preproc.py

import os
from PIL import Image, ImageEnhance
import torch
import random
import numpy as np
from torchvision.transforms import functional as F
from torchvision.transforms import InterpolationMode
import numbers
import random


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]
    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)
        img[randX, randY] = random.randint(0, 1) * 255
    return Image.fromarray(img)