How do I crop an image based on custom mask

Question

I start my model and get a mask prediction of the area I would like to crop.

img = cv2.imread('picture.jpg')
img = cv2.resize(img, (224, 224))
print(img.shape)
T = np.zeros((1, 224, 224, 3), dtype='float32')
T[0]=img
prediction = model.predict(T , verbose=1)
prediction = prediction[0, :, : , :]
plt.imshow(img)
plt.show()
print(prediction.shape)
plt.imshow(np.squeeze(prediction).astype(np.float32))
plt.show()

This code is the closest I have gotten to the desired image,
the problem with this image that is provided is that it doesn’t crop out black background

result = prediction * img
plt.imshow((result/255).astype(np.float32))
plt.show()

My desired image should be like this

Asked By: LuQchor

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Source

Answer 1

You could try this:

prediction = np.squeeze(prediction).astype(np.float32)
threshold = 0.5
xmin,ymin,xmax,ymax = (-1,-1,-1,-1)
for j in range(224):
    for i in range(224):
        if prediction[j,i] <= threshold: continue
        xmin = i if xmin == -1 or xmin > i else xmin
        xmax = i if xmax == -1 or xmax < i else xmax
        ymin = j if ymin == -1 or ymin > j else ymin
        ymax = j if ymax == -1 or ymax < j else ymax
cropImg = img[ymin:ymax,xmin:xmax]

Answered By: PepeChuy

Answer 2

You can try to find the bounding box around your predicted mask and then crop your image using the detected bounding box. Using the code from this excellent SO answer, it would result in something like this:

def bbox2(img):
    # from https://stackoverflow.com/a/31402351/19249364
    rows = np.any(img, axis=1)
    cols = np.any(img, axis=0)
    rmin, rmax = np.where(rows)[0][[0, -1]]
    cmin, cmax = np.where(cols)[0][[0, -1]]
    return rmin, rmax, cmin, cmax

bbox = bbox2(prediction.squeeze())
cropped = img[bbox[0]:bbox[1]+1, bbox[2]:bbox[3]+1, :]

Couldn’t test this code, so just tell me if it doesn’t work!

Answered By: asdf

How do I crop an image based on custom mask

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