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https://github.com/Laurent2916/REVA-QCAV.git
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66 lines
1.6 KiB
Python
66 lines
1.6 KiB
Python
import random
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import numpy as np
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def hwc_to_chw(img):
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return np.transpose(img, axes=[2, 0, 1])
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def resize_and_crop(pilimg, scale=0.5, final_height=None):
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w = pilimg.size[0]
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h = pilimg.size[1]
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newW = int(w * scale)
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newH = int(h * scale)
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if not final_height:
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diff = 0
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else:
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diff = newH - final_height
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img = pilimg.resize((newW, newH))
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img = img.crop((0, diff // 2, newW, newH - diff // 2))
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ar = np.array(img, dtype=np.float32)
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if len(ar.shape) == 2:
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# for greyscale images, add a new axis
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ar = np.expand_dims(ar, axis=2)
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return ar
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def batch(iterable, batch_size):
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"""Yields lists by batch"""
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b = []
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for i, t in enumerate(iterable):
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b.append(t)
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if (i + 1) % batch_size == 0:
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yield b
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b = []
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if len(b) > 0:
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yield b
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def split_train_val(dataset, val_percent=0.05):
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dataset = list(dataset)
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length = len(dataset)
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n = int(length * val_percent)
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random.shuffle(dataset)
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return {'train': dataset[:-n], 'val': dataset[-n:]}
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def normalize(x):
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return x / 255
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# credits to https://stackoverflow.com/users/6076729/manuel-lagunas
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def rle_encode(mask_image):
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pixels = mask_image.flatten()
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# We avoid issues with '1' at the start or end (at the corners of
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# the original image) by setting those pixels to '0' explicitly.
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# We do not expect these to be non-zero for an accurate mask,
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# so this should not harm the score.
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pixels[0] = 0
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pixels[-1] = 0
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runs = np.where(pixels[1:] != pixels[:-1])[0] + 2
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runs[1::2] = runs[1::2] - runs[:-1:2]
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return runs
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