| from PIL import Image, ImageEnhance | |
| import random | |
| import numpy as np | |
| import random | |
| def preproc(image, label, preproc_methods=['flip']): | |
| if 'flip' in preproc_methods: | |
| image, label = cv_random_flip(image, label) | |
| if 'crop' in preproc_methods: | |
| image, label = random_crop(image, label) | |
| if 'rotate' in preproc_methods: | |
| image, label = random_rotate(image, label) | |
| if 'enhance' in preproc_methods: | |
| image = color_enhance(image) | |
| if 'pepper' in preproc_methods: | |
| label = random_pepper(label) | |
| return image, label | |
| def cv_random_flip(img, label): | |
| if random.random() > 0.5: | |
| img = img.transpose(Image.FLIP_LEFT_RIGHT) | |
| label = label.transpose(Image.FLIP_LEFT_RIGHT) | |
| return img, label | |
| def random_crop(image, label): | |
| border = 30 | |
| image_width = image.size[0] | |
| image_height = image.size[1] | |
| border = int(min(image_width, image_height) * 0.1) | |
| crop_win_width = np.random.randint(image_width - border, image_width) | |
| crop_win_height = np.random.randint(image_height - border, image_height) | |
| random_region = ( | |
| (image_width - crop_win_width) >> 1, (image_height - crop_win_height) >> 1, (image_width + crop_win_width) >> 1, | |
| (image_height + crop_win_height) >> 1) | |
| return image.crop(random_region), label.crop(random_region) | |
| def random_rotate(image, label, angle=15): | |
| mode = Image.BICUBIC | |
| if random.random() > 0.8: | |
| random_angle = np.random.randint(-angle, angle) | |
| image = image.rotate(random_angle, mode) | |
| label = label.rotate(random_angle, mode) | |
| return image, label | |
| def color_enhance(image): | |
| bright_intensity = random.randint(5, 15) / 10.0 | |
| image = ImageEnhance.Brightness(image).enhance(bright_intensity) | |
| contrast_intensity = random.randint(5, 15) / 10.0 | |
| image = ImageEnhance.Contrast(image).enhance(contrast_intensity) | |
| color_intensity = random.randint(0, 20) / 10.0 | |
| image = ImageEnhance.Color(image).enhance(color_intensity) | |
| sharp_intensity = random.randint(0, 30) / 10.0 | |
| image = ImageEnhance.Sharpness(image).enhance(sharp_intensity) | |
| return image | |
| def random_gaussian(image, mean=0.1, sigma=0.35): | |
| def gaussianNoisy(im, mean=mean, sigma=sigma): | |
| for _i in range(len(im)): | |
| im[_i] += random.gauss(mean, sigma) | |
| return im | |
| img = np.asarray(image) | |
| width, height = img.shape | |
| img = gaussianNoisy(img[:].flatten(), mean, sigma) | |
| img = img.reshape([width, height]) | |
| return Image.fromarray(np.uint8(img)) | |
| def random_pepper(img, N=0.0015): | |
| img = np.array(img) | |
| noiseNum = int(N * img.shape[0] * img.shape[1]) | |
| for i in range(noiseNum): | |
| randX = random.randint(0, img.shape[0] - 1) | |
| randY = random.randint(0, img.shape[1] - 1) | |
| if random.randint(0, 1) == 0: | |
| img[randX, randY] = 0 | |
| else: | |
| img[randX, randY] = 255 | |
| return Image.fromarray(img) | |