yolov5测试单张图片

yolov5测试单张图片，返回一个列表[类别，置信度,x,y,w,h]

from numpy import random
import torch
from models.experimental import attempt_load
from utils.datasets import LoadStreams, LoadImages
from utils.general import (check_img_size, non_max_suppression, apply_classifier, scale_coords,xyxy2xywh, plot_one_box, strip_optimizer, set_logging)
from utils.torch_utils import select_device, load_classifier, time_synchronized
import os
import shutil# Initialize
out = r'inference\output'
set_logging()
device = select_device('')
if os.path.exists(out):shutil.rmtree(out)  # delete output folder
os.makedirs(out)  # make new output folder
half = device.type != 'cpu'  # half precision only supported on CUDA# Load model
model = attempt_load('weights/yolov5s.pt', map_location=device)  # load FP32 model
imgsz = check_img_size(512, s=model.stride.max())  # check img_size
if half:model.half()  # to FP16# Second-stage classifier
classify = False
if classify:modelc = load_classifier(name='resnet101', n=2)  # initializemodelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model'])  # load weightsmodelc.to(device).eval()# Set Dataloader# Get names and colors
names = model.module.names if hasattr(model, 'module') else model.names
colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))]
img = torch.zeros((1, 3, imgsz, imgsz), device=device)  # init img
_ = model(img.half() if half else img) if device.type != 'cpu' else None  # run oncedef PoseDect(path, imgsz=512):res = []dataset = LoadImages(path, img_size=imgsz)for path, img, im0s, vid_cap in dataset:img = torch.from_numpy(img).to(device)img = img.half() if half else img.float()  # uint8 to fp16/32img /= 255.0  # 0 - 255 to 0.0 - 1.0if img.ndimension() == 3:img = img.unsqueeze(0)# Inferencet1 = time_synchronized()pred = model(img, augment=False)[0]# Apply NMSpred = non_max_suppression(pred, 0.4, 0.5, classes=None, agnostic=False)t2 = time_synchronized()# Apply Classifierif classify:pred = apply_classifier(pred, modelc, img, im0s)# Process detectionsfor i, det in enumerate(pred):  # detections per imagegn = torch.tensor(im0s.shape)[[1, 0, 1, 0]]  # normalization gain whwhif det is not None and len(det):# Rescale boxes from img_size to im0 sizedet[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0s.shape).round()for *xyxy, conf, cls in reversed(det):x, y, w, h = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()res.append([names[int(cls)], float(conf), x, y, w, h])return res# for _, img, im0s, _ in dataset:##     img = torch.from_numpy(img).to(device)#     img = img.half() if half else 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 = model(img, augment=False)[0]#     # Apply NMS#     pred = non_max_suppression(pred, 0.4, .05, classes=None, agnostic=None)##     for i, det in enumerate(pred):  # detections per image#         p, s, im0 = path[i], '%g: ' % i, im0s[i].copy()##         gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh#         if det is not None and len(det):#             # Rescale boxes from img_size to im0 size#             det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()##             # Write results#             for *xyxy, conf, cls in reversed(det):#                 xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh#                 x, y, w, h = xywh#                 if(int(cls)==0):#                     res.append([names[int(cls)], float(conf), x, y, w, h])#                 #res.append([int(cls), float(conf), x, y, w, h])##                 # draw#                 # label = '%s %.2f' % (names[int(cls)], conf)#                 #plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)## return resif __name__ == '__main__':path = r'inference\images\0152498D-225A-4126-AEBE-B6D9423E12E7.png's = PoseDect(path=path)print(s)import cv2img = cv2.imread(r'inference\images\0152498D-225A-4126-AEBE-B6D9423E12E7.png')for box in s:x1, y1, x2, y2 = box[2:]# 映射原图尺寸x = int(x1 * img.shape[1])y = int(y1 * img.shape[0])w = int(x2 * img.shape[1])h = int(y2 * img.shape[0])# 计算出左上角和右下角：原x,y是矩形框的中心点a = int(x - w / 2)b = int(y - h / 2)c = int(x + w / 2)d = int(y + h / 2)print(x1, y1, x1 + x2, y1 + y2)print(x, y, x + w, y + h)print(a, b, c, d)cv2.rectangle(img, (a, b), (c, d), (255, 0, 0), 2)cv2.imshow('dst', img)cv2.waitKey()cv2.destroyAllWindows()

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