mirror of
https://github.com/Laurent2916/REVA-QCAV.git
synced 2024-09-19 11:35:28 +00:00
122 lines
2.7 KiB
Python
122 lines
2.7 KiB
Python
import PIL
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import numpy as np
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import random
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def get_square(img, pos):
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"""Extract a left or a right square from PILimg shape : (H, W, C))"""
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img = np.array(img)
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h = img.shape[0]
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if pos == 0:
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return img[:, :h]
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else:
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return img[:, -h:]
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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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return img
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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.seed(42)
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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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def merge_masks(img1, img2, full_w):
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w = img1.shape[1]
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overlap = int(2 * w - full_w)
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h = img1.shape[0]
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new = np.zeros((h, full_w), np.float32)
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margin = 0
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new[:, :full_w//2+1] = img1[:, :full_w//2+1]
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new[:, full_w//2+1:] = img2[:, -(full_w//2-1):]
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#new[:, w-overlap+1+margin//2:-(w-overlap+margin//2)] = (img1[:, -overlap+margin:] +
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# img2[:, :overlap-margin])/2
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return new
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import matplotlib.pyplot as plt
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def encode(mask):
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"""mask : HxW"""
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plt.imshow(mask.transpose())
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plt.show()
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flat = mask.transpose().reshape(-1)
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enc = []
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i = 1
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while i <= len(flat):
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if(flat[i-1]):
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s = i
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while(flat[i-1]):
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i += 1
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e = i-1
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enc.append(s)
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enc.append(e - s + 1)
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i += 1
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plt.imshow(decode(enc))
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plt.show()
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return enc
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def decode(list):
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mask = np.zeros((1280*1920), np.bool)
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for i, e in enumerate(list):
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if(i%2 == 0):
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mask[e-1:e-2+list[i+1]] = True
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mask = mask.reshape(1920, 1280).transpose()
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return mask
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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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