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jblitzar
/
github-python-corpus

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classifier = nn.Sequential(nn.Linear(num_conv_features, 4), nn.Sigmoid())
net.classifier = classifier
net.eval().cuda()
save_path = f.checkpoint_box_trainval_path
if path.exists(save_path):
net.load_state_dict(torch.load(save_path))
print("Model loaded")
else:
raise FileNotFoundError()
self.net = net
def predict_box(self, img_gray):
assert len(img_gray.shape) == 2
assert np.max(img_gray) > 1.1, "expect uint8 image [0, 255]"
img = [[img_gray]] # NCHW
img = np.asarray(img, np.float32)
img_01 = img / 255.0
img_01 = img_01 * np.ones([1, 3, 1, 1], np.float32)
test_imgs_tensor = torch.from_numpy(img_01).cuda()
with torch.no_grad():
pred_box = self.net(test_imgs_tensor) # NCHW
pred_box = pred_box.detach().cpu().numpy()
return pred_box[0]
class TrimMachine():
def __init__(self):
self.box_predictor = Box()
def trim_height(self, img_gray):
# raise NotImplementedError("cobb angle parse will use image height, can't change it now.")
assert len(img_gray.shape) == 2, "h, w"
hw = img_gray.shape
h, w = float(hw[0]), float(hw[1])
# Zoom h to 1120 (set according to training image size)
target_h = 752.
zoom_rate = target_h / h
target_w = w * zoom_rate
zoom_img_gray = cv2.resize(img_gray, dsize=(int(target_w), int(target_h)), interpolation=cv2.INTER_CUBIC)
box = self.box_predictor.predict_box(zoom_img_gray)
_, _, y_min, y_max = box
if y_max < 0.7:
y_max = 0.7
y_top = int(h * y_min)
y_bottom = int(h * y_max + 0.05 * h)
assert y_top < y_bottom
# use zero to fill height
img_gray[:y_top, :] = 0
img_gray[y_bottom:, :] = 0
return img_gray
def trim_width(self, img_gray):
assert len(img_gray.shape) == 2, "h, w"
hw = img_gray.shape
h, w = float(hw[0]), float(hw[1])
target_w = 256.
zoom_rate = target_w / w
target_h = h * zoom_rate
zoom_img_gray = cv2.resize(img_gray, dsize=(int(target_w), int(target_h)), interpolation=cv2.INTER_CUBIC)
box = self.box_predictor.predict_box(zoom_img_gray)
x_min, x_max, _, _ = box
# Center after crop
x_center = (w * x_min + w * x_max) / 2
expected_w = h / 3
x_left = max(0, x_center-(expected_w/2))
x_right = min(w, x_center+(expected_w/2))
crop_img = img_gray[:, int(x_left): int(x_right)]
# x_left = int(w * x_min - 0.02 * h) # Use h, because w becomes wider if original image is wider.
# x_right = int(w * x_max + 0.02 * h)
# assert x_left < x_right
# crop_img = img_gray[:, x_left: x_right]
return crop_img
def trim_width_height(self, img_gray):
assert len(img_gray.shape) == 2, "h, w"
hw = img_gray.shape
h, w = float(hw[0]), float(hw[1])
target_w = 256.
zoom_rate = target_w / w
target_h = h * zoom_rate
zoom_img_gray = cv2.resize(img_gray, dsize=(int(target_w), int(target_h)), interpolation=cv2.INTER_CUBIC)
box = self.box_predictor.predict_box(zoom_img_gray)
x_min, x_max, y_min, y_max = box
x_left = int(w * x_min - 0.03 * w)
x_right = int(w * x_max + 0.03 * w)
assert x_left < x_right
img_gray = img_gray[:, x_left: x_right]
y_top = int(h * y_min)
y_bottom = int(h * y_max + 0.05 * h)
assert y_top < y_bottom
# use zero to fill height