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gray_scale_functions.py
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from _image_editing_functions import *
def lightScale_Blue(img:np.ndarray,output_file_name:str) -> np.ndarray:
"""
This function uses light scale and changes the whole image's hue into Blue
Through interating the function. Must write outputFile Name in .png/jpg end.
"""
#Deep copy that image.
img_ = img.copy()
# img_ = numpy.array(img)
#Get images dimensions.
dimensions = img_.shape
# Get height width and channels.
height = dimensions[0]
width = dimensions[1]
smallestValue = 0
for i in range(height):
for j in range(width):
smallestValue = min(img_[i][j][0],img_[i][j][1],img_[i][j][2])
img_[i][j] = [smallestValue,0,0]
return outputFile(img_, output_file_name)
def lightScale_Green(img:np.ndarray,output_file_name:str) -> np.ndarray:
#Deep copy that image.
img_ = img.copy()
#Get images dimensions.
dimensions = img_.shape
# Get height width and channels.
height = dimensions[0]
width = dimensions[1]
smallestValue = 0
for i in range(height):
for j in range(width):
smallestValue = min(img_[i][j][0],img_[i][j][1],img_[i][j][2])
img_[i][j] = [0,smallestValue,0]
return outputFile(img_, output_file_name)
def lightScale_Red(img:np.ndarray,output_file_name:str) -> np.ndarray:
#Deep copy that image.
img_ = img.copy()
#Get images dimensions.
dimensions = img_.shape
# Get height width and channels.
height = dimensions[0]
width = dimensions[1]
smallestValue = 0
for i in range(height):
for j in range(width):
smallestValue = min(img_[i][j][0],img_[i][j][1],img_[i][j][2])
img_[i][j] = [0,0,smallestValue]
return outputFile(img_, output_file_name)
def lightScale_Cyan(img:np.ndarray,output_file_name:str) -> np.ndarray:
#Deep copy that image.
img_ = img.copy()
#Get images dimensions.
dimensions = img_.shape
# Get height width and channels.
height = dimensions[0]
width = dimensions[1]
smallestValue = 0
for i in range(height):
for j in range(width):
smallestValue = min(img_[i][j][0],img_[i][j][1],img_[i][j][2])
img_[i][j] = [smallestValue,smallestValue,0]
return outputFile(img_, output_file_name)
def lightScale_Pink(img:np.ndarray,output_file_name:str) -> np.ndarray:
#Deep copy that image.
img_ = img.copy()
#Get images dimensions.
dimensions = img_.shape
# Get height width and channels.
height = dimensions[0]
width = dimensions[1]
smallestValue = 0
for i in range(height):
for j in range(width):
smallestValue = min(img_[i][j][0],img_[i][j][1],img_[i][j][2])
img_[i][j] = [smallestValue,0,smallestValue]
return outputFile(img_, output_file_name)
def lightScale_Yellow(img:np.ndarray,output_file_name:str) -> np.ndarray:
#Deep copy that image.
img_ = img.copy()
#Get images dimensions.
dimensions = img_.shape
# Get height width and channels.
height = dimensions[0]
width = dimensions[1]
smallestValue = 0
for i in range(height):
for j in range(width):
smallestValue = min(img_[i][j][0],img_[i][j][1],img_[i][j][2])
img_[i][j] = [0,smallestValue,smallestValue]
return outputFile(img_, output_file_name)
def lightScale(img:np.ndarray,output_file_name:str) -> np.ndarray:
#Deep copy that image.
img_ = img.copy()
#Get images dimensions.
dimensions = img_.shape
# Get height width and channels.
height = dimensions[0]
width = dimensions[1]
smallestValue = 0
for i in range(height):
for j in range(width):
smallestValue = min(img_[i][j][0],img_[i][j][1],img_[i][j][2])
img_[i][j] = [smallestValue,smallestValue,smallestValue]
return outputFile(img_, output_file_name)
def darkScale(img:np.ndarray,output_file_name:str) -> np.ndarray:
#Deep copy that image.
img_ = img.copy()
#Get images dimensions.
dimensions = img_.shape
# Get height width and channels.
height = dimensions[0]
width = dimensions[1]
biggestValue = 255
for i in range(height):
for j in range(width):
biggestValue = max(img_[i][j][0],img_[i][j][1],img_[i][j][2])
img_[i][j] = [biggestValue,biggestValue,biggestValue]
return outputFile(img_, output_file_name)
def darkScale_Blue(img:np.ndarray,output_file_name:str) -> np.ndarray:
"""
This function uses light scale and changes the whole image's hue into Blue
Through interating the function. Must write outputFile Name in .png/jpg end.
"""
#Deep copy that image.
img_ = img.copy()
# img_ = numpy.array(img)
#Get images dimensions.
dimensions = img_.shape
# Get height width and channels.
height = dimensions[0]
width = dimensions[1]
biggestValue = 255
for i in range(height):
for j in range(width):
biggestValue = max(img_[i][j][0],img_[i][j][1],img_[i][j][2])
img_[i][j] = [biggestValue,0,0]
return outputFile(img_, output_file_name)
def darkScale_Green(img:np.ndarray,output_file_name:str) -> np.ndarray:
#Deep copy that image.
img_ = img.copy()
#Get images dimensions.
dimensions = img_.shape
# Get height width and channels.
height = dimensions[0]
width = dimensions[1]
biggestValue = 255
for i in range(height):
for j in range(width):
biggestValue = max(img_[i][j][0],img_[i][j][1],img_[i][j][2])
img_[i][j] = [0,biggestValue,0]
return outputFile(img_, output_file_name)
def darkScale_Red(img:np.ndarray,output_file_name:str) -> np.ndarray:
#Deep copy that image.
img_ = img.copy()
#Get images dimensions.
dimensions = img_.shape
# Get height width and channels.
height = dimensions[0]
width = dimensions[1]
biggestValue = 255
for i in range(height):
for j in range(width):
biggestValue = max(img_[i][j][0],img_[i][j][1],img_[i][j][2])
img_[i][j] = [0,0,biggestValue]
return outputFile(img_, output_file_name)
def darkScale_Cyan(img:np.ndarray,output_file_name:str) -> np.ndarray:
#Deep copy that image.
img_ = img.copy()
#Get images dimensions.
dimensions = img_.shape
# Get height width and channels.
height = dimensions[0]
width = dimensions[1]
biggestValue = 255
for i in range(height):
for j in range(width):
biggestValue = max(img_[i][j][0],img_[i][j][1],img_[i][j][2])
img_[i][j] = [biggestValue,biggestValue,0]
return outputFile(img_, output_file_name)
def darkScale_Pink(img:np.ndarray,output_file_name:str) -> np.ndarray:
#Deep copy that image.
img_ = img.copy()
#Get images dimensions.
dimensions = img_.shape
# Get height width and channels.
height = dimensions[0]
width = dimensions[1]
biggestValue = 255
for i in range(height):
for j in range(width):
biggestValue = max(img_[i][j][0],img_[i][j][1],img_[i][j][2])
img_[i][j] = [biggestValue,0,biggestValue]
return outputFile(img_, output_file_name)
def darkScale_Yellow(img:np.ndarray,output_file_name:str) -> np.ndarray:
#Deep copy that image.
img_ = img.copy()
#Get images dimensions.
dimensions = img_.shape
# Get height width and channels.
height = dimensions[0]
width = dimensions[1]
biggestValue = 255
for i in range(height):
for j in range(width):
biggestValue = max(img_[i][j][0],img_[i][j][1],img_[i][j][2])
img_[i][j] = [0,biggestValue,biggestValue]
return outputFile(img_, output_file_name)
def avgScale(img:np.ndarray,output_file_name:str) -> np.ndarray:
#Deep copy that image.
img_ = img.copy()
#Get images dimensions.
dimensions = img_.shape
# Get height width and channels.
height = dimensions[0]
width = dimensions[1]
avgValue = floor(255/3)
for i in range(height):
for j in range(width):
avgValue = floor((img_[i][j][0]+img_[i][j][1]+img_[i][j][2])/3)
img_[i][j] = [avgValue,avgValue,avgValue]
return outputFile(img_, output_file_name)
def avgImprovedScale(img:np.ndarray,output_file_name:str) -> np.ndarray:
"""
Same functionality as avg scale above but much more improved in terms of algorithm..
TODO: CHANGE?
"""
#Deep copy that image.
img_ = img.copy()
#Get images dimensions.
dimensions = img_.shape
# Get height width and channels.
height = dimensions[0]
width = dimensions[1]
avgValue = floor(255/3)
for i in range(height):
for j in range(width):
avgValue = int((img_[i][j][0]+img_[i][j][1]+img_[i][j][2])/3)
img_[i][j] = [avgValue,avgValue,avgValue]
return outputFile(img_, output_file_name)