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68 lines
2.1 KiB
68 lines
2.1 KiB
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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