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79 lines
2.4 KiB
79 lines
2.4 KiB
import torch
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import numpy as np
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from yolov5.models.experimental import attempt_load
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from yolov5.utils.datasets import letterbox
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from yolov5.utils.general import non_max_suppression, scale_coords
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from yolov5.utils.torch_utils import select_device
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class Detector:
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def __init__(self, img_size, view_img_size,weights):
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self.img_size = img_size
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self.view_img_size=view_img_size
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self.threshold = 0.3
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self.stride = 1
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self.weights =weights
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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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half = self.device != 'cpu' # half precision only supported on CUDA
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# Load model
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model = torch.load(self.weights, map_location=self.device)[
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'model'].float() # load to FP32
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model.to(self.device).eval()
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if half:
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model.half() # to FP16
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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()
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img /= 255.0
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if img.ndimension() == 3:
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img = img.unsqueeze(0)
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return img
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def detect(self, im):
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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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return img,pred
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# 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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# print(lbl,':'+str(conf))
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# if float(conf)<0.5:
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# continue
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# if lbl not in ['person', 'bicycle', 'car', 'motorcycle', 'bus', 'truck','dog']:
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# continue
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# pass
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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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# boxes.append(
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# (x1, y1, x2, y2, cls_id, conf,lbl))
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# return boxes
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