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69 lines
2.1 KiB
69 lines
2.1 KiB
4 years ago
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import torch
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import numpy as np
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from models.experimental import attempt_load
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from utils.general import non_max_suppression, scale_coords, letterbox
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from utils.torch_utils import select_device
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from utils.BaseDetector import baseDet
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class Detector(baseDet):
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def __init__(self):
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super(Detector, self).__init__()
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self.init_model()
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self.build_config()
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def init_model(self):
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self.weights = 'weights/yolov5m.pt'
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self.device = '0' if torch.cuda.is_available() else 'cpu'
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self.device = select_device(self.device)
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model = attempt_load(self.weights, map_location=self.device)
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model.to(self.device).eval()
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model.half()
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# torch.save(model, 'test.pt')
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self.m = model
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self.names = model.module.names if hasattr(
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model, 'module') else model.names
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def preprocess(self, img):
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img0 = img.copy()
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img = letterbox(img, new_shape=self.img_size)[0]
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img = img[:, :, ::-1].transpose(2, 0, 1)
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img = np.ascontiguousarray(img)
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img = torch.from_numpy(img).to(self.device)
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img = img.half() # 半精度
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img /= 255.0 # 图像归一化
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if img.ndimension() == 3:
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img = img.unsqueeze(0)
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return img0, img
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def detect(self, im):
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im0, img = self.preprocess(im)
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pred = self.m(img, augment=False)[0]
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pred = pred.float()
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pred = non_max_suppression(pred, self.threshold, 0.4)
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pred_boxes = []
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for det in pred:
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if det is not None and len(det):
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det[:, :4] = scale_coords(
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img.shape[2:], det[:, :4], im0.shape).round()
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for *x, conf, cls_id in det:
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lbl = self.names[int(cls_id)]
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if not lbl in ['person', 'car', 'truck']:
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continue
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x1, y1 = int(x[0]), int(x[1])
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x2, y2 = int(x[2]), int(x[3])
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pred_boxes.append(
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(x1, y1, x2, y2, lbl, conf))
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return im, pred_boxes
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