REVA-QCAV/utils.py
milesial 7ea54febec Added CLI for predict, cleaned up code, updated README
Former-commit-id: 77555ccc0925a8fba796ce7e42843d95b6e9dce0
2017-11-30 06:45:19 +01:00

126 lines
2.8 KiB
Python

import PIL
import numpy as np
import random
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.seed(42)
random.shuffle(dataset)
return {'train': dataset[:-n], 'val': dataset[-n:]}
def normalize(x):
return x / 255
def merge_masks(img1, img2, full_w):
w = img1.shape[1]
overlap = int(2 * w - full_w)
h = img1.shape[0]
new = np.zeros((h, full_w), np.float32)
margin = 0
new[:, :full_w//2+1] = img1[:, :full_w//2+1]
new[:, full_w//2+1:] = img2[:, -(full_w//2-1):]
#new[:, w-overlap+1+margin//2:-(w-overlap+margin//2)] = (img1[:, -overlap+margin:] +
# img2[:, :overlap-margin])/2
return new
import matplotlib.pyplot as plt
def encode(mask):
"""mask : HxW"""
plt.imshow(mask.transpose())
plt.show()
flat = mask.transpose().reshape(-1)
enc = []
i = 1
while i <= len(flat):
if(flat[i-1]):
s = i
while(flat[i-1]):
i += 1
e = i-1
enc.append(s)
enc.append(e - s + 1)
i += 1
plt.imshow(decode(enc))
plt.show()
return enc
def decode(list):
mask = np.zeros((1280*1920), np.bool)
for i, e in enumerate(list):
if(i%2 == 0):
mask[e-1:e-2+list[i+1]] = True
mask = mask.reshape(1920, 1280).transpose()
return mask
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
def full_process(filename):
im = PIL.Image.open(filename)
im = resize_and_crop(im)