機械学習の大きな宿題をするときは、いくつかのデータセットのデータを拡張および拡張する必要があります。
記事のディレクトリ
画像処理機能の強化
もともと明るさを変えたかったのですが、効果が良くなかったので、カーネル関数だけを使って画像オブジェクトのエッジをシャープにし、ターゲットオブジェクトのエッジと色の情報を強調しました。
def img_process(img, img_class, img_num, class_dir_path):
#1.图像锐化
kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]], np.float32) #定义一个核
#采用核边缘锐化，增强目标物体的边缘及色彩特征信息
dst = cv2.filter2D(img, -1, kernel=kernel)
# cv2.imshow("img_filter2D", dst)
#2.图像翻转
img_flip = cv2.flip(dst, 0)
# cv2.imshow("img_flip", img_flip)
効果を図に示します。
- 処理前の画像
- カーネル関数のエッジシャープニング、機能強化
- X、Y、X + Y軸にそれぞれ基づいて画像を反転します
フォルダ内のさまざまなカテゴリの写真をトラバースします
dataset_dir = "G://Python//class//ml2020//class_dataset//dataset//images"
class_list = ["glass", "metal", "paper", "plastic"]
if os.path.isdir(dataset_dir):
#进入数据集路径下
os.chdir(dataset_dir)
#依次遍历4类的文件夹
for class_name in class_list:
class_dir_path = dataset_dir + "//" + class_name
print(class_dir_path)
img_files_path = os.listdir(class_dir_path)
print(img_files_path)
#进入对应的文件夹
os.chdir(class_dir_path)
print(len(img_files_path))
#依次遍历每个类的文件夹中的图片
i = 1
for img_path in img_files_path:
process_img = cv2.imread(class_dir_path + "//" +img_path)
print(class_dir_path + "//" +img_path)
#图像处理部分XXXX
i += 1
print(class_name + "processes done!")
print("ALL done!")
else:
print("the dataset path is incorrect!")
完全なコード
import cv2
import numpy as np
import os
def img_process(img, img_class, img_num, class_dir_path, id):
'''
图像处理的主程序
'''
if id == 0:
processed_img = contrast_brightness_image(img, 1.2, 10)
elif id == 1:
processed_img = gasuss_noise(img)
elif id == 2:
processed_img = mirror(img)
elif id == 3:
processed_img = resize(img)
elif id == 4:
processed_img = rotate(img)
elif id == 5:
processed_img = shear(img)
else:
#1.图像锐化
kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]], np.float32) #定义一个核
#采用核边缘锐化，增强目标物体的边缘及色彩特征信息
dst = cv2.filter2D(img, -1, kernel=kernel)
# cv2.imshow("img_filter2D", dst)
#2.图像翻转
processed_img = cv2.flip(dst, 0)
#3.图片保存
cv2.imwrite(class_dir_path + "//" + img_class + str(img_num) + ".jpg", processed_img)
'''opencv数据增强
对图片进行色彩增强、高斯噪声、水平镜像、放大、旋转、剪切
'''
def contrast_brightness_image(src1, a, g):
'''
色彩增强（通过调节对比度和亮度）
'''
h, w, ch = src1.shape # 获取shape的数值，height和width、通道
# 新建全零图片数组src2,将height和width，类型设置为原图片的通道类型(色素全为零，输出为全黑图片)
src2 = np.zeros([h, w, ch], src1.dtype)
# addWeighted函数说明:计算两个图像阵列的加权和
dst = cv2.addWeighted(src1, a, src2, 1 - a, g)
return dst
def gasuss_noise(image, mean=0, var=0.001):
'''
添加高斯噪声
mean : 均值
var : 方差
'''
image = np.array(image / 255, dtype=float)
noise = np.random.normal(mean, var ** 0.5, image.shape)
out = image + noise
if out.min() < 0:
low_clip = -1.
else:
low_clip = 0.
out = np.clip(out, low_clip, 1.0)
out = np.uint8(out * 255)
return out
def mirror(image):
'''
水平镜像
'''
h_flip = cv2.flip(image, 1)
return h_flip
def resize(image):
'''
放大两倍
'''
height, width = image.shape[:2]
large = cv2.resize(image, (2 * width, 2 * height))
return large
def rotate(image):
'''
旋转
'''
rows, cols = image.shape[:2]
M = cv2.getRotationMatrix2D((cols / 2, rows / 2), 10, 1)
dst = cv2.warpAffine(image, M, (cols, rows))
return dst
def shear(image):
'''
剪切
'''
height, width = image.shape[:2]
cropped = image[int(height / 9):height, int(width / 9):width]
return cropped
def cv_process_show(image):
cv2.imshow("src", image)
contrast_brightness_image = contrast_brightness_image(image, 1.2, 10)
cv2.imshow("contrast_brightness_image", contrast_brightness_image)
gasuss_noise_image = gasuss_noise(image, mean=0, var=0.001)
cv2.imshow("gasuss_noise_image", gasuss_noise_image)
mirror_image = mirror(image)
cv2.imshow("mirror_image", mirror_image)
resize_image = resize(image)
cv2.imshow("resize_image", resize_image)
rotate_image = rotate(image)
cv2.imshow("rotate_image", rotate_image)
shear_image = shear(image)
cv2.imshow("shear_image", shear_image)
#1.图像锐化
kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]], np.float32) #定义一个核
#采用核边缘锐化，增强目标物体的边缘及色彩特征信息
dst = cv2.filter2D(image, -1, kernel=kernel)
#2.图像翻转
processed_img = cv2.flip(dst, 0)
cv2.imshow("img_filter2D", processed_img)
def batch_preocess(dataset_dir, class_list):
if os.path.isdir(dataset_dir):
#进入数据集路径下
os.chdir(dataset_dir)
#依次遍历4类的文件夹
for class_name in class_list:
class_dir_path = dataset_dir + "//" + class_name
print(class_dir_path)
img_files_path = os.listdir(class_dir_path)
print(img_files_path)
#进入对应的文件夹
os.chdir(class_dir_path)
print(len(img_files_path))
#依次遍历每个类的文件夹中的图片
i = 1
for img_path in img_files_path:
process_img = cv2.imread(class_dir_path + "//" +img_path)
print(class_dir_path + "//" +img_path)
#数据增强子函数
img_process(process_img, class_name, len(img_files_path) + i, class_dir_path, i)
i += 1
print(class_name + "processes done!")
print("ALL done!")
else:
print("the dataset path is incorrect!")
if __name__ == '__main__':
dataset_dir = "G://Python//class//ml2020//class_dataset//dataset//images_processed"
class_list = ["glass", "metal", "paper", "plastic"]
img_dir = r"G:\Python\class\ml2020\PaddlePaddle\test_pic\metal1.jpg"
image = cv2.imread(img_dir)
# 单张照片处理效果展示
cv_process_show(image)
# 批量处理
# batch_preocess(dataset_dir, class_list)
参照：