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Opt: Faster color_similarity_2d

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LmeSzinc
2020-07-22 00:58:48 +08:00
parent 232f56be26
commit a64dbb5837
1 changed files with 11 additions and 8 deletions
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19
module/base/utils.py
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@@ -1,3 +1,4 @@
import cv2
import numpy as np import numpy as np
from PIL import ImageStat from PIL import ImageStat
@@ -299,10 +300,11 @@ def color_similarity_2d(image, color):
Returns: Returns:
np.ndarray: uint8 np.ndarray: uint8
""" """
diff = np.array(image).astype(int) - color r, g, b = cv2.split(cv2.subtract(image, (*color, 0)))
diff = 255 - np.max(np.maximum(diff, 0), axis=2) + np.min(np.minimum(diff, 0), axis=2) positive = cv2.max(cv2.max(r, g), b)
image = np.clip(diff, 0, 255).astype(np.uint8) r, g, b = cv2.split(cv2.subtract((*color, 0), image))
return image negative = cv2.max(cv2.max(r, g), b)
return cv2.subtract(255, cv2.add(positive, negative))
def extract_letters(image, letter=(255, 255, 255), threshold=128): def extract_letters(image, letter=(255, 255, 255), threshold=128):
@@ -316,10 +318,11 @@ def extract_letters(image, letter=(255, 255, 255), threshold=128):
Returns: Returns:
np.ndarray: Shape (height, width) np.ndarray: Shape (height, width)
""" """
image = color_similarity_2d(np.array(image), color=letter) r, g, b = cv2.split(cv2.subtract(image, (*letter, 0)))
image = (255.0 - image) * (255.0 / threshold) positive = cv2.max(cv2.max(r, g), b)
image = np.clip(image, 0, 255).astype(np.uint8) r, g, b = cv2.split(cv2.subtract((*letter, 0), image))
return image negative = cv2.max(cv2.max(r, g), b)
return cv2.multiply(cv2.add(positive, negative), 255.0 / threshold)
def red_overlay_transparency(color1, color2, red=247): def red_overlay_transparency(color1, color2, red=247):