平滑处理

图像平滑处理

1import cv2
2
3original_img = cv2.imread('./images/fruits/bananas_black1b_noise.jpg')
4grayscale_img = cv2.cvtColor(original_img, cv2.COLOR_BGR2GRAY)
5
6# 应用不同的全局阈值
7thresholds = [60, 90, 105, 120, 150]
8for threshold_ndx in range(len(thresholds)):
9    threshold = thresholds[threshold_ndx]
10    retval, mask = cv2.threshold(grayscale_img, threshold, 255, cv2.THRESH_BINARY)
11    # 在掩码上标注所使用的阈值
12    cv2.putText(mask, str(threshold), (0, 60), cv2.FONT_HERSHEY_SIMPLEX, 
13    1.0, 255, 2)
14    cv2.imwrite(f'./output/{threshold_ndx + 1}_mask.png', mask)
15
16cv2.imwrite('./output/0_original.png', original_img)

1import cv2
2
3original_img = cv2.imread('./images/fruits/bananas_black1b_noise.jpg')
4
5# 使用不同卷积核尺寸进行均值模糊
6kernel_sizes = [(3, 3), (7, 7), (11, 11)]
7for kernel_ndx in range(len(kernel_sizes)):
8  kernel_size = kernel_sizes[kernel_ndx]
9  blurred_img = cv2.blur(
10    src=original_img,
11    ksize=kernel_size
12  )
13  # 在图像上标注使用的卷积核尺寸
14  cv2.putText(blurred_img, str(kernel_size), (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 
15    1.0, (255, 255, 255), 2)
16  cv2.imwrite(f'./output/{kernel_ndx + 1}_blurred.png', blurred_img)
17
18cv2.imwrite('./output/0_original.png', original_img)

1import cv2
2
3original_img = cv2.imread('./images/fruits/bananas_black1b_noise.jpg')
4
5# 使用不同卷积核尺寸进行高斯模糊
6kernel_sizes = [(3, 3), (7, 7), (11, 11)]
7for kernel_ndx in range(len(kernel_sizes)):
8  kernel_size = kernel_sizes[kernel_ndx]
9  blurred_img = cv2.GaussianBlur(
10    src=original_img,
11    ksize=kernel_size,
12    sigmaX=kernel_size[0]/4,  # 水平方向的高斯标准差
13    sigmaY=kernel_size[1]/4,  # 垂直方向的高斯标准差
14  )
15  # 在图像上标注卷积核尺寸
16  cv2.putText(blurred_img, str(kernel_size), (0, 60), cv2.FONT_HERSHEY_SIMPLEX, 
17    1.0, (255, 255, 255), 2)
18  cv2.imwrite(f'./output/{kernel_ndx + 1}_blurred.png', blurred_img)
19
20cv2.imwrite('./output/0_original.png', original_img)

1import cv2
2
3original_img = cv2.imread('./images/fruits/bananas_black1b_saltPepper.jpg')
4
5# 使用中值滤波处理椒盐噪声，尝试不同卷积核尺寸
6kernel_sizes = [3, 7, 11]
7for kernel_ndx in range(len(kernel_sizes)):
8  kernel_size = kernel_sizes[kernel_ndx]
9  blurred_img = cv2.medianBlur(
10    src=original_img,
11    ksize=kernel_size,
12  )
13  # 在图像上标注卷积核尺寸
14  cv2.putText(blurred_img, str(kernel_size), (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 
15    1.0, (255, 255, 255), 2)
16  cv2.imwrite(f'./output/{kernel_ndx + 1}_blurred.png', blurred_img)
17
18cv2.imwrite('./output/0_original.png', original_img)