def get_mask(self, im: np.array): """ get_mask(im) Function to compute the mask of an certain image,in this case is done in HSV system to an image with Y-histogram equalised Parameters Value ---------------------- 'im' Dataset image Returns the mask, binary image with the detections. """ # Equalization of the Y channel of the image im = histogram_equalization(im, False) # Color segmentation in HSV mask, im = segregation(im, 'hsv') # Mask Blurring mask = blur(mask) # Hole filling mask = fill_holes(mask) # Compute the final mask mask = discard_geometry.get_mask(mask) return mask, im
def get_mask(self, im: np.array): """ get_mask(im) Function to compute the mask of an certain image,in this case is done in RGB system Parameters Value ---------------------- 'im' Dataset image Returns the mask, binary image with the detections. """ # Color segmentation in RGB mask, im = segregation(im, 'rgb') # Hole filling mask = fill_holes(mask) # Mask Blurring mask = blur(mask) # Compute the final mask mask = discard_geometry.get_mask(mask) return mask, im
def get_mask(self, im: np.array): """ get_mask(im) Function to compute the mask of an certain image,in this case is done in HSV system Parameters Value ---------------------- 'im' Dataset image Returns the mask, binary image with the detections. """ # Color segmentation in HSV mask, im = segregation(im, 'hsv') mask = morpho(mask) regions = get_cc_regions(mask) # Compute the final mask mask, regions = discard_geometry.get_mask(mask, regions) return regions, mask, im
def get_mask(self, im: np.array): mask, im = segregation(im, 'hsv') mask = morpho(mask) mask, regions = sliding_window(mask) return regions, mask, im
def get_mask(self, im: np.array): # Color segmentation in HSV mask, im = segregation(im, 'hsv') mask = morpho(mask) mask, regions = integral(mask) return regions, mask, im
def get_mask(self, im: np.array): # Color segmentation in HSV mask, im = segregation(im, 'hsv') mask = morpho(mask) mask = fill_holes(mask) mask, regions = convolution(mask) mask, regions = discard_geometry.get_mask(mask, regions) return regions, mask, im
'mask' Binary image with the detections obtained by the image segmentation returns the improved mask """ im_floodfill = mask.copy() # Mask used to flood filling. # Notice the size needs to be 2 pixels than the image. h, w = im_floodfill.shape[:2] filling_mask = np.zeros((h + 2, w + 2), np.uint8) cv2.floodFill(im_floodfill, filling_mask, (0, 0), 255) im_floodfill_inv = cv2.bitwise_not(im_floodfill) im_out = mask | im_floodfill_inv return im_out if __name__ == '__main__': im = cv2.imread('../../datasets/train/00.000948.jpg') mask_hsv = segregation(im, 'hsv') mask_hsv = get_mask(mask_hsv) cv2.imshow('image', im) cv2.imshow('mask', mask_hsv) cv2.waitKey(0) cv2.destroyAllWindows()
def get_mask(self, im: np.array): mask, im = segregation(im, 'hsv') regions = template_matching.template_matching_global(mask) mask = np.zeros(im.shape[0:2]) return regions, mask, im