（scikit图像）HOG可视化图像在保存时显示为黑色

Question

I am new to computer vision and image processing and am using this code

from skimage.feature import hog
hog_list, hog_img = hog(test_img_gray, 
                        orientations=8, 
                        pixels_per_cell=(16, 16), cells_per_block=(1, 1),
                        block_norm='L1', 
                        visualise=True,
                        feature_vector=True)
plt.figure(figsize=(15,10))
plt.imshow(hog_img)

to get this HOG visualization image

I have 2 questions at this point:

1. When I try to save this image (as a .pdf or .jpg) the resulting image is pure black. Converting this image to PIL format and examining it with

   hog_img_pil = Image.fromarray(hog_img) hog_img_pil.show()

still shows the image as pure black. Why is this happening and how can I fix it?

2. When I try to run this code

   hog_img = cv2.cvtColor(hog_img, cv2.COLOR_BGR2GRAY)

to convert the image to grayscale I get the error error: (-215) depth == CV_8U || depth == CV_16U || depth == CV_32F in function cvtColor error: (-215) depth == CV_8U || depth == CV_16U || depth == CV_32F in function cvtColor error: (-215) depth == CV_8U || depth == CV_16U || depth == CV_32F in function cvtColor . What do I need to do to get this image in grayscale and why would this be happening?

As additional information, running hog_img.shape returns (1632, 1224) which is just the size of the image, which I had initially interpreted to mean that the image is already is already in grayscale (since it appears to be lacking a dimension for color channel). However, when I then tried to run

test_img_bw = cv2.adaptiveThreshold(
    src=hog_img, 
    maxValue=255, 
    adaptiveMethod=cv2.ADAPTIVE_THRESH_GAUSSIAN_C, 
    thresholdType=cv2.THRESH_BINARY, 
    blockSize=115, C=4)

I got the error error: (-215) src.type() == CV_8UC1 in function adaptiveThreshold which this answer seems to indicate means that the image is not in grayscale.

Finally, another bit of useful information is that running print(hog_img.dtype) on the image returns float64 .

I will continue to debug, in the meantime

Thanks for any thoughts :)

Answer 1

1. Inverting the image with hog_img_inv = cv2.bitwise_not(hog_img) and using plt.figure(figsize=(15,10)) plt.imshow(hog_img_uint8_inv) showed that the lines were in fact there but are very faint (I've included the image here for comletness, but you can barley see it (but trust me, it's there)). I will have to do some more processing of the image to get the lines more distinguishable.

2. Running print(hog_img.dtype) showed that the dtype was float64 when (I think) it should have been uint8 . I fixed this by running hog_img_uint8 = hog_img.astype(np.uint8) which seems to have fixed the problem with passing the image to other algorithms (eg. cv2.adaptiveThreshold).

Answer 2

If had the same problem. But if you look inside the docu , they also use this code for better visualisation:

# Rescale histogram for better display
hog_image_rescaled = exposure.rescale_intensity(hog_image, in_range=(0, 0.02))

But I still have the same problem. Visualisation with matplotlib is no problem. saving the image with opencv (or skimage) saves only a black image...