import mediapipe as mp import math from typing import List, Mapping, Optional, Tuple, Union import cv2 import dataclasses import numpy as np from mediapipe.framework.formats import landmark_pb2 import os mp_holistic = mp.solutions.holistic _PRESENCE_THRESHOLD = 0.5 _VISIBILITY_THRESHOLD = 0.5 _BGR_CHANNELS = 3 WHITE_COLOR = (224, 224, 224) BLACK_COLOR = (0, 0, 0) RED_COLOR = (0, 0, 255) GREEN_COLOR = (0, 128, 0) BLUE_COLOR = (255, 0, 0) @dataclasses.dataclass class DrawingSpec: """ 定义标注框的线性、色彩 """ # Color for drawing the annotation. Default to the white color. color: Tuple[int, int, int] = GREEN_COLOR # Thickness for drawing the annotation. Default to 2 pixels. thickness: int = 2 # Circle radius. Default to 2 pixels. circle_radius: int = 2 def _normalized_to_pixel_coordinates( normalized_x: float, normalized_y: float, image_width: int, image_height: int) -> Union[None, Tuple[int, int]]: """ 将关键点坐标转化为图像像素坐标 """ # Checks if the float value is between 0 and 1. def is_valid_normalized_value(value: float) -> bool: return (value > 0 or math.isclose(0, value)) and (value < 1 or math.isclose(1, value)) if not (is_valid_normalized_value(normalized_x) and is_valid_normalized_value(normalized_y)): # TODO: Draw coordinates even if it's outside of the image bounds. return None x_px = min(math.floor(normalized_x * image_width), image_width - 1) y_px = min(math.floor(normalized_y * image_height), image_height - 1) # return print("转化的真实坐标：",x_px, y_px) return x_px,y_px def draw_landmarks( image: np.ndarray, landmark_list: landmark_pb2.NormalizedLandmarkList, connections: Optional[List[Tuple[int, int]]] = None, landmark_drawing_spec: Union[DrawingSpec,Mapping[int, DrawingSpec]] = DrawingSpec(color=RED_COLOR), connection_drawing_spec: Union[DrawingSpec, Mapping[Tuple[int, int],DrawingSpec]] = DrawingSpec()): """ 主要是绘制关键点的连接图 image:输入的数据 landmark_list:关键点列表 connections:连接点 """ if not landmark_list: return if image.shape[2] != _BGR_CHANNELS: raise ValueError('Input image must contain three channel bgr data.') image_rows, image_cols, _ = image.shape idx_to_coordinates = {} for idx, landmark in enumerate(landmark_list.landmark): if ((landmark.HasField('visibility') and landmark.visibility < _VISIBILITY_THRESHOLD) or (landmark.HasField('presence') and landmark.presence < _PRESENCE_THRESHOLD)): continue landmark_px = _normalized_to_pixel_coordinates(landmark.x, landmark.y, #将归一化坐标值转换为图像坐标值 image_cols, image_rows) # print('图像像素坐标:',landmark_px) if landmark_px: idx_to_coordinates[idx] = landmark_px # print("这是什么：",idx_to_coordinates[idx]) dot_list = [] if connections: # num_landmarks = len(landmark_list.landmark) #connections:keypoint索引元组的列表，用于指定如何在图形中连接地标。 # Draws the connections if the start and end landmarks are both visible. starts = [] ends = [] for connection in connections: # print("怎样连接的：",connection[0],connection[1]) start_idx = connection[0] end_idx = connection[1] starts.append(start_idx) ends.append(end_idx) """取消注释部分可以绘制关键点连接的图像""" # if not (0 <= start_idx < num_landmarks and 0 <= end_idx < num_landmarks): # raise ValueError(f'Landmark index is out of range. Invalid connection ' # f'from landmark #{start_idx} to landmark #{end_idx}.') # if start_idx in idx_to_coordinates and end_idx in idx_to_coordinates: # drawing_spec = connection_drawing_spec[connection] if isinstance( # connection_drawing_spec, Mapping) else connection_drawing_spec # cv2.line(image, idx_to_coordinates[start_idx], # idx_to_coordinates[end_idx], drawing_spec.color, # drawing_spec.thickness) # print("头节点：",start_list) # print("尾结点：",end_list) for dot in ends: if dot in list(idx_to_coordinates.keys()): # print((idx_to_coordinates.keys())) dot_list.append(idx_to_coordinates[dot]) # if landmark_drawing_spec: # for idx, landmark_px in idx_to_coordinates.items(): # drawing_spec = landmark_drawing_spec[idx] if isinstance( # landmark_drawing_spec, Mapping) else landmark_drawing_spec # # White circle border # circle_border_radius = max(drawing_spec.circle_radius + 1, # int(drawing_spec.circle_radius * 1.2)) # cv2.circle(image, landmark_px, circle_border_radius, WHITE_COLOR, # drawing_spec.thickness) # # Fill color into the circle # cv2.circle(image, landmark_px, drawing_spec.circle_radius, # drawing_spec.color, drawing_spec.thickness) return dot_list class mediapipe_detect: def mediapipe_detection(self,image, model): """ mediapipe检测模块 image：输入数据集 model：调用mediapipe模型检测动作的模块 """ image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # COLOR CONVERSION BGR 2 RGB image.flags.writeable = False # Image is no longer writeable results = model.process(image) # 利用模块预测动作并输出坐标 # image.flags.writeable = True # Image is now writeable # image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) # COLOR COVERSION RGB 2 BGR return image, results def Drawing_bbox(self,result,bias): ''' 根据关键点坐标，获取最大外接矩形的坐标点 result:关键点坐标 b:修正值,增大或者减小矩形框 ''' result = np.array(result) b = bias if result.any(): rect = cv2.boundingRect(result) #返回值, 左上角的坐标[x,y, w,h] bbox = [[rect[0] - b, rect[1] - b], [rect[0] + rect[2] + b, rect[1] - b], [rect[0] - b, rect[1] + rect[3] + b], [rect[0] + rect[2] + b, rect[1] + rect[3] + b]] #四个角的坐标 # print(bbox) return bbox def get_bbox(self,image, results,face_b,left_hand_b,right_hand_b): ''' 主要是根据关键点坐标，绘制矩形框 images: 待检测数据 results: mediapipe检测结果 ''' image.flags.writeable = True image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) """获取头部、手部关键点""" face_location = draw_landmarks( image, results.face_landmarks, mp_holistic.FACEMESH_CONTOURS, # DrawingSpec(color=(80, 110, 10), thickness=1, circle_radius=1), # DrawingSpec(color=(80, 256, 121), thickness=1, circle_radius=1) ) right_hand_location = draw_landmarks( image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS, # DrawingSpec(color=(121, 22, 76), thickness=2, circle_radius=4), # DrawingSpec(color=(121, 44, 250), thickness=2, circle_radius=2) ) left_hand_location = draw_landmarks( image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS, # DrawingSpec(color=(245, 117, 66), thickness=2, circle_radius=4), # DrawingSpec(color=(245, 66, 230), thickness=2, circle_radius=2) ) """根据关键点的坐标绘制最大外接矩形""" fl_bbox = self.Drawing_bbox(face_location,face_b) lh_bbox = self.Drawing_bbox(left_hand_location,left_hand_b) rh_bbox = self.Drawing_bbox(right_hand_location,right_hand_b) # print("fl",fl_bbox) # print("lh", lh_bbox) # print("rh",rh_bbox) """调整头部检测框的大小""" if fl_bbox is not None: """对头部动作检测框微调""" fl_bbox[3][0] = fl_bbox[3][0] - 15 fl_bbox[3][1] = fl_bbox[3][1] - 30 fl_bbox[0][0] = fl_bbox[0][0] + 30 fl_bbox[0][1] = fl_bbox[0][1] + 5 cv2.rectangle(image, fl_bbox[0], fl_bbox[3],DrawingSpec.color, DrawingSpec.thickness) if lh_bbox is not None: cv2.rectangle(image, lh_bbox[0], lh_bbox[3],DrawingSpec.color, DrawingSpec.thickness) if rh_bbox is not None: cv2.rectangle(image, rh_bbox[0], rh_bbox[3],DrawingSpec.color, DrawingSpec.thickness) res = {'face_bbox': fl_bbox, 'hand_bbox': [lh_bbox,rh_bbox]} return image,res def main(input_path,face_b,left_hand_b,right_hand_b): """" 图片检测模块 input_path:检测图片路径 face_b:头部标注框修正值，可根据输入值调整头部标注框的大小 left_hand_b:左手检测标注框... right_hand_b:右手检测标注框... return:返回是关键点坐标框的坐标值 """ image = cv2.imread(input_path) # image_name = os.path.basename(input_path) with mp_holistic.Holistic(model_complexity=2,min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic: image,res = mediapipe_detect().mediapipe_detection(image,holistic) image ,res = mediapipe_detect().get_bbox(image,res,face_b,left_hand_b,right_hand_b) # cv2.imwrite(output+"/"+image_name,image) #取消注释可以保存处理后的图片 cv2.namedWindow("mediapipe_detections", cv2.WINDOW_AUTOSIZE) cv2.imshow("mediapipe_detections", image) cv2.waitKey() cv2.destroyAllWindows() # print("标注框坐标值",res) return res if __name__ == "__main__": input = 'D:/inference/mediapipe/mediapipe/python/video/test_picture/test11.jpg' # output = 'D:/inference/mediapipe/mediapipe/python/video/output' face_b = 50 #头部标注框修正值 left_hand_b = 7 #头部标注框修正值 right_hand_b = 7 #头部标注框修正值 main(input,face_b,left_hand_b,right_hand_b)