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Opt: [ALAS] Improve memory performance in utils

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LmeSzinc 2024-04-30 17:22:36 +08:00
parent 6a8a7d18c8
commit 74a2ccf2dc
1 changed files with 70 additions and 19 deletions
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89
module/base/utils/utils.py
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@ -625,17 +625,29 @@ def image_paste(image, background, origin):
def rgb2gray(image): def rgb2gray(image):
""" """
gray = ( MAX(r, g, b) + MIN(r, g, b)) / 2
Args: Args:
image (np.ndarray): Shape (height, width, channel) image (np.ndarray): Shape (height, width, channel)
Returns: Returns:
np.ndarray: Shape (height, width) np.ndarray: Shape (height, width)
""" """
# r, g, b = cv2.split(image)
# return cv2.add(
# cv2.multiply(cv2.max(cv2.max(r, g), b), 0.5),
# cv2.multiply(cv2.min(cv2.min(r, g), b), 0.5)
# )
r, g, b = cv2.split(image) r, g, b = cv2.split(image)
return cv2.add( maximum = cv2.max(r, g)
cv2.multiply(cv2.max(cv2.max(r, g), b), 0.5), cv2.max(maximum, b, dst=maximum)
cv2.multiply(cv2.min(cv2.min(r, g), b), 0.5) cv2.convertScaleAbs(maximum, alpha=0.5, dst=maximum)
) cv2.min(r, g, dst=r)
cv2.min(r, b, dst=r)
cv2.convertScaleAbs(r, alpha=0.5, dst=r)
# minimum = r
cv2.add(maximum, r, dst=maximum)
return maximum
def rgb2hsv(image): def rgb2hsv(image):
@ -791,11 +803,24 @@ def color_similarity_2d(image, color):
Returns: Returns:
np.ndarray: uint8 np.ndarray: uint8
""" """
r, g, b = cv2.split(cv2.subtract(image, (*color, 0))) # r, g, b = cv2.split(cv2.subtract(image, (*color, 0)))
positive = cv2.max(cv2.max(r, g), b) # positive = cv2.max(cv2.max(r, g), b)
r, g, b = cv2.split(cv2.subtract((*color, 0), image)) # r, g, b = cv2.split(cv2.subtract((*color, 0), image))
negative = cv2.max(cv2.max(r, g), b) # negative = cv2.max(cv2.max(r, g), b)
return cv2.subtract(255, cv2.add(positive, negative)) # return cv2.subtract(255, cv2.add(positive, negative))
diff = cv2.subtract(image, (*color, 0))
r, g, b = cv2.split(diff)
cv2.max(r, g, dst=r)
cv2.max(r, b, dst=r)
positive = r
cv2.subtract((*color, 0), image, dst=diff)
r, g, b = cv2.split(diff)
cv2.max(r, g, dst=r)
cv2.max(r, b, dst=r)
negative = r
cv2.add(positive, negative, dst=positive)
cv2.subtract(255, positive, dst=positive)
return positive
def extract_letters(image, letter=(255, 255, 255), threshold=128): def extract_letters(image, letter=(255, 255, 255), threshold=128):
@ -809,11 +834,24 @@ def extract_letters(image, letter=(255, 255, 255), threshold=128):
Returns: Returns:
np.ndarray: Shape (height, width) np.ndarray: Shape (height, width)
""" """
r, g, b = cv2.split(cv2.subtract(image, (*letter, 0))) # r, g, b = cv2.split(cv2.subtract(image, (*letter, 0)))
positive = cv2.max(cv2.max(r, g), b) # positive = cv2.max(cv2.max(r, g), b)
r, g, b = cv2.split(cv2.subtract((*letter, 0), image)) # r, g, b = cv2.split(cv2.subtract((*letter, 0), image))
negative = cv2.max(cv2.max(r, g), b) # negative = cv2.max(cv2.max(r, g), b)
return cv2.multiply(cv2.add(positive, negative), 255.0 / threshold) # return cv2.multiply(cv2.add(positive, negative), 255.0 / threshold)
diff = cv2.subtract(image, (*letter, 0))
r, g, b = cv2.split(diff)
cv2.max(r, g, dst=r)
cv2.max(r, b, dst=r)
positive = r
cv2.subtract((*letter, 0), image, dst=diff)
r, g, b = cv2.split(diff)
cv2.max(r, g, dst=r)
cv2.max(r, b, dst=r)
negative = r
cv2.add(positive, negative, dst=positive)
cv2.convertScaleAbs(positive, alpha=255.0 / threshold, dst=positive)
return positive
def extract_white_letters(image, threshold=128): def extract_white_letters(image, threshold=128):
@ -827,10 +865,21 @@ def extract_white_letters(image, threshold=128):
Returns: Returns:
np.ndarray: Shape (height, width) np.ndarray: Shape (height, width)
""" """
# minimum = cv2.min(cv2.min(r, g), b)
# maximum = cv2.max(cv2.max(r, g), b)
# return cv2.multiply(cv2.add(maximum, cv2.subtract(maximum, minimum)), 255.0 / threshold)
r, g, b = cv2.split(cv2.subtract((255, 255, 255, 0), image)) r, g, b = cv2.split(cv2.subtract((255, 255, 255, 0), image))
minimum = cv2.min(cv2.min(r, g), b) maximum = cv2.max(r, g)
maximum = cv2.max(cv2.max(r, g), b) cv2.max(maximum, b, dst=maximum)
return cv2.multiply(cv2.add(maximum, cv2.subtract(maximum, minimum)), 255.0 / threshold) cv2.convertScaleAbs(maximum, alpha=0.5, dst=maximum)
cv2.min(r, g, dst=r)
cv2.min(r, b, dst=r)
cv2.convertScaleAbs(r, alpha=0.5, dst=r)
minimum = r
cv2.subtract(maximum, minimum, dst=minimum)
cv2.add(maximum, minimum, dst=maximum)
cv2.convertScaleAbs(maximum, alpha=255.0 / threshold, dst=maximum)
return maximum
def color_mapping(image, max_multiply=2): def color_mapping(image, max_multiply=2):
@ -849,7 +898,9 @@ def color_mapping(image, max_multiply=2):
low, high = np.min(image), np.max(image) low, high = np.min(image), np.max(image)
multiply = min(255 / (high - low), max_multiply) multiply = min(255 / (high - low), max_multiply)
add = (255 - multiply * (low + high)) / 2 add = (255 - multiply * (low + high)) / 2
image = cv2.add(cv2.multiply(image, multiply), add) # image = cv2.add(cv2.multiply(image, multiply), add)
cv2.multiply(image, multiply, dst=image)
cv2.add(image, add, dst=image)
image[image > 255] = 255 image[image > 255] = 255
image[image < 0] = 0 image[image < 0] = 0
return image.astype(np.uint8) return image.astype(np.uint8)
@ -909,7 +960,7 @@ def color_bar_percentage(image, area, prev_color, reverse=False, starter=0, thre
Returns: Returns:
float: 0 to 1. float: 0 to 1.
""" """
image = crop(image, area) image = crop(image, area, copy=False)
image = image[:, ::-1, :] if reverse else image image = image[:, ::-1, :] if reverse else image
length = image.shape[1] length = image.shape[1]
prev_index = starter prev_index = starter