masked image
时间: 2023-10-27 13:06:22 浏览: 47
Masked image通常指的是一种图像处理技术,即在图像中将某些区域遮挡或隐藏起来,通常使用遮罩层(mask layer)实现。这种技术常用于图像分割、语义分割等任务中,以便将感兴趣的区域与背景分开,或者在图像处理过程中去除干扰。在计算机视觉领域,常用的遮罩层包括二值图、灰度图、RGB图以及alpha图等。
相关问题
from skimage.segmentation import slic from skimage.segmentation import mark_boundaries from skimage.util import img_as_float import matplotlib.pyplot as plt import numpy as np import cv2 import os # args args = {"image": 'I:\\18Breakageratecalculation\\SVM run\\images\\030.jpg'} # load the image and apply SLIC and extract (approximately) # the supplied number of segments image = cv2.imread(args["image"]) segments = slic(img_as_float(image), n_segments=100, sigma=3) # show the output of SLIC fig = plt.figure('Superpixels') ax = fig.add_subplot(1, 1, 1) ax.imshow(mark_boundaries(img_as_float(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)), segments)) plt.axis("off") plt.show() print("segments:\n", segments) print("np.unique(segments):", np.unique(segments)) # loop over the unique segment values for (i, segVal) in enumerate(np.unique(segments)): # construct a mask for the segment print("[x] inspecting segment {}, for {}".format(i, segVal)) mask = np.zeros(image.shape[:2], dtype="uint8") mask[segments == segVal] = 255 # apply the mask to the image masked_image = np.multiply(image, cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR) > 0) # save the masked image as a file filename = os.path.join(r"I:\18Breakageratecalculation\SVM run\run-images2\030", "segment_%d.png" % i) cv2.imwrite(filename, masked_image) # show the masked region cv2.imshow("Mask", mask) cv2.imshow("Applied", np.multiply(image, cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR) > 0)) cv2.waitKey(0)保存超像素识别结果图像
可以使用下面的代码来保存超像素识别结果图像:
```
# create a directory to store the segmented images
if not os.path.exists("segmented_images"):
os.makedirs("segmented_images")
# save the image with superpixel boundaries
plt.imsave(os.path.join("segmented_images", "superpixel_boundaries.png"), mark_boundaries(img_as_float(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)), segments))
# loop over the unique segment values
for (i, segVal) in enumerate(np.unique(segments)):
# construct a mask for the segment
mask = np.zeros(image.shape[:2], dtype="uint8")
mask[segments == segVal] = 255
# apply the mask to the image
masked_image = np.multiply(image, cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR) > 0)
# save the masked image as a file
filename = os.path.join("segmented_images", "segment_%d.png" % i)
cv2.imwrite(filename, masked_image)
```
这将在当前目录下创建一个名为 "segmented_images" 的文件夹,并将超像素边界图像和每个超像素块保存为单独的图像文件。
Traceback (most recent call last): File "/home/lang/Documents/orr_3d_vis/orr_3d_vis/vis.py", line 262, in <module> masked_image_values = [masked_image[i, j] for i in range(masked_image.shape[0]) for j in TypeError: 'tuple' object is not callable
这个错误是由于在代码的某个地方,你把一个元组当做函数来调用了。通常情况下,元组后面跟着括号时,它会被解释为一个元组而不是函数调用。你可以在代码中找到这个错误,并检查一下是否有地方写错了。可以参考下面的示例代码来避免这个错误:
```python
masked_image_values = [masked_image[i, j] for i in range(masked_image.shape[0]) for j in range(masked_image.shape[1])]
```
在这个示例中,我们使用了两个 `range` 函数来生成 `i` 和 `j` 的值,这样就避免了把元组当做函数调用的错误。