79 lines
1.8 KiB
Python
79 lines
1.8 KiB
Python
import random
|
|
|
|
import numpy as np
|
|
|
|
|
|
def get_square(img, pos):
|
|
"""Extract a left or a right square from PILimg shape : (H, W, C))"""
|
|
img = np.array(img)
|
|
h = img.shape[0]
|
|
if pos == 0:
|
|
return img[:, :h]
|
|
else:
|
|
return img[:, -h:]
|
|
|
|
|
|
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 img
|
|
|
|
|
|
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
|
|
|
|
|
|
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):]
|
|
|
|
return new
|
|
|
|
|
|
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
|