] # _nextl = self.landmarks_to_calculate[idx + 1] # _next = landmarks[_nextl.value] # pixel_distance = self.euclidean_distance( # _current.x * self.resized_width, # _current.y * self.resized_height, # _next.x * self.resized_width, # _next.y * self.resized_height, # ) # real_distance += pixel_distance * self.pixel_to_metric_ratio( # ) # print(real_distance) # self.distances[l.name] = real_distance # 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: 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, yc, xt, yt) logging.warning("top_center_pixel_distance: %s", self.pixel_distance) self.pixel_height = self.pixel_distance * 2 logging.warning("pxl height: %s ", self.pixel_height) self.distance = (self.euclidean_distance(xc, yc, xt, yt) * self.pixel_to_metric_ratio()) return self.distance l = Landmarker() try: l.run() except: print("error") finally: l.destroy()