营销型网站五大系统 单仁,上海 网站建设google,企业邮箱怎么搞,广州网络公司排名本篇在将图像输入hrnet识别之前先进行目标检测来确定识别的位置#xff0c;让识别更加精准。 本段代码设置了一个区域框BOX#xff0c;让人走入区域内才开始检测#xff0c;适用于考核等场景#xff0c;也可以直接去掉BOX也是一样的效果。若画面背景中有多个行人#xff0… 本篇在将图像输入hrnet识别之前先进行目标检测来确定识别的位置让识别更加精准。 本段代码设置了一个区域框BOX让人走入区域内才开始检测适用于考核等场景也可以直接去掉BOX也是一样的效果。若画面背景中有多个行人还是只取要检测的那个人同理还是适用考核场景。 为了让检测效果更直观在一些点位直接使用线连接起来模拟人体骨骼。
import os
import sys
import numpy as np
from mmpose.apis import init_model, inference_topdown
import cv2import torch
sys.path.append(/home/yons/train/code/yolov5)
from models.experimental import attempt_load
from utils.general import non_max_suppression, scale_boxes
from torchvision import transforms# 配置文件路径和检查点文件路径
config_file /home/.../pose_td-hm_hrnet-w48_8xb32-210e_PullUp-det/pose_td-hm_hrnet-w48_8xb32-210e_PullUp-det.py
checkpoint_file /home/.../pose_td-hm_hrnet-w48_8xb32-210e_PullUp-det/best_coco_AP_epoch_250.pth# 初始化姿态估计模型
pose_model init_model(config_file, checkpoint_file, devicecuda:0)
yolo_model attempt_load(yolov5x6.pt, devicecuda:0) # 加载训练好的yolov5模型
pose_model.eval()
yolo_model.eval()VIDEO_PATH input.mp4
BOX (300, 50, 300, 450) # 区域框的左上角坐标和宽高
OUTPUT_VIDEO_PATH output.mp4def draw_keypoints(frame, keypoints, box, det_box):# 在帧上绘制关键点# 这里假设关键点是一个 Nx2 的数组其中 N 是关键点的数量# 并且关键点的坐标是相对于裁剪区域的x, y, w, h boxfor kp in keypoints:kp_x, kp_y kpx_rec1 int(det_box[0] x)y_rec1 int(det_box[1] y)x_rec2 int(det_box[2] x)y_rec2 int(det_box[3] y)cv2.rectangle(frame, (x_rec1, y_rec1), (x_rec2, y_rec2), (0, 0, 255), 2)x_cir int(kp_x det_box[0] x)y_cir int(kp_y det_box[1] y)cv2.circle(frame, (x_cir, y_cir), 3, (0, 255, 0), -1)lines [(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)]for line in lines:pt1 (int(keypoints[line[0]][0] det_box[0] x), int(keypoints[line[0]][1] det_box[1] y))pt2 (int(keypoints[line[1]][0] det_box[0] x), int(keypoints[line[1]][1] det_box[1] y))cv2.line(frame, pt1, pt2, (0, 255, 0), 2)def letterbox(im, new_shape(640, 640), color(114, 114, 114), autoTrue, scaleFillFalse, scaleupTrue, stride32):Resizes and pads image to new_shape with stride-multiple constraints, returns resized image, ratio, padding.shape im.shape[:2] # current shape [height, width]if isinstance(new_shape, int):new_shape (new_shape, new_shape)# Scale ratio (new / old)r min(new_shape[0] / shape[0], new_shape[1] / shape[1])if not scaleup: # only scale down, do not scale up (for better val mAP)r min(r, 1.0)# Compute paddingratio r, r # width, height ratiosnew_unpad int(round(shape[1] * r)), int(round(shape[0] * r))dw, dh new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh paddingif auto: # minimum rectangledw, dh np.mod(dw, stride), np.mod(dh, stride) # wh paddingelif scaleFill: # stretchdw, dh 0.0, 0.0new_unpad (new_shape[1], new_shape[0])ratio new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratiosdw / 2 # divide padding into 2 sidesdh / 2if shape[::-1] ! new_unpad: # resizeim cv2.resize(im, new_unpad, interpolationcv2.INTER_LINEAR)print(im.shape)top, bottom int(round(dh - 0.1)), int(round(dh 0.1))left, right int(round(dw - 0.1)), int(round(dw 0.1))im cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, valuecolor) # add borderreturn im, ratio, (dw, dh)# 处理每一张图像
k 0
if __name__ __main__:# 打开视频cap cv2.VideoCapture(VIDEO_PATH)# 获取视频的一些属性fps cap.get(cv2.CAP_PROP_FPS)width int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))height int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))# 创建 VideoWriter 对象fourcc cv2.VideoWriter_fourcc(*mp4v) # 或者使用 XVIDout cv2.VideoWriter(OUTPUT_VIDEO_PATH, fourcc, fps, (width, height))while True:# 读取一帧ret, frame cap.read()if not ret:break# 加载帧x, y, w, h BOXimg0 frame[y:yh, x:xw, :]img_size (1280, 1280)stride max(int(yolo_model.stride.max()), 32)img letterbox(img0, img_size, stridestride, autoTrue)[0] # padded resizeimg img.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGBimg np.ascontiguousarray(img) # contiguous# yolo_model.warmup(imgsz(1, 3, *img_size)) # warmupimg torch.from_numpy(img).to(cuda:0)# img img.half() if yolo_model.fp16 else img.float() # uint8 to fp16/32img img.float()img / 255 # 0 - 255 to 0.0 - 1.0if len(img.shape) 3:img img[None] # expand for batch dimwith torch.no_grad():pred yolo_model(img) # Inferencepred non_max_suppression(pred, 0.25, 0.45, 0) # NMS 0 for person# input()det_box None# Process predictions# print(pred)for i, det in enumerate(pred): # per image# print(det)if len(det):# Rescale boxes from img_size to img0 sizedet[:, :4] scale_boxes(img.shape[2:], det[:, :4], img0.shape).round()max_bb Nonefor x1, y1, x2, y2, conf, cls in reversed(det):if max_bb is None:max_bb [x1, y1, x2, y2]else:if ((x2 - x1) * (y2 - y1)) ((max_bb[2] - max_bb[0]) * (max_bb[3] - max_bb[3])):max_bb [x1, y1, x2, y2]det_box max_bbfor idx in range(len(det_box)):det_box[idx] int(det_box[idx])x1, y1, x2, y2 det_box# print(det_box)img_seg img0[y1:y2, x1:x2, :]person_results np.array([[0, 0, x2-x1, y2-y1]])# 推理得到关键点坐标pose_results inference_topdown(pose_model, img_seg, person_results, bbox_formatxyxy)# 提取关键点坐标并检查是否检测出17个关键点keypoints []if len(pose_results) 0 and pose_results[0].pred_instances.keypoints.shape[1] 17:keypoints pose_results[0].pred_instances.keypoints[0]draw_keypoints(frame, keypoints, BOX, det_box)# 写入帧out.write(frame)# 显示帧cv2.imshow(frame, frame)if cv2.waitKey(1) 0xFF ord(q):breakcap.release()out.release()cv2.destroyAllWindows()input.mp4视频如下 引体向上原始视频 output.mp4视频如下 引体向上推理结果视频 大概原理是区域框内进行一系列处理后输入进yolo进行目标检测在多个目标框内选出我们要检测的人物的目标框输入进hrnet得到关键点关键点从目标框映射回区域框再映射回原图得到最终结果。
