REVA-QCAV/utils/utils.py

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import random
import numpy as np
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def get_square(img, pos):
"""Extract a left or a right square from ndarray shape : (H, W, C))"""
h = img.shape[0]
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if pos == 0:
return img[:, :h]
else:
return img[:, -h:]
def split_img_into_squares(img):
return get_square(img, 0), get_square(img, 1)
def hwc_to_chw(img):
return np.transpose(img, axes=[2, 0, 1])
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def resize_and_crop(pilimg, scale=0.5, final_height=None):
w = pilimg.size[0]
h = pilimg.size[1]
newW = int(w * scale)
newH = int(h * scale)
if not final_height:
diff = 0
else:
diff = newH - final_height
img = pilimg.resize((newW, newH))
img = img.crop((0, diff // 2, newW, newH - diff // 2))
return np.array(img, dtype=np.float32)
def batch(iterable, batch_size):
"""Yields lists by batch"""
b = []
for i, t in enumerate(iterable):
b.append(t)
if (i + 1) % batch_size == 0:
yield b
b = []
if len(b) > 0:
yield b
def split_train_val(dataset, val_percent=0.05):
dataset = list(dataset)
length = len(dataset)
n = int(length * val_percent)
random.shuffle(dataset)
return {'train': dataset[:-n], 'val': dataset[-n:]}
def normalize(x):
return x / 255
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def merge_masks(img1, img2, full_w):
h = img1.shape[0]
new = np.zeros((h, full_w), np.float32)
new[:, :full_w // 2 + 1] = img1[:, :full_w // 2 + 1]
new[:, full_w // 2 + 1:] = img2[:, -(full_w // 2 - 1):]
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return new
# credits to https://stackoverflow.com/users/6076729/manuel-lagunas
def rle_encode(mask_image):
pixels = mask_image.flatten()
# We avoid issues with '1' at the start or end (at the corners of
# the original image) by setting those pixels to '0' explicitly.
# We do not expect these to be non-zero for an accurate mask,
# so this should not harm the score.
pixels[0] = 0
pixels[-1] = 0
runs = np.where(pixels[1:] != pixels[:-1])[0] + 2
runs[1::2] = runs[1::2] - runs[:-1:2]
return runs