PPLiteSeg_CARLA.zip

import torch from numpy import random from models.experimental import attempt_load from utils.datasets import MyLoadImages from utils.general import check_img_size, non_max_suppression, apply_classifier, \ scale_coords, set_logging from utils.plots import plot_one_box from utils.torch_utils import select_device, load_classifier class simulation_opt: def __init__(self, weights='models/yolov7.pt', img_size = 640, conf_thres = 0.25, iou_thres = 0.45,device='', view_img= False, classes = None, agnostic_nms = False, augment = False, update = False, exist_ok = False): self.weights = weights self.source = None self.img_size = img_size self.conf_thres = conf_thres self.iou_thres = iou_thres self.device = device self.view_img = view_img self.classes = classes self.agnostic_nms = agnostic_nms self.augment =augment self.update = update self.exist_ok = exist_ok class detectapi: def __init__(self, weights, img_size=640): self.opt = simulation_opt(weights=weights, img_size=img_size) weights, imgsz = self.opt.weights, self.opt.img_size # Initialize set_logging() self.device = select_device(self.opt.device) self.half = self.device.type != 'cpu' # half precision only supported on CUDA # Load model self.model = attempt_load(weights, map_location=self.device) # load FP32 model self.stride = int(self.model.stride.max()) # model stride self.imgsz = check_img_size(imgsz, s=self.stride) # check img_size if self.half: self.model.half() # to FP16 # Second-stage classifier self.classify = False if self.classify: self.modelc = load_classifier(name='resnet101', n=2) # initialize self.modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=self.device)['model']).to(self.device).eval() # read names and colors 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] def detect(self, source): # 使用时，调用这个函数 if type(source) != list: raise TypeError('source must be a list which contain pictures read by cv2') dataset = MyLoadImages(source, img_size=self.imgsz, stride=self.stride)#imgsz # 原来是通过路径加载数据集的，现在source里面就是加载好的图片，所以数据集对象的实现要 # 重写。修改代码后附。在utils.dataset.py上修改。 # Run inference if self.device.type != 'cpu': self.model(torch.zeros(1, 3, self.imgsz, self.imgsz).to(self.device).type_as(next(self.model.parameters()))) # run once #t0 = time.time() result = [] ''' for path, img, im0s, vid_cap in dataset:''' for img, im0s in dataset: img = torch.from_numpy(img).to(self.device) img = img.half() if self.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) # Inference #t1 = time_synchronized() 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) #t2 = time_synchronized() # Apply Classifier if self.classify: pred = apply_classifier(pred, self.modelc, img, im0s) # Print time (inference + NMS) #print(f'{s}Done. ({t2 - t1:.3f}s)') # Process detections det = pred[0] # 原来的情况是要保持图片，因此多了很多关于保持路径上的处理。另外，pred # 其实是个列表。元素个数为batch_size。由于对于我这个api，每次只处理一个图片， # 所以pred中只有一个元素，直接取出来就行，不用for循环。 im0 = im0s.copy() # 这是原图片，与被传进来的图片是同地址的，需要copy一个副本，否则，原来的图片会受到影响 # s += '%gx%g ' % img.shape[2:] # print string # gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh result_txt = [] # 对于一张图片，可能有多个可被检测的目标。所以结果标签也可能有多个。 # 每被检测出一个物体，result_txt的长度就加一。result_txt中的每个元素是个列表，记录着 # 被检测物的类别引索，在图片上的位置，以及置信度 if 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 line = (int(cls.item()), [int(_.item()) for _ in xyxy], conf.item()) # label format result_txt.append(line) label = f'{self.names[int(cls)]} {conf:.2f}' plot_one_box(xyxy, im0, label=label, color=self.colors[int(cls)], line_thickness=3) result.append((im0, result_txt)) # 对于每张图片，返回画完框的图片，以及该图片的标签列表。 return result, self.names