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"""OpenCV Correct Transforms."""
import math
import cv2
import numpy as np
from third.opencv.color_transfer import color_transfer
from transform.opencv import ImageTransformOpenCV
class DressToCorrect(ImageTransformOpenCV):
"""Dress -> Correct [OPENCV]."""
def _execute(self, *args):
"""
Execute dress to correct phase.
:param args: <[RGB]> Image to correct
:return: <RGB> image corrected
"""
return self.correct_color(args[0], 5)
@staticmethod
def correct_color(img, percent):
"""
Correct the color of an image.
:param img: <RGB> Image to correct
:param percent: <int> Percent of correction (1-100)
:return <RGB>: image corrected
"""
if img.shape[2] != 3:
raise AssertionError()
if not 0 < percent <= 100:
raise AssertionError()
half_percent = percent / 200.0
channels = cv2.split(img)
out_channels = []
for channel in channels:
if len(channel.shape) != 2:
raise AssertionError()
# find the low and high precentile values (based on the input percentile)
height, width = channel.shape
vec_size = width * height
flat = channel.reshape(vec_size)
if len(flat.shape) != 1:
raise AssertionError()
flat = np.sort(flat)
n_cols = flat.shape[0]
low_val = flat[math.floor(n_cols * half_percent)]
high_val = flat[math.ceil(n_cols * (1.0 - half_percent))]
# saturate below the low percentile and above the high percentile
thresholded = DressToCorrect.apply_threshold(channel, low_val, high_val)
# scale the channel
normalized = cv2.normalize(thresholded, thresholded.copy(), 0, 255, cv2.NORM_MINMAX)
out_channels.append(normalized)
return cv2.merge(out_channels)
@staticmethod
def apply_threshold(matrix, low_value, high_value):
"""
Apply a threshold on a matrix.
:param matrix: <array> matrix
:param low_value: <float> low value
:param high_value: <float> high value
:return: None
"""
low_mask = matrix < low_value
matrix = DressToCorrect.apply_mask(matrix, low_mask, low_value)
high_mask = matrix > high_value
matrix = DressToCorrect.apply_mask(matrix, high_mask, high_value)
return matrix
@staticmethod
def apply_mask(matrix, mask, fill_value):
"""
Apply a mask on a matrix.
:param matrix: <array> matrix
:param mask: <RGB> image mask
:param fill_value: <> fill value
:return: None
"""
masked = np.ma.array(matrix, mask=mask, fill_value=fill_value)
return masked.filled()
class ColorTransfer(ImageTransformOpenCV):
"""ColorTransfer [OPENCV]."""
def __init__(self, input_index=(0, -1)):
"""
Color Transfer constructor.
:param input_index: <tuple> index where to take the inputs (default is (0,-1)
for first and previous transformation)
:param args: <dict> args parameter to run the image transformation (default use Conf.args)
"""
super().__init__(input_index=input_index)
def _execute(self, *args):
"""
Transfers the color distribution from the source to the target.
:param args: <[RGB,RGB]> Image source, Image target
:return: <RGB> Color transfer image
"""
return color_transfer(args[0], args[1], clip=True, preserve_paper=False)