【深度学习笔记】-代码解读4-图像数据增强
【深度学习笔记】-代码解读4-图像数据增强 cv2.resize 尺寸放大缩小cv2.warpAffine 旋转（任意角度，如45°，90°，180°，270°）cv2.flip 翻转（水平翻转，垂直翻转）明亮度改变（变亮，变暗）cv2.warpAffine 像素平移（往一个方向平移像素，空出部分自动填补黑色）添加噪声（椒盐噪声，高斯噪声）
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1 方法
- cv2.resize 尺寸放大缩小
- cv2.warpAffine 旋转（任意角度，如45°，90°，180°，270°）
- cv2.flip 翻转（水平翻转，垂直翻转）
- 明亮度改变（变亮，变暗）
- cv2.warpAffine 像素平移（往一个方向平移像素，空出部分自动填补黑色）
- 添加噪声（椒盐噪声，高斯噪声）
- 【注】不可用 “复制粘贴” 思想进行图像增强，因为此方法对计算机来说得到的图像是一样的，不会起到数据增强的效果。
2 演示
缩放
cv2.resize(image, None, fx=scale, fy=scale, interpolation=cv2.INTER_LINEAR)翻转
- 水平翻转
cv2.flip(image, 1, dst=None) # 水平镜像- 垂直翻转
cv2.flip(image, 0, dst=None) # 垂直镜像- 旋转
cv2.warpAffine(image, M, (w, h))明亮度
- 变暗
image_copy[xj, xi, 0] = int(image[xj, xi, 0] * percetage) image_copy[xj, xi, 1] = int(image[xj, xi, 1] * percetage) image_copy[xj, xi, 2] = int(image[xj, xi, 2] * percetage)- 变亮
image_copy[xj, xi, 0] = np.clip(int(image[xj, xi, 0] * percetage), a_max=255, a_min=0) image_copy[xj, xi, 1] = np.clip(int(image[xj, xi, 1] * percetage), a_max=255, a_min=0) image_copy[xj, xi, 2] = np.clip(int(image[xj, xi, 2] * percetage), a_max=255, a_min=0)- 平移
cv2.warpAffine(img, mat_translation, (width, height))增加噪声
增加椒盐噪声
randR = np.random.randint(0, src.shape[0] - 1) randG = np.random.randint(0, src.shape[1] - 1) randB = np.random.randint(0, 3) if np.random.randint(0, 1) == 0: SP_NoiseImg[randR, randG, randB] = 0 else: SP_NoiseImg[randR, randG, randB] = 255增加高斯噪声
temp_x = np.random.randint(0, h) temp_y = np.random.randint(0, w) G_Noiseimg[temp_x][temp_y][np.random.randint(3)] = np.random.randn(1)[0]
3 总结
import os import cv2 import numpy as np ''' 缩放 ''' # 放大缩小 def Scale(image, scale): return cv2.resize(image, None, fx=scale, fy=scale, interpolation=cv2.INTER_LINEAR) ''' 翻转 ''' # 水平翻转 def Horizontal(image): return cv2.flip(image, 1, dst=None) # 水平镜像 # 垂直翻转 def Vertical(image): return cv2.flip(image, 0, dst=None) # 垂直镜像 # 旋转 def Rotate(image, angle=15, scale=0.9): w = image.shape[1] h = image.shape[0] M = cv2.getRotationMatrix2D((w / 2, h / 2), angle, scale) # rotate matrix image = cv2.warpAffine(image, M, (w, h)) # rotate return image ''' 明亮度 ''' # 变暗 def Darker(image, percetage=0.9): image_copy = image.copy() w = image.shape[1] h = image.shape[0] # get darker for xi in range(0, w): for xj in range(0, h): image_copy[xj, xi, 0] = int(image[xj, xi, 0] * percetage) image_copy[xj, xi, 1] = int(image[xj, xi, 1] * percetage) image_copy[xj, xi, 2] = int(image[xj, xi, 2] * percetage) return image_copy # 明亮 def Brighter(image, percetage=1.1): image_copy = image.copy() w = image.shape[1] h = image.shape[0] # get brighter for xi in range(0, w): for xj in range(0, h): image_copy[xj, xi, 0] = np.clip(int(image[xj, xi, 0] * percetage), a_max=255, a_min=0) image_copy[xj, xi, 1] = np.clip(int(image[xj, xi, 1] * percetage), a_max=255, a_min=0) image_copy[xj, xi, 2] = np.clip(int(image[xj, xi, 2] * percetage), a_max=255, a_min=0) return image_copy # 平移 def Move(img, x, y): img_info = img.shape height = img_info[0] width = img_info[1] mat_translation = np.float32([[1, 0, x], [0, 1, y]]) # 变换矩阵：设置平移变换所需的计算矩阵：2行3列 # [[1,0,20],[0,1,50]] 表示平移变换：其中x表示水平方向上的平移距离，y表示竖直方向上的平移距离。 dst = cv2.warpAffine(img, mat_translation, (width, height)) # 变换函数 return dst ''' 增加噪声 ''' # 椒盐噪声 def SaltAndPepper(src, percetage=0.05): SP_NoiseImg = src.copy() SP_NoiseNum = int(percetage * src.shape[0] * src.shape[1]) for i in range(SP_NoiseNum): randR = np.random.randint(0, src.shape[0] - 1) randG = np.random.randint(0, src.shape[1] - 1) randB = np.random.randint(0, 3) if np.random.randint(0, 1) == 0: SP_NoiseImg[randR, randG, randB] = 0 else: SP_NoiseImg[randR, randG, randB] = 255 return SP_NoiseImg # 高斯噪声 def GaussianNoise(image, percetage=0.05): G_Noiseimg = image.copy() w = image.shape[1] h = image.shape[0] G_NoiseNum = int(percetage * image.shape[0] * image.shape[1]) for i in range(G_NoiseNum): temp_x = np.random.randint(0, h) temp_y = np.random.randint(0, w) G_Noiseimg[temp_x][temp_y][np.random.randint(3)] = np.random.randn(1)[0] return G_Noiseimg def Blur(img): blur = cv2.GaussianBlur(img, (7, 7), 1.5) # cv2.GaussianBlur(图像，卷积核，标准差） return blur def TestOnePic(): test_jpg_loc = r"data/daisy/1.jpg" test_jpg = cv2.imread(test_jpg_loc) cv2.imshow("Show Img", test_jpg) img1 = Blur(test_jpg) cv2.imshow("Img 1", img1) img2 = GaussianNoise(test_jpg,0.01) cv2.imshow("Img 2", img2) cv2.waitKey(0) def TestOneDir(): root_path = "data/daisy" save_path = root_path for a, b, c in os.walk(root_path): for file_i in c: file_i_path = os.path.join(a, file_i) print(file_i_path) img_i = cv2.imread(file_i_path) img_scale = Scale(img_i,1.5) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_scale.jpg"), img_scale) img_horizontal = Horizontal(img_i) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_horizontal.jpg"), img_horizontal) img_vertical = Vertical(img_i) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_vertical.jpg"), img_vertical) img_rotate = Rotate(img_i,90) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_rotate90.jpg"), img_rotate) img_rotate = Rotate(img_i, 180) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_rotate180.jpg"), img_rotate) img_rotate = Rotate(img_i, 270) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_rotate270.jpg"), img_rotate) img_move = Move(img_i,15,15) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_move.jpg"), img_move) img_darker = Darker(img_i) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_darker.jpg"), img_darker) img_brighter = Brighter(img_i) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_brighter.jpg"), img_brighter) img_blur = Blur(img_i) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_blur.jpg"), img_blur) img_salt = SaltAndPepper(img_i,0.05) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_salt.jpg"), img_salt) def AllData(rootpath): root_path = "data/" save_loc = root_path for a, b, c in os.walk(root_path): for file_i in c: file_i_path = os.path.join(a, file_i) print(file_i_path) split = os.path.split(file_i_path) dir_loc = os.path.split(split[0])[1] save_path = os.path.join(save_loc, dir_loc) img_i = cv2.imread(file_i_path) img_scale = Scale(img_i,1.5) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_scale.jpg"), img_scale) img_horizontal = Horizontal(img_i) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_horizontal.jpg"), img_horizontal) img_vertical = Vertical(img_i) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_vertical.jpg"), img_vertical) img_rotate = Rotate(img_i, 90) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_rotate90.jpg"), img_rotate) img_rotate = Rotate(img_i, 180) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_rotate180.jpg"), img_rotate) img_rotate = Rotate(img_i, 270) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_rotate270.jpg"), img_rotate) img_move = Move(img_i, 15, 15) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_move.jpg"), img_move) img_darker = Darker(img_i) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_darker.jpg"), img_darker) img_brighter = Brighter(img_i) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_brighter.jpg"), img_brighter) img_blur = Blur(img_i) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_blur.jpg"), img_blur) img_salt = SaltAndPepper(img_i, 0.05) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_salt.jpg"), img_salt) if __name__ == "__main__": TestOneDir() TestOnePic() root_path = "data/" AllData(root_path)
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