基本資料增強處理
阿新 • • 發佈：2019-01-04
資料增強主要包含如下方式：
1.旋轉： 可通過在原圖上先放大影象，然後剪下影象得到。
2.平移：先放大影象，然後水平或垂直偏移位置剪下
3.縮放：縮放影象
4.水平翻轉：以過影象中心的豎直軸為對稱軸，將左、右兩邊畫素交換
填充模式：最近鄰方式
5.顏色色差（飽和度、亮度、對比度、 銳度等）
相關Python原始碼： """資料增強 1. 翻轉變換 flip 2. 隨機修剪 random crop 3. 色彩抖動 color jittering 4. 平移變換 shift 5. 尺度變換 scale 6. 對比度變換 contrast 7. 噪聲擾動 noise 8. 旋轉變換/反射變換 Rotation/reflection from PIL import Image, ImageEnhance, ImageOps, ImageFile import numpy as np import random import threading, os, time import logging logger = logging.getLogger(__name__) ImageFile.LOAD_TRUNCATED_IMAGES = True class DataAugmentation: """ 包含資料增強的八種方式 """ def __init__(self): pass @staticmethod def openImage(image): return Image.open(image, mode="r") @staticmethod def randomRotation(image, mode=Image.BICUBIC): """ 對影象進行隨機任意角度(0~360度)旋轉 :param mode 鄰近插值,雙線性插值,雙三次B樣條插值(default) :param image PIL的影象image :return: 旋轉轉之後的影象 """ random_angle = np.random.randint(1, 360) return image.rotate(random_angle, mode) @staticmethod def randomCrop(image): """ 對影象隨意剪下,考慮到影象大小範圍(68,68),使用一個一個大於(36*36)的視窗進行截圖 :param image: PIL的影象image :return: 剪下之後的影象 """ image_width = image.size[0] image_height = image.size[1] crop_win_size = np.random.randint(40, 68) random_region = ( (image_width - crop_win_size) >> 1, (image_height - crop_win_size) >> 1, (image_width + crop_win_size) >> 1, (image_height + crop_win_size) >> 1) return image.crop(random_region) @staticmethod def randomColor(image): """ 對影象進行顏色抖動 :param image: PIL的影象image :return: 有顏色色差的影象image """ random_factor = np.random.randint(0, 31) / 10. # 隨機因子 color_image = ImageEnhance.Color(image).enhance(random_factor) # 調整影象的飽和度 random_factor = np.random.randint(10, 21) / 10. # 隨機因子 brightness_image = ImageEnhance.Brightness(color_image).enhance(random_factor) # 調整影象的亮度 random_factor = np.random.randint(10, 21) / 10. # 隨機因1子 contrast_image = ImageEnhance.Contrast(brightness_image).enhance(random_factor) # 調整影象對比度 random_factor = np.random.randint(0, 31) / 10. # 隨機因子 return ImageEnhance.Sharpness(contrast_image).enhance(random_factor) # 調整影象銳度 @staticmethod def randomGaussian(image, mean=0.2, sigma=0.3): """ 對影象進行高斯噪聲處理 :param image: :return: """ def gaussianNoisy(im, mean=0.2, sigma=0.3): """ 對影象做高斯噪音處理 :param im: 單通道影象 :param mean: 偏移量 :param sigma: 標準差 :return: """ for _i in range(len(im)): im[_i] += random.gauss(mean, sigma) return im # 將影象轉化成陣列 img = np.asarray(image) img.flags.writeable = True # 將陣列改為讀寫模式 width, height = img.shape[:2] img_r = gaussianNoisy(img[:, :, 0].flatten(), mean, sigma) img_g = gaussianNoisy(img[:, :, 1].flatten(), mean, sigma) img_b = gaussianNoisy(img[:, :, 2].flatten(), mean, sigma) img[:, :, 0] = img_r.reshape([width, height]) img[:, :, 1] = img_g.reshape([width, height]) img[:, :, 2] = img_b.reshape([width, height]) return Image.fromarray(np.uint8(img)) @staticmethod def saveImage(image, path): image.save(path) def makeDir(path): try: if not os.path.exists(path): if not os.path.isfile(path): # os.mkdir(path) os.makedirs(path) return 0 else: return 1 except Exception, e: print str(e) return -2 def imageOps(func_name, image, des_path, file_name, times=5): funcMap = {"randomRotation": DataAugmentation.randomRotation, "randomCrop": DataAugmentation.randomCrop, "randomColor": DataAugmentation.randomColor, "randomGaussian": DataAugmentation.randomGaussian } if funcMap.get(func_name) is None: logger.error("%s is not exist", func_name) return -1 for _i in range(0, times, 1): new_image = funcMap[func_name](image) DataAugmentation.saveImage(new_image, os.path.join(des_path, func_name + str(_i) + file_name)) opsList = {"randomRotation", "randomCrop", "randomColor", "randomGaussian"} def threadOPS(path, new_path): """ 多執行緒處理事務 :param src_path: 資原始檔 :param des_path: 目的地檔案 :return: """ if os.path.isdir(path): img_names = os.listdir(path) else: img_names = [path] for img_name in img_names: print img_name tmp_img_name = os.path.join(path, img_name) if os.path.isdir(tmp_img_name): if makeDir(os.path.join(new_path, img_name)) != -1: threadOPS(tmp_img_name, os.path.join(new_path, img_name)) else: print 'create new dir failure' return -1 # os.removedirs(tmp_img_name) elif tmp_img_name.split('.')[1] != "DS_Store": # 讀取檔案並進行操作 image = DataAugmentation.openImage(tmp_img_name) threadImage = [0] * 5 _index = 0 for ops_name in opsList: threadImage[_index] = threading.Thread(target=imageOps, args=(ops_name, image, new_path, img_name,)) threadImage[_index].start() _index += 1 time.sleep(0.2) if __name__ == '__main__': threadOPS("/home/pic-image/train/12306train", "/home/pic-image/train/12306train3")
相關Python原始碼： """資料增強 1. 翻轉變換 flip 2. 隨機修剪 random crop 3. 色彩抖動 color jittering 4. 平移變換 shift 5. 尺度變換 scale 6. 對比度變換 contrast 7. 噪聲擾動 noise 8. 旋轉變換/反射變換 Rotation/reflection from PIL import Image, ImageEnhance, ImageOps, ImageFile import numpy as np import random import threading, os, time import logging logger = logging.getLogger(__name__) ImageFile.LOAD_TRUNCATED_IMAGES = True class DataAugmentation: """ 包含資料增強的八種方式 """ def __init__(self): pass @staticmethod def openImage(image): return Image.open(image, mode="r") @staticmethod def randomRotation(image, mode=Image.BICUBIC): """ 對影象進行隨機任意角度(0~360度)旋轉 :param mode 鄰近插值,雙線性插值,雙三次B樣條插值(default) :param image PIL的影象image :return: 旋轉轉之後的影象 """ random_angle = np.random.randint(1, 360) return image.rotate(random_angle, mode) @staticmethod def randomCrop(image): """ 對影象隨意剪下,考慮到影象大小範圍(68,68),使用一個一個大於(36*36)的視窗進行截圖 :param image: PIL的影象image :return: 剪下之後的影象 """ image_width = image.size[0] image_height = image.size[1] crop_win_size = np.random.randint(40, 68) random_region = ( (image_width - crop_win_size) >> 1, (image_height - crop_win_size) >> 1, (image_width + crop_win_size) >> 1, (image_height + crop_win_size) >> 1) return image.crop(random_region) @staticmethod def randomColor(image): """ 對影象進行顏色抖動 :param image: PIL的影象image :return: 有顏色色差的影象image """ random_factor = np.random.randint(0, 31) / 10. # 隨機因子 color_image = ImageEnhance.Color(image).enhance(random_factor) # 調整影象的飽和度 random_factor = np.random.randint(10, 21) / 10. # 隨機因子 brightness_image = ImageEnhance.Brightness(color_image).enhance(random_factor) # 調整影象的亮度 random_factor = np.random.randint(10, 21) / 10. # 隨機因1子 contrast_image = ImageEnhance.Contrast(brightness_image).enhance(random_factor) # 調整影象對比度 random_factor = np.random.randint(0, 31) / 10. # 隨機因子 return ImageEnhance.Sharpness(contrast_image).enhance(random_factor) # 調整影象銳度 @staticmethod def randomGaussian(image, mean=0.2, sigma=0.3): """ 對影象進行高斯噪聲處理 :param image: :return: """ def gaussianNoisy(im, mean=0.2, sigma=0.3): """ 對影象做高斯噪音處理 :param im: 單通道影象 :param mean: 偏移量 :param sigma: 標準差 :return: """ for _i in range(len(im)): im[_i] += random.gauss(mean, sigma) return im # 將影象轉化成陣列 img = np.asarray(image) img.flags.writeable = True # 將陣列改為讀寫模式 width, height = img.shape[:2] img_r = gaussianNoisy(img[:, :, 0].flatten(), mean, sigma) img_g = gaussianNoisy(img[:, :, 1].flatten(), mean, sigma) img_b = gaussianNoisy(img[:, :, 2].flatten(), mean, sigma) img[:, :, 0] = img_r.reshape([width, height]) img[:, :, 1] = img_g.reshape([width, height]) img[:, :, 2] = img_b.reshape([width, height]) return Image.fromarray(np.uint8(img)) @staticmethod def saveImage(image, path): image.save(path) def makeDir(path): try: if not os.path.exists(path): if not os.path.isfile(path): # os.mkdir(path) os.makedirs(path) return 0 else: return 1 except Exception, e: print str(e) return -2 def imageOps(func_name, image, des_path, file_name, times=5): funcMap = {"randomRotation": DataAugmentation.randomRotation, "randomCrop": DataAugmentation.randomCrop, "randomColor": DataAugmentation.randomColor, "randomGaussian": DataAugmentation.randomGaussian } if funcMap.get(func_name) is None: logger.error("%s is not exist", func_name) return -1 for _i in range(0, times, 1): new_image = funcMap[func_name](image) DataAugmentation.saveImage(new_image, os.path.join(des_path, func_name + str(_i) + file_name)) opsList = {"randomRotation", "randomCrop", "randomColor", "randomGaussian"} def threadOPS(path, new_path): """ 多執行緒處理事務 :param src_path: 資原始檔 :param des_path: 目的地檔案 :return: """ if os.path.isdir(path): img_names = os.listdir(path) else: img_names = [path] for img_name in img_names: print img_name tmp_img_name = os.path.join(path, img_name) if os.path.isdir(tmp_img_name): if makeDir(os.path.join(new_path, img_name)) != -1: threadOPS(tmp_img_name, os.path.join(new_path, img_name)) else: print 'create new dir failure' return -1 # os.removedirs(tmp_img_name) elif tmp_img_name.split('.')[1] != "DS_Store": # 讀取檔案並進行操作 image = DataAugmentation.openImage(tmp_img_name) threadImage = [0] * 5 _index = 0 for ops_name in opsList: threadImage[_index] = threading.Thread(target=imageOps, args=(ops_name, image, new_path, img_name,)) threadImage[_index].start() _index += 1 time.sleep(0.2) if __name__ == '__main__': threadOPS("/home/pic-image/train/12306train", "/home/pic-image/train/12306train3")