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
