一、态估体关前言
由于工程项目中需要对视频中的计HR检键点person进行关键点检测，我测试各个算法后，测视并没有采用比较应用化成熟的频中Openpose，决定采用检测精度更高的态估体关HRnet系列。但是计HR检键点由于官方给的算法只能测试数据集，需要自己根据算法模型编写实例化代码。测视
本文根据SimDR工程实现视频关键点检测。频中SimDR根据HRnet改进而来，态估体关整个工程既包括HRnet又包括改进后的计HR检键点算法，使用起来较为方便，测视而且本文仅在cpu上就可以跑通整个工程。频中
二、态估体关环境配置
python的计HR检键点环境主要就是按照工程中SimDR与yolov5的requirement.txt安装即可。总之缺啥装啥。测视
三、工程准备
1、克隆工程
git clone https://github.com/leeyegy/SimDR.git #克隆姿态估计工程cd SimDRgit clone -b v5.0 https://github.com/ultralytics/yolov5.git #在姿态估计工程中添加yolov5算法2、目标检测
①添加权重文件
添加yolov5x.pt（见评论区网盘）到‘ SimDR/yolov5/weights/ ’文件夹下。
②获取边界框
在yolov5文件夹下新建YOLOv5.py，复制以下内容到文件中。注意：根据大家的反馈，不同的电脑，导入yolov5相关包时会不同的方式，代码中我是from yolov5.xxx import xxx，但是有些可以不用前面的yolov5，大家自行尝试哈。一般出现No module xxx 都是有关yolov5 的包导入出错哈。
import argparseimport timefrom pathlib import Pathimport numpy as npimport cv2import torchimport torch.backends.cudnn as cudnnfrom numpy import randomimport sysimport osfrom yolov5.models.experimental import attempt_loadfrom yolov5.utils.datasets import LoadStreams, LoadImagesfrom yolov5.utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \ scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_pathfrom yolov5.utils.plots import plot_one_boxfrom yolov5.utils.torch_utils import select_device, load_classifier, time_synchronizedfrom yolov5.utils.datasets import letterboxclass Yolov5(): def __init__(self, weights=None, opt=None, device=None): """ @param weights: @param save_txt: @param opt: @param device: """ self.weights = weights self.device = device # save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run # save_dir.mkdir(parents=True, exist_ok=True) # make dir self.img_size = 640 self.model = attempt_load(weights, map_location=self.device) self.stride = int(self.model.stride.max()) self.names = self.model.module.names if hasattr(self.model, 'module') else self.model.names self.colors = [[random.randint(0, 255) for _ in range(3)] for _ in self.names] self.opt = opt def detect(self,img0): """ @param img0: 输入图片 shape=[h,w,3] @return: """ person_boxes = np.ones((6)) img = letterbox(img0, self.img_size, stride=self.stride)[0] # Convert img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 img = np.ascontiguousarray(img) img = torch.from_numpy(img).to(self.device) img = img.float() # uint8 to fp16/32 img /= 255.0 # 0 - 255 to 0.0 - 1.0 if img.ndimension() == 3: img = img.unsqueeze(0) pred = self.model(img, augment=self.opt.augment)[0] # Apply NMS pred = non_max_suppression(pred, self.opt.conf_thres, self.opt.iou_thres, classes=self.opt.classes, agnostic=self.opt.agnostic_nms) for i, det in enumerate(pred): if len(det): # Rescale boxes from img_size to im0 size det[:, :4] = scale_coords(img.shape[2:], det[:, :4], img0.shape).round() boxes = reversed(det) boxes = boxes.cpu().numpy() #2022.04.06修改，在GPU上跑boxes无法直接转numpy数据 #for i , box in enumerate(np.array(boxes)): for i , box in enumerate(boxes): if int(box[-1]) == 0 and box[-2]>=0.7: person_boxes=np.vstack((person_boxes , box)) # label = f'{ self.names[int(box[-1])]} { box[-2]:.2f}' # print(label) # plot_one_box(box, img0, label=label, color=self.colors[int(box[-1])], line_thickness=3) # cv2.imwrite('result1.jpg',img0) # print(s) # print(person_boxes,np.ndim(person_boxes)) if np.ndim(person_boxes)>=2 : person_boxes_result = person_boxes[1:] boxes_result = person_boxes[1:,:4] else: person_boxes_result = [] boxes_result = [] return person_boxes_result,boxes_resultdef yolov5test(opt,path = ''): detector = Yolov5(weights='weights/yolov5x.pt',opt=opt,device=torch.device('cpu')) img0 = cv2.imread(path) personboxes ,boxes= detector.detect(img0) for i,(x1,y1,x2,y2) in enumerate(boxes): print(x1,y1,x2,y2) print(personboxes,'\n',boxes)if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold') parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS') parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3') parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS') parser.add_argument('--augment', action='store_true', help='augmented inference') parser.add_argument('--update', action='store_true', help='update all model') parser.add_argument('--project', default='runs/detect', help='save results to project/name') parser.add_argument('--name', default='exp', help='save results to project/name') parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') opt = parser.parse_args() print(opt) # check_requirements(exclude=('pycocotools', 'thop')) with torch.no_grad(): yolov5test(opt,'data/images/zidane.jpg')③路径问题
本文代码是在pycharm中运行，yolov5工程的加入导致有些文件夹名称相同，pycharm会搞混，可能会出现某些包找不到。这里需要先运行一下YOLOv5.py脚本，根据报错改一下import的内容。举个例子，./SimDR/yolov5/models/experimental.py 文件中会出现图片中的问题
改成如下即可，其他的文件改法相同。
④添加SPPF模块
yolov5 v5.0工程中没有SPPF模块，此时我们需要在./SimDR/yolov5/models/common.py文件末尾加入以下代码。
import warningsclass SPPF(nn.Module): # Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13)) super().__init__() c_ = c1 // 2 # hidden channels self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(c_ * 4, c2, 1, 1) self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2) def forward(self, x): x = self.cv1(x) with warnings.catch_warnings(): warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning y1 = self.m(x) y2 = self.m(y1) return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1))3、姿态估计
①添加权重
在SimDR文件夹下新建weight/hrnet文件夹，添加pose_hrnet_w48_384x288.pth等文件（见评论区网盘）
②修改yaml文件
SimDR/experiments/文件夹下是coco与mpii数据集的配置文件，本文以coco为例。
接下来，修改./SimDR/experiments/coco/hrnet/heatmap/w48_384x288_adam_lr1e-3.yaml文件中的TEST部分的MODEL_FILE路径，如图所示。（SimDR算法的配置文件同理改动。）
③获取关键点
在’ SimDR/ ‘文件夹下新建Point_detect.py ,复制以下内容到文件中。
注意：代码第12行的路径要改成自己yolov5工程的路径，有这条代码才能正常运行。
【2022.04.16更新：根据评论区的建议，为关键点增加置信度值，这个值我是根据模型输出经过softmax后取最大值（关键点坐标就是这个最大值的索引），仅供参考。根据这个置信度可以解决半身照也会绘制全部点的问题。】
import cv2import numpy as npimport torchfrom torchvision.transforms import transformsimport torch.nn.functional as Ffrom lib.config import cfgfrom yolov5.YOLOv5 import Yolov5from lib.utils.transforms import flip_back_simdr,transform_preds,get_affine_transformfrom lib import modelsimport argparseimport syssys.path.insert(0, 'D:\\Study\\Pose Estimation\\SimDR\\yolov5')class Points(): def __init__(self, model_name='sa-simdr', resolution=(384,288), opt=None, yolo_weights_path="./yolov5/weights/yolov5x.pt", ): """ Initializes a new SimpleHRNet object. HRNet (and YOLOv3) are initialized on the torch.device("device") and its (their) pre-trained weights will be loaded from disk. Args: c (int): number of channels (when using HRNet model) or resnet size (when using PoseResNet model). nof_joints (int): number of joints. checkpoint_path (str): path to an official hrnet checkpoint or a checkpoint obtained with `train_coco.py`. model_name (str): model name (HRNet or PoseResNet). Valid names for HRNet are: `HRNet`, `hrnet` Valid names for PoseResNet are: `PoseResNet`, `poseresnet`, `ResNet`, `resnet` Default: "HRNet" resolution (tuple): hrnet input resolution - format: (height, width). Default: (384, 288) interpolation (int): opencv interpolation algorithm. Default: cv2.INTER_CUBIC multiperson (bool): if True, multiperson detection will be enabled. This requires the use of a people detector (like YOLOv3). Default: True return_heatmaps (bool): if True, heatmaps will be returned along with poses by self.predict. Default: False return_bounding_boxes (bool): if True, bounding boxes will be returned along with poses by self.predict. Default: False max_batch_size (int): maximum batch size used in hrnet inference. Useless without multiperson=True. Default: 16 yolo_model_def (str): path to yolo model definition file. Default: "./model/detectors/yolo/config/yolov3.cfg" yolo_class_path (str): path to yolo class definition file. Default: "./model/detectors/yolo/data/coco.names" yolo_weights_path (str): path to yolo pretrained weights file. Default: "./model/detectors/yolo/weights/yolov3.weights.cfg" device (:class:`torch.device`): the hrnet (and yolo) inference will be run on this device. Default: torch.device("cpu") """ self.model_name = model_name self.resolution = resolution # in the form (height, width) as in the original implementation self.aspect_ratio = resolution[1]/resolution[0] self.yolo_weights_path = yolo_weights_path self.flip_pairs = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14], [15, 16]] self.device = torch.device(opt.device) cfg.defrost() if model_name in ('sa-simdr','sasimdr','sa_simdr'): if resolution ==(384,288): cfg.merge_from_file('./experiments/coco/hrnet/sa_simdr/w48_384x288_adam_lr1e-3_split1_5_sigma4.yaml') elif resolution == (256,192): cfg.merge_from_file('./experiments/coco/hrnet/sa_simdr/w48_256x192_adam_lr1e-3_split2_sigma4.yaml') else: raise ValueError('Wrong cfg file') elif model_name in ('simdr'): if resolution == (256, 192): cfg.merge_from_file('./experiments/coco/hrnet/simdr/nmt_w48_256x192_adam_lr1e-3.yaml') else: raise ValueError('Wrong cfg file') elif model_name in ('hrnet','HRnet','Hrnet'): if resolution == (384,288): cfg.merge_from_file('./experiments/coco/hrnet/heatmap/w48_384x288_adam_lr1e-3.yaml') elif resolution == (256,192): cfg.merge_from_file('./experiments/coco/hrnet/heatmap/w48_256x192_adam_lr1e-3.yaml') else: raise ValueError('Wrong cfg file') else: raise ValueError('Wrong model name.') cfg.freeze() self.model = eval('models.' + cfg.MODEL.NAME + '.get_pose_net')( cfg, is_train=False) print('=>loading model from { }'.format(cfg.TEST.MODEL_FILE)) checkpoint_path = cfg.TEST.MODEL_FILE checkpoint = torch.load(checkpoint_path, map_location=self.device) if 'model' in checkpoint: self.model.load_state_dict(checkpoint['model']) else: self.model.load_state_dict(checkpoint) if 'cuda' in str(self.device): print("device: 'cuda' - ", end="") if 'cuda' == str(self.device): # if device is set to 'cuda', all available GPUs will be used print("%d GPU(s) will be used" % torch.cuda.device_count()) device_ids = None else: # if device is set to 'cuda:IDS', only that/those device(s) will be used print("GPU(s) '%s' will be used" % str(self.device)) device_ids = [int(x) for x in str(self.device)[5:].split(',')] elif 'cpu' == str(self.device): print("device: 'cpu'") else: raise ValueError('Wrong device name.') self.model = self.model.to(self.device) self.model.eval() self.detector = Yolov5( weights=yolo_weights_path, opt=opt , device=self.device) self.transform = transforms.Compose([ transforms.ToPILImage(), transforms.Resize((self.resolution[0], self.resolution[1])), # (height, width) transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) def _box2cs(self, box): x, y, w, h = box[:4] return self._xywh2cs(x, y, w, h) def _xywh2cs(self, x, y, w, h): center = np.zeros((2), dtype=np.float32) center[0] = x + w * 0.5 center[1] = y + h * 0.5 if w >self.aspect_ratio * h: h = w * 1.0 / self.aspect_ratio elif w < self.aspect_ratio * h: w = h * self.aspect_ratio scale = np.array( [w * 1.0 / 200, h * 1.0 / 200], dtype=np.float32) if center[0] != -1: scale = scale * 1.25 return center, scale def predict(self, image): """ Predicts the human pose on a single image or a stack of n images. Args: image (:class:`np.ndarray`): the image(s) on which the human pose will be estimated. image is expected to be in the opencv format. image can be: - a single image with shape=(height, width, BGR color channel) - a stack of n images with shape=(n, height, width, BGR color channel) Returns: :class:`np.ndarray` or list: a numpy array containing human joints for each (detected) person. Format: if image is a single image: shape=(# of people, # of joints (nof_joints), 3); dtype=(np.float32). if image is a stack of n images: list of n np.ndarrays with shape=(# of people, # of joints (nof_joints), 3); dtype=(np.float32). Each joint has 3 values: (y position, x position, joint confidence). If self.return_heatmaps, the class returns a list with (heatmaps, human joints) If self.return_bounding_boxes, the class returns a list with (bounding boxes, human joints) If self.return_heatmaps and self.return_bounding_boxes, the class returns a list with (heatmaps, bounding boxes, human joints) """ if len(image.shape) == 3: return self._predict_single(image) else: raise ValueError('Wrong image format.') def sa_simdr_pts(self,img,detection,images,boxes): c, s = [], [] if detection is not None: for i, (x1, y1, x2, y2) in enumerate(detection): x1 = int(round(x1.item())) x2 = int(round(x2.item())) y1 = int(round(y1.item())) y2 = int(round(y2.item())) boxes[i] = [x1, y1, x2, y2] w, h = x2 - x1, y2 - y1 xx1 = np.max((0, x1)) yy1 = np.max((0, y1)) xx2 = np.min((img.shape[1] - 1, x1 + np.max((0, w - 1)))) yy2 = np.min((img.shape[0] - 1, y1 + np.max((0, h - 1)))) box = [xx1, yy1, xx2 - xx1, yy2 - yy1] center, scale = self._box2cs(box) c.append(center) s.append(scale) trans = get_affine_transform(center, scale, 0, np.array(cfg.MODEL.IMAGE_SIZE)) input = cv2.warpAffine( img, trans, (int(self.resolution[1]), int(self.resolution[0])), flags=cv2.INTER_LINEAR) images[i] = self.transform(input) if images.shape[0] >0: images = images.to(self.device) with torch.no_grad(): output_x, output_y = self.model(images) if cfg.TEST.FLIP_TEST: input_flipped = images.flip(3) output_x_flipped_, output_y_flipped_ = self.model(input_flipped) output_x_flipped = flip_back_simdr(output_x_flipped_.cpu().numpy(), self.flip_pairs, type='x') output_y_flipped = flip_back_simdr(output_y_flipped_.cpu().numpy(), self.flip_pairs, type='y') output_x_flipped = torch.from_numpy(output_x_flipped.copy()).to(self.device) output_y_flipped = torch.from_numpy(output_y_flipped.copy()).to(self.device) # feature is not aligned, shift flipped heatmap for higher accuracy if cfg.TEST.SHIFT_HEATMAP: output_x_flipped[:, :, 0:-1] = \ output_x_flipped.clone()[:, :, 1:] output_x = F.softmax((output_x + output_x_flipped) * 0.5, dim=2) output_y = F.softmax((output_y + output_y_flipped) * 0.5, dim=2) else: output_x = F.softmax(output_x, dim=2) output_y = F.softmax(output_y, dim=2) max_val_x, preds_x = output_x.max(2, keepdim=True) max_val_y, preds_y = output_y.max(2, keepdim=True) mask = max_val_x >max_val_y max_val_x[mask] = max_val_y[mask] maxvals = max_val_x * 10.0 output = torch.ones([images.size(0), preds_x.size(1), 3]) output[:, :, 0] = torch.squeeze(torch.true_divide(preds_x, cfg.MODEL.SIMDR_SPLIT_RATIO)) output[:, :, 1] = torch.squeeze(torch.true_divide(preds_y, cfg.MODEL.SIMDR_SPLIT_RATIO)) # output[:, :, 2] = maxvals.squeeze(2) output = output.cpu().numpy() preds = output.copy() for i in range(output.shape[0]): preds[i] = transform_preds( output[i], c[i], s[i], [cfg.MODEL.IMAGE_SIZE[0], cfg.MODEL.IMAGE_SIZE[1]] ) preds[:, :, 2] = maxvals.squeeze(2) else: preds = np.empty((0, 0, 3), dtype=np.float32) return preds def simdr_pts(self,img,detection,images,boxes): c, s = [], [] if detection is not None: for i, (x1, y1, x2, y2) in enumerate(detection): x1 = int(round(x1.item())) x2 = int(round(x2.item())) y1 = int(round(y1.item())) y2 = int(round(y2.item())) boxes[i] = [x1, y1, x2, y2] w, h = x2 - x1, y2 - y1 xx1 = np.max((0, x1)) yy1 = np.max((0, y1)) xx2 = np.min((img.shape[1] - 1, x1 + np.max((0, w - 1)))) yy2 = np.min((img.shape[0] - 1, y1 + np.max((0, h - 1)))) box = [xx1, yy1, xx2 - xx1, yy2 - yy1] center, scale = self._box2cs(box) c.append(center) s.append(scale) trans = get_affine_transform(center, scale, 0, np.array(cfg.MODEL.IMAGE_SIZE)) input = cv2.warpAffine( img, trans, (int(self.resolution[1]), int(self.resolution[0])), flags=cv2.INTER_LINEAR) images[i] = self.transform(input) if images.shape[0] >0: images = images.to(self.device) with torch.no_grad(): output_x, output_y = self.model(images) if cfg.TEST.FLIP_TEST: input_flipped = images.flip(3) output_x_flipped_, output_y_flipped_ = self.model(input_flipped) output_x_flipped = flip_back_simdr(output_x_flipped_.cpu().numpy(), self.flip_pairs, type='x') output_y_flipped = flip_back_simdr(output_y_flipped_.cpu().numpy(), self.flip_pairs, type='y') output_x_flipped = torch.from_numpy(output_x_flipped.copy()).to(self.device) output_y_flipped = torch.from_numpy(output_y_flipped.copy()).to(self.device) # feature is not aligned, shift flipped heatmap for higher accuracy if cfg.TEST.SHIFT_HEATMAP: output_x_flipped[:, :, 0:-1] = \ output_x_flipped.clone()[:, :, 1:] output_x = (F.softmax(output_x, dim=2) + F.softmax(output_x_flipped, dim=2)) * 0.5 output_y = (F.softmax(output_y, dim=2) + F.softmax(output_y_flipped, dim=2)) * 0.5 else: output_x = F.softmax(output_x, dim=2) output_y = F.softmax(output_y, dim=2) max_val_x, preds_x = output_x.max(2, keepdim=True) max_val_y, preds_y = output_y.max(2, keepdim=True) mask = max_val_x >max_val_y max_val_x[mask] = max_val_y[mask] maxvals = max_val_x * 10.0 output = torch.ones([images.size(0), preds_x.size(1), 3]) output[:, :, 0] = torch.squeeze(torch.true_divide(preds_x, cfg.MODEL.SIMDR_SPLIT_RATIO)) output[:, :, 1] = torch.squeeze(torch.true_divide(preds_y, cfg.MODEL.SIMDR_SPLIT_RATIO)) output = output.cpu().numpy() preds = output.copy() for i in range(output.shape[0]): preds[i] = transform_preds( output[i], c[i], s[i], [cfg.MODEL.IMAGE_SIZE[0], cfg.MODEL.IMAGE_SIZE[1]] ) preds[:, :, 2] = maxvals.squeeze(2) else: preds = np.empty((0, 0, 3), dtype=np.float32) return preds def hrnet_pts(self,img,detection,images,boxes): if detection is not None: for i, (x1, y1, x2, y2) in enumerate(detection): x1 = int(round(x1.item())) x2 = int(round(x2.item())) y1 = int(round(y1.item())) y2 = int(round(y2.item())) # Adapt detections to match HRNet input aspect ratio (as suggested by xtyDoge in issue #14) correction_factor = self.resolution[0] / self.resolution[1] * (x2 - x1) / (y2 - y1) if correction_factor >1: # increase y side center = y1 + (y2 - y1) // 2 length = int(round((y2 - y1) * correction_factor)) y1 = max(0, center - length // 2) y2 = min(img.shape[0], center + length // 2) elif correction_factor < 1: # increase x side center = x1 + (x2 - x1) // 2 length = int(round((x2 - x1) * 1 / correction_factor)) x1 = max(0, center - length // 2) x2 = min(img.shape[1], center + length // 2) boxes[i] = [x1, y1, x2, y2] images[i] = self.transform(img[y1:y2, x1:x2, ::-1]) if images.shape[0] >0: images = images.to(self.device) with torch.no_grad(): out = self.model(images) out = out.detach().cpu().numpy() pts = np.empty((out.shape[0], out.shape[1], 3), dtype=np.float32) # For each human, for each joint: y, x, confidence for i, human in enumerate(out): for j, joint in enumerate(human): pt = np.unravel_index(np.argmax(joint), (self.resolution[0] // 4, self.resolution[1] // 4)) # 0: pt_x / (height // 4) * (bb_y2 - bb_y1) + bb_y1 # 1: pt_y / (width // 4) * (bb_x2 - bb_x1) + bb_x1 # 2: confidences pts[i, j, 0] = pt[1] * 1. / (self.resolution[1] // 4) * (boxes[i][2] - boxes[i][0]) + boxes[i][0] pts[i, j, 1] = pt[0] * 1. / (self.resolution[0] // 4) * (boxes[i][3] - boxes[i][1]) + boxes[i][1] pts[i, j, 2] = joint[pt] else: pts = np.empty((0, 0, 3), dtype=np.float32) return pts def _predict_single(self, image): _,detections = self.detector.detect(image) nof_people = len(detections) if detections is not None else 0 boxes = np.empty((nof_people, 4), dtype=np.int32) images = torch.empty((nof_people, 3, self.resolution[0], self.resolution[1])) # (height, width) if self.model_name in ('sa-simdr','sasimdr'): pts=self.sa_simdr_pts(image,detections,images,boxes) elif self.model_name in ('hrnet','HRnet','hrnet'): pts = self.hrnet_pts(image, detections, images, boxes) elif self.model_name in ('simdr'): pts = self.simdr_pts(image, detections, images, boxes) return pts # c,s=[],[] # if detections is not None: # for i, (x1, y1, x2, y2) in enumerate(detections): # x1 = int(round(x1.item())) # x2 = int(round(x2.item())) # y1 = int(round(y1.item())) # y2 = int(round(y2.item())) # boxes[i] = [x1,y1,x2,y2] # w ,h= x2-x1,y2-y1 # xx1 = np.max((0, x1)) # yy1 = np.max((0, y1)) # xx2 = np.min((image.shape[1] - 1, x1 + np.max((0, w - 1)))) # yy2 = np.min((image.shape[0] - 1, y1 + np.max((0, h - 1)))) # box = [xx1, yy1, xx2-xx1, yy2-yy1] # center,scale = self._box2cs(box) # c.append(center) # s.append(scale) # # trans = get_affine_transform(center, scale, 0, np.array(cfg.MODEL.IMAGE_SIZE)) # input = cv2.warpAffine( # image, # trans, # (int(self.resolution[1]), int(self.resolution[0])), # flags=cv2.INTER_LINEAR) # images[i] = self.transform(input) # if images.shape[0] >0: # images = images.to(self.device) # with torch.no_grad(): # output_x,output_y = self.model(images) # if cfg.TEST.FLIP_TEST: # input_flipped = images.flip(3) # output_x_flipped_, output_y_flipped_ = self.model(input_flipped) # output_x_flipped = flip_back_simdr(output_x_flipped_.cpu().numpy(), # self.flip_pairs, type='x') # output_y_flipped = flip_back_simdr(output_y_flipped_.cpu().numpy(), # self.flip_pairs, type='y') # output_x_flipped = torch.from_numpy(output_x_flipped.copy()).to(self.device) # output_y_flipped = torch.from_numpy(output_y_flipped.copy()).to(self.device) # # # feature is not aligned, shift flipped heatmap for higher accuracy # if cfg.TEST.SHIFT_HEATMAP: # output_x_flipped[:, :, 0:-1] = \ # output_x_flipped.clone()[:, :, 1:] # output_x = F.softmax((output_x + output_x_flipped) * 0.5, dim=2) # output_y = F.softmax((output_y + output_y_flipped) * 0.5, dim=2) # else: # output_x = F.softmax(output_x, dim=2) # output_y = F.softmax(output_y, dim=2) # max_val_x, preds_x = output_x.max(2, keepdim=True) # max_val_y, preds_y = output_y.max(2, keepdim=True) # # mask = max_val_x >max_val_y # max_val_x[mask] = max_val_y[mask] # maxvals = max_val_x.cpu().numpy() # # output = torch.ones([images.size(0), preds_x.size(1), 2]) # output[:, :, 0] = torch.squeeze(torch.true_divide(preds_x, cfg.MODEL.SIMDR_SPLIT_RATIO)) # output[:, :, 1] = torch.squeeze(torch.true_divide(preds_y, cfg.MODEL.SIMDR_SPLIT_RATIO)) # # output = output.cpu().numpy() # preds = output.copy() # for i in range(output.shape[0]): # preds[i] = transform_preds( # output[i], c[i], s[i], [cfg.MODEL.IMAGE_SIZE[0], cfg.MODEL.IMAGE_SIZE[1]] # ) # else: # preds = np.empty((0, 0, 2), dtype=np.float32) # return preds# parser = argparse.ArgumentParser()# parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')# parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')# parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')# parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')# parser.add_argument('--augment', action='store_true', help='augmented inference')# parser.add_argument('--update', action='store_true', help='update all model')# parser.add_argument('--project', default='runs/detect', help='save results to project/name')# parser.add_argument('--name', default='exp', help='save results to project/name')# parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')# opt = parser.parse_args()# model = Points(model_name='hrnet',opt=opt)# img0 = cv2.imread('./data/test1.jpg')# pts = model.predict(img0)# print(pts.shape)# for point in pts[0]:# image = cv2.circle(img0, (int(point[0]), int(point[1])), 3, [255,0,255], -1 , lineType= cv2.LINE_AA)# cv2.imwrite('./data/test11_result.jpg',image)④绘制骨骼关键点
根据以上步骤，我们已经得到了关键点的坐标值，接下来需要在图片中描绘出来，以便展示检测结果。
首先在’ ./SimDR/lib/utils/ ‘文件夹下新建visualization.py文件，将以下内容复制到文件中。骨架绘制代码结合了simple-hrnet与Openpose工程。
【2022.04.16更新：由于之前的绘制代码被我魔改过，现在恢复成所有点与骨骼都绘制的模样，但是总觉得好丑，没有openpose那种美观，如果有人绘制出比较美观的骨架，希望能分享一下哈，共同进步！】
import cv2import matplotlib.pyplot as pltimport numpy as npimport torchimport torchvisionimport ffmpegimport randomimport mathimport copydef plot_one_box(x, img, color=None, label=None, line_thickness=3): # Plots one bounding box on image img tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness color = color or [random.randint(0, 255) for _ in range(3)] c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3])) cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA) if label: tf = max(tl - 1, 1) # font thickness t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3 cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA) return imgdef joints_dict(): joints = { "coco": { "keypoints": { 0: "nose", 1: "left_eye", 2: "right_eye", 3: "left_ear", 4: "right_ear", 5: "left_shoulder", 6: "right_shoulder", 7: "left_elbow", 8: "right_elbow", 9: "left_wrist", 10: "right_wrist", 11: "left_hip", 12: "right_hip", 13: "left_knee", 14: "right_knee", 15: "left_ankle", 16: "right_ankle" }, "skeleton": [ # # [16, 14], [14, 12], [17, 15], [15, 13], [12, 13], [6, 12], [7, 13], [6, 7], [6, 8], # # [7, 9], [8, 10], [9, 11], [2, 3], [1, 2], [1, 3], [2, 4], [3, 5], [4, 6], [5, 7] # [15, 13], [13, 11], [16, 14], [14, 12], [11, 12], [5, 11], [6, 12], [5, 6], [5, 7], # [6, 8], [7, 9], [8, 10], [1, 2], [0, 1], [0, 2], [1, 3], [2, 4], [3, 5], [4, 6] [15, 13], [13, 11], [16, 14], [14, 12], [11, 12], [5, 11], [6, 12], [5, 6], [5, 7], [6, 8], [7, 9], [8, 10], [1, 2], [0, 1], [0, 2], [1, 3], [2, 4], # [3, 5], [4, 6] [0, 5], [0, 6] # [15, 13], [13, 11], [16, 14], [14, 12], [11, 12], [5, 11], [6, 12], [5, 6], [5, 7], # [6, 8], [7, 9], [8, 10], [0, 3], [0, 4], [1, 3], [2, 4], # [3, 5], [4, 6] # [0, 5], [0, 6] ] }, "mpii": { "keypoints": { 0: "right_ankle", 1: "right_knee", 2: "right_hip", 3: "left_hip", 4: "left_knee", 5: "left_ankle", 6: "pelvis", 7: "thorax", 8: "upper_neck", 9: "head top", 10: "right_wrist", 11: "right_elbow", 12: "right_shoulder", 13: "left_shoulder", 14: "left_elbow", 15: "left_wrist" }, "skeleton": [ # [5, 4], [4, 3], [0, 1], [1, 2], [3, 2], [13, 3], [12, 2], [13, 12], [13, 14], # [12, 11], [14, 15], [11, 10], # [2, 3], [1, 2], [1, 3], [2, 4], [3, 5], [4, 6], [5, 7] [5, 4], [4, 3], [0, 1], [1, 2], [3, 2], [3, 6], [2, 6], [6, 7], [7, 8], [8, 9], [13, 7], [12, 7], [13, 14], [12, 11], [14, 15], [11, 10], ] }, } return jointsdef draw_points(image, points, color_palette='tab20', palette_samples=16, confidence_threshold=0.1,color=None): """ Draws `points` on `image`. Args: image: image in opencv format points: list of points to be drawn. Shape: (nof_points, 3) Format: each point should contain (y, x, confidence) color_palette: name of a matplotlib color palette Default: 'tab20' palette_samples: number of different colors sampled from the `color_palette` Default: 16 confidence_threshold: only points with a confidence higher than this threshold will be drawn. Range: [0, 1] Default: 0.1 Returns: A new image with overlaid points """ circle_size = max(2, int(np.sqrt(np.max(np.max(points, axis=0) - np.min(points, axis=0)) // 16))) for i, pt in enumerate(points): if pt[2] >= confidence_threshold: image = cv2.circle(image, (int(pt[0]), int(pt[1])), circle_size, color[i] ,-1, lineType= cv2.LINE_AA) return imagedef draw_skeleton(image, points, skeleton, color_palette='Set2', palette_samples=8, person_index=0, confidence_threshold=0.1,sk_color=None): """ Draws a `skeleton` on `image`. Args: image: image in opencv format points: list of points to be drawn. Shape: (nof_points, 3) Format: each point should contain (y, x, confidence) skeleton: list of joints to be drawn Shape: (nof_joints, 2) Format: each joint should contain (point_a, point_b) where `point_a` and `point_b` are an index in `points` color_palette: name of a matplotlib color palette Default: 'Set2' palette_samples: number of different colors sampled from the `color_palette` Default: 8 person_index: index of the person in `image` Default: 0 confidence_threshold: only points with a confidence higher than this threshold will be drawn. Range: [0, 1] Default: 0.1 Returns: A new image with overlaid joints """ canvas = copy.deepcopy(image) cur_canvas = canvas.copy() for i, joint in enumerate(skeleton): pt1, pt2 = points[joint] if pt1[2] >= confidence_threshold and pt2[2]>= confidence_threshold : length = ((pt1[0] - pt2[0]) ** 2 + (pt1[1] - pt2[1]) ** 2) ** 0.5 angle = math.degrees(math.atan2(pt1[1] - pt2[1],pt1[0] - pt2[0])) polygon = cv2.ellipse2Poly((int(np.mean((pt1[0],pt2[0]))), int(np.mean((pt1[1],pt2[1])))), (int(length / 2), 2), int(angle), 0, 360, 1) cv2.fillConvexPoly(cur_canvas, polygon, sk_color[i],lineType=cv2.LINE_AA) # cv2.fillConvexPoly(cur_canvas, polygon, sk_color,lineType=cv2.LINE_AA) canvas = cv2.addWeighted(canvas, 0.4, cur_canvas, 0.6, 0) return canvasdef draw_points_and_skeleton(image, points, skeleton, points_color_palette='tab20', points_palette_samples=16, skeleton_color_palette='Set2', skeleton_palette_samples=8, person_index=0, confidence_threshold=0.1,color=None,sk_color=None): """ Draws `points` and `skeleton` on `image`. Args: image: image in opencv format points: list of points to be drawn. Shape: (nof_points, 3) Format: each point should contain (y, x, confidence) skeleton: list of joints to be drawn Shape: (nof_joints, 2) Format: each joint should contain (point_a, point_b) where `point_a` and `point_b` are an index in `points` points_color_palette: name of a matplotlib color palette Default: 'tab20' points_palette_samples: number of different colors sampled from the `color_palette` Default: 16 skeleton_color_palette: name of a matplotlib color palette Default: 'Set2' skeleton_palette_samples: number of different colors sampled from the `color_palette` Default: 8 person_index: index of the person in `image` Default: 0 confidence_threshold: only points with a confidence higher than this threshold will be drawn. Range: [0, 1] Default: 0.1 Returns: A new image with overlaid joints """ colors1 = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], [0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85], [255, 0, 85]] image = draw_skeleton(image, points, skeleton, color_palette=skeleton_color_palette, palette_samples=skeleton_palette_samples, person_index=person_index, confidence_threshold=confidence_threshold,sk_color=colors1) image = draw_points(image, points, color_palette=points_color_palette, palette_samples=points_palette_samples, confidence_threshold=confidence_threshold,color=colors1) return imagedef save_images(images, target, joint_target, output, joint_output, joint_visibility, summary_writer=None, step=0, prefix=''): """ Creates a grid of images with gt joints and a grid with predicted joints. This is a basic function for debugging purposes only. If summary_writer is not None, the grid will be written in that SummaryWriter with name "{ prefix}_images" and "{ prefix}_predictions". Args: images (torch.Tensor): a tensor of images with shape (batch x channels x height x width). target (torch.Tensor): a tensor of gt heatmaps with shape (batch x channels x height x width). joint_target (torch.Tensor): a tensor of gt joints with shape (batch x joints x 2). output (torch.Tensor): a tensor of predicted heatmaps with shape (batch x channels x height x width). joint_output (torch.Tensor): a tensor of predicted joints with shape (batch x joints x 2). joint_visibility (torch.Tensor): a tensor of joint visibility with shape (batch x joints). summary_writer (tb.SummaryWriter): a SummaryWriter where write the grids. Default: None step (int): summary_writer step. Default: 0 prefix (str): summary_writer name prefix. Default: "" Returns: A pair of images which are built from torchvision.utils.make_grid """ # Input images with gt images_ok = images.detach().clone() images_ok[:, 0].mul_(0.229).add_(0.485) images_ok[:, 1].mul_(0.224).add_(0.456) images_ok[:, 2].mul_(0.225).add_(0.406) for i in range(images.shape[0]): joints = joint_target[i] * 4. joints_vis = joint_visibility[i] for joint, joint_vis in zip(joints, joints_vis): if joint_vis[0]: a = int(joint[1].item()) b = int(joint[0].item()) # images_ok[i][:, a-1:a+1, b-1:b+1] = torch.tensor([1, 0, 0]) images_ok[i][0, a - 1:a + 1, b - 1:b + 1] = 1 images_ok[i][1:, a - 1:a + 1, b - 1:b + 1] = 0 grid_gt = torchvision.utils.make_grid(images_ok, nrow=int(images_ok.shape[0] ** 0.5), padding=2, normalize=False) if summary_writer is not None: summary_writer.add_image(prefix + 'images', grid_gt, global_step=step) # Input images with prediction images_ok = images.detach().clone() images_ok[:, 0].mul_(0.229).add_(0.485) images_ok[:, 1].mul_(0.224).add_(0.456) images_ok[:, 2].mul_(0.225).add_(0.406) for i in range(images.shape[0]): joints = joint_output[i] * 4. joints_vis = joint_visibility[i] for joint, joint_vis in zip(joints, joints_vis): if joint_vis[0]: a = int(joint[1].item()) b = int(joint[0].item()) # images_ok[i][:, a-1:a+1, b-1:b+1] = torch.tensor([1, 0, 0]) images_ok[i][0, a - 1:a + 1, b - 1:b + 1] = 1 images_ok[i][1:, a - 1:a + 1, b - 1:b + 1] = 0 grid_pred = torchvision.utils.make_grid(images_ok, nrow=int(images_ok.shape[0] ** 0.5), padding=2, normalize=False) if summary_writer is not None: summary_writer.add_image(prefix + 'predictions', grid_pred, global_step=step) # Heatmaps # ToDo # for h in range(0,17): # heatmap = torchvision.utils.make_grid(output[h].detach(), nrow=int(np.sqrt(output.shape[0])), # padding=2, normalize=True, range=(0, 1)) # summary_writer.add_image('train_heatmap_%d' % h, heatmap, global_step=step + epoch*len_dl_train) return grid_gt, grid_preddef check_video_rotation(filename): # thanks to # https://stackoverflow.com/questions/53097092/frame-from-video-is-upside-down-after-extracting/55747773#55747773 # this returns meta-data of the video file in form of a dictionary meta_dict = ffmpeg.probe(filename) # from the dictionary, meta_dict['streams'][0]['tags']['rotate'] is the key # we are looking for rotation_code = None try: if int(meta_dict['streams'][0]['tags']['rotate']) == 90: rotation_code = cv2.ROTATE_90_CLOCKWISE elif int(meta_dict['streams'][0]['tags']['rotate']) == 180: rotation_code = cv2.ROTATE_180 elif int(meta_dict['streams'][0]['tags']['rotate']) == 270: rotation_code = cv2.ROTATE_90_COUNTERCLOCKWISE else: raise ValueError except KeyError: pass return rotation_code4、测试算法
①主程序
在SimDR文件夹下新建main.py ,复制以下代码到文件中，修改parser参数source的默认值，运行代码。
import argparseimport timeimport osimport cv2 as cvimport numpy as npfrom pathlib import Pathfrom Point_detect import Pointsfrom lib.utils.visualization import draw_points_and_skeleton,joints_dictdef image_detect(opt): skeleton = joints_dict()['coco']['skeleton'] hrnet_model = Points(model_name='hrnet', opt=opt,resolution=(384,288)) #resolution = (384,288) or (256,192) # simdr_model = Points(model_name='simdr', opt=opt,resolution=(256,192)) #resolution = (256,192) # sa_simdr_model = Points(model_name='sa-simdr', opt=opt,resolution=(384,288)) #resolution = (384,288) or (256,192) img0 = cv.imread(opt.source) frame = img0.copy() #predict pred = hrnet_model.predict(img0) # pred = simdr_model.predict(frame) # pred = sa_simdr_model.predict(frame) #vis for i, pt in enumerate(pred): frame = draw_points_and_skeleton(frame, pt, skeleton) #save cv.imwrite('test_result.jpg', frame)def video_detect(opt): hrnet_model = Points(model_name='hrnet', opt=opt, resolution=(384, 288)) # resolution = (384,288) or (256,192) # simdr_model = Points(model_name='simdr', opt=opt,resolution=(256,192)) #resolution = (256,192) # sa_simdr_model = Points(model_name='sa-simdr', opt=opt,resolution=(384,288)) #resolution = (384,288) or (256,192) skeleton = joints_dict()['coco']['skeleton'] cap = cv.VideoCapture(opt.source) if opt.save_video: fourcc = cv.VideoWriter_fourcc(*'MJPG') out = cv.VideoWriter('data/runs/{ }_out.avi'.format(os.path.basename(opt.source).split('.')[0]), fourcc, 24, (int(cap.get(3)), int(cap.get(4)))) while cap.isOpened(): ret, frame = cap.read() if not ret: break pred = hrnet_model.predict(frame) # pred = simdr_model.predict(frame) # pred = sa_simdr_model.predict(frame) for pt in pred: frame = draw_points_and_skeleton(frame,pt,skeleton) if opt.show: cv.imshow('result', frame) if opt.save_video: out.write(frame) if cv.waitKey(1) == 27: break out.release() cap.release() cv.destroyAllWindows()# video_detect(0)if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--source', type=str, default='./data/images/test1.jpg', help='source') # file/folder, 0 for webcam parser.add_argument('--detect_weight', type=str, default="./yolov5/weights/yolov5x.pt", help='e.g "./yolov5/weights/yolov5x.pt"') parser.add_argument('--save_video', action='store_true', default=False,help='save results to *.avi') parser.add_argument('--show', action='store_true', default=True, help='save results to *.avi') parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold') parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS') parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3') parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS') parser.add_argument('--augment', action='store_true', help='augmented inference') opt = parser.parse_args() image_detect(opt)②结果展示
四、总结
全文较长，主要都是些代码，整个工程从跑数据集到实际检测需要对代码工程有一定的理解，整个项目不难，主要考验类的构造。如果需要整个工程可以私聊我。由于我也是刚入门的萌新，所以代码格式写法或者理论看法有很多错误，欢迎指正，共同进步，如果有帮助欢迎点赞评论，万分感谢。
五、参考内容
1、GitHub - leeyegy/SimDR: PyTorch implementation for: Is 2D Heatmap Representation Even Necessary for Human Pose Estimation? (http://arxiv.org/abs/2107.03332)
2、https://github.com/ultralytics/yolov5
3、GitHub - GreenTeaHua/simple-HRNet: Multi-person Human Pose Estimation with HRNet in Pytorch
