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import numpy as np
def random_crop(img, w, h):
height, width = img.shape[:2]
h_rnd = height - h
w_rnd = width - w
y = np.random.randint(0, h_rnd) if h_rnd > 0 else 0
x = np.random.randint(0, w_rnd) if w_rnd > 0 else 0
return img[y:y + height, x:x + width]
def normalize_channels(img, target_channels):
img_shape_len = len(img.shape)
if img_shape_len == 2:
h, w = img.shape
c = 0
elif img_shape_len == 3:
h, w, c = img.shape
else:
raise ValueError("normalize: incorrect image dimensions.")
if c == 0 and target_channels > 0:
img = img[..., np.newaxis]
c = 1
if c == 1 and target_channels > 1:
img = np.repeat(img, target_channels, -1)
c = target_channels
if c > target_channels:
img = img[..., 0:target_channels]
c = target_channels
return img
def cut_odd_image(img):
h, w, c = img.shape
wm, hm = w % 2, h % 2
if wm + hm != 0:
img = img[0:h - hm, 0:w - wm, :]
return img
def overlay_alpha_image(img_target, img_source, xy_offset=(0, 0)):
(h, w, c) = img_source.shape
if c != 4:
raise ValueError("overlay_alpha_image, img_source must have 4 channels")
x1, x2 = xy_offset[0], xy_offset[0] + w
y1, y2 = xy_offset[1], xy_offset[1] + h
alpha_s = img_source[:, :, 3] / 255.0
alpha_l = 1.0 - alpha_s
for c in range(0, 3):
img_target[y1:y2, x1:x2, c] = (alpha_s * img_source[:, :, c] +
alpha_l * img_target[y1:y2, x1:x2, c])
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