Bienqq
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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-10-12 01:18:41 +02:00
parent a59350852b
commit 171f25cfc6
2 changed files with 3 additions and 2 deletions
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1
detect.py
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@ -32,6 +32,7 @@ def detect(save_txt=False, save_img=False):
if classify: if classify:
modelc = torch_utils.load_classifier(name='resnet101', n=2) # initialize modelc = torch_utils.load_classifier(name='resnet101', n=2) # initialize
modelc.load_state_dict(torch.load('resnet101.pt', map_location=device)['model']) # load weights modelc.load_state_dict(torch.load('resnet101.pt', map_location=device)['model']) # load weights
modelc.to(device).eval()
# Fuse Conv2d + BatchNorm2d layers # Fuse Conv2d + BatchNorm2d layers
# model.fuse() # model.fuse()

4
utils/utils.py
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@ -730,7 +730,7 @@ def apply_classifier(x, model, img, im0):
# Reshape and pad cutouts # Reshape and pad cutouts
b = xyxy2xywh(d[:, :4]) # boxes b = xyxy2xywh(d[:, :4]) # boxes
b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # rectangle to square b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # rectangle to square
b[:, 2:] = b[:, 2:] * 1.0 + 0 # pad b[:, 2:] = b[:, 2:] * 1.3 + 30 # pad
d[:, :4] = xywh2xyxy(b).long() d[:, :4] = xywh2xyxy(b).long()
# Rescale boxes from img_size to im0 size # Rescale boxes from img_size to im0 size
@ -743,7 +743,7 @@ def apply_classifier(x, model, img, im0):
for a in d: # per item for a in d: # per item
j += 1 j += 1
cutout = im0[int(a[1]):int(a[3]), int(a[0]):int(a[2])] cutout = im0[int(a[1]):int(a[3]), int(a[0]):int(a[2])]
im = cv2.resize(cutout, (128, 128)) # BGR im = cv2.resize(cutout, (224, 224)) # BGR
cv2.imwrite('test%i.jpg' % j, cutout) cv2.imwrite('test%i.jpg' % j, cutout)
im = im[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 im = im[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416