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128 lines
4.5 KiB
Python
128 lines
4.5 KiB
Python
from collections import OrderedDict
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import cv2 as cv
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import numpy as np
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from .blender import Blender
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class SeamFinder:
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"""https://docs.opencv.org/4.x/d7/d09/classcv_1_1detail_1_1SeamFinder.html""" # noqa
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SEAM_FINDER_CHOICES = OrderedDict()
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SEAM_FINDER_CHOICES['dp_color'] = cv.detail_DpSeamFinder('COLOR')
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SEAM_FINDER_CHOICES['dp_colorgrad'] = cv.detail_DpSeamFinder('COLOR_GRAD')
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SEAM_FINDER_CHOICES['voronoi'] = cv.detail.SeamFinder_createDefault(cv.detail.SeamFinder_VORONOI_SEAM) # noqa
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SEAM_FINDER_CHOICES['no'] = cv.detail.SeamFinder_createDefault(cv.detail.SeamFinder_NO) # noqa
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DEFAULT_SEAM_FINDER = list(SEAM_FINDER_CHOICES.keys())[0]
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def __init__(self, finder=DEFAULT_SEAM_FINDER):
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self.finder = SeamFinder.SEAM_FINDER_CHOICES[finder]
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def find(self, imgs, corners, masks):
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"""https://docs.opencv.org/4.x/d0/dd5/classcv_1_1detail_1_1DpSeamFinder.html#a7914624907986f7a94dd424209a8a609""" # noqa
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imgs_float = [img.astype(np.float32) for img in imgs]
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return self.finder.find(imgs_float, corners, masks)
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@staticmethod
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def resize(seam_mask, mask):
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dilated_mask = cv.dilate(seam_mask, None)
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resized_seam_mask = cv.resize(dilated_mask, (mask.shape[1],
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mask.shape[0]),
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0, 0, cv.INTER_LINEAR_EXACT)
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return cv.bitwise_and(resized_seam_mask, mask)
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@staticmethod
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def draw_seam_mask(img, seam_mask, color=(0, 0, 0)):
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seam_mask = cv.UMat.get(seam_mask)
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overlayed_img = np.copy(img)
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overlayed_img[seam_mask == 0] = color
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return overlayed_img
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@staticmethod
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def draw_seam_polygons(panorama, blended_seam_masks, alpha=0.5):
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return add_weighted_image(panorama, blended_seam_masks, alpha)
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@staticmethod
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def draw_seam_lines(panorama, blended_seam_masks,
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linesize=1, color=(0, 0, 255)):
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seam_lines = \
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SeamFinder.exctract_seam_lines(blended_seam_masks, linesize)
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panorama_with_seam_lines = panorama.copy()
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panorama_with_seam_lines[seam_lines == 255] = color
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return panorama_with_seam_lines
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@staticmethod
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def exctract_seam_lines(blended_seam_masks, linesize=1):
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seam_lines = cv.Canny(np.uint8(blended_seam_masks), 100, 200)
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seam_indices = (seam_lines == 255).nonzero()
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seam_lines = remove_invalid_line_pixels(
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seam_indices, seam_lines, blended_seam_masks
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)
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kernelsize = linesize + linesize - 1
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kernel = np.ones((kernelsize, kernelsize), np.uint8)
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return cv.dilate(seam_lines, kernel)
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@staticmethod
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def blend_seam_masks(seam_masks, corners, sizes, colors=[
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(255, 000, 000), # Blue
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(000, 000, 255), # Red
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(000, 255, 000), # Green
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(000, 255, 255), # Yellow
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(255, 000, 255), # Magenta
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(128, 128, 255), # Pink
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(128, 128, 128), # Gray
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(000, 000, 128), # Brown
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(000, 128, 255)] # Orange
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):
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blender = Blender("no")
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blender.prepare(corners, sizes)
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for idx, (seam_mask, size, corner) in enumerate(
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zip(seam_masks, sizes, corners)):
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if idx+1 > len(colors):
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raise ValueError("Not enough default colors! Pass additional "
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"colors to \"colors\" parameter")
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one_color_img = create_img_by_size(size, colors[idx])
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blender.feed(one_color_img, seam_mask, corner)
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return blender.blend()
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def create_img_by_size(size, color=(0, 0, 0)):
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width, height = size
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img = np.zeros((height, width, 3), np.uint8)
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img[:] = color
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return img
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def add_weighted_image(img1, img2, alpha):
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return cv.addWeighted(
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img1, alpha, img2, (1.0 - alpha), 0.0
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)
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def remove_invalid_line_pixels(indices, lines, mask):
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for x, y in zip(*indices):
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if check_if_pixel_or_neighbor_is_black(mask, x, y):
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lines[x, y] = 0
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return lines
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def check_if_pixel_or_neighbor_is_black(img, x, y):
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check = [is_pixel_black(img, x, y),
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is_pixel_black(img, x+1, y), is_pixel_black(img, x-1, y),
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is_pixel_black(img, x, y+1), is_pixel_black(img, x, y-1)]
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return any(check)
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def is_pixel_black(img, x, y):
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return np.all(get_pixel_value(img, x, y) == 0)
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def get_pixel_value(img, x, y):
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try:
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return img[x, y]
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except IndexError:
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pass
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