58 lines
2.2 KiB
Python
58 lines
2.2 KiB
Python
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import sys
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import cv2
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import os
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import numpy as np
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import math
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import random
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def facial_recognition_augmentation(label):
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aug = 5
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image_path = sys.path[0] + '/Facial_images/face_rec/train/' + label + '/'
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imagelist = os.listdir(image_path)
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for file_name in imagelist:
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(nameWithoutExtention, extention) = os.path.splitext(os.path.basename(file_name))
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if (extention==".jpg"):
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#print(image_path+file_name)
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inputImage = cv2.imread(image_path+file_name)
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for i in range(aug):
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rotate = random.randint(-8,8)
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brightness = random.randint(-30, +30)
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clahe = random.uniform(0.0, 1.0)
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choice = random.randint(0, 10)
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(h, w) = inputImage.shape[:2] #10
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center = (w // 2, h // 2) #11
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M = cv2.getRotationMatrix2D(center, rotate, 1.0) #12
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inputImage = cv2.warpAffine(inputImage, M, (w, h)) #13
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if (choice%3 == 1):
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clahe = cv2.createCLAHE(clipLimit=clahe, tileGridSize=(8,8))
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inputImage = cv2.cvtColor(inputImage, cv2.COLOR_BGR2LAB) # convert from BGR to LAB color space
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l, a, b = cv2.split(inputImage) # split on 3 different channels
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l2 = clahe.apply(l) # apply CLAHE to the L-channel
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inputImage = cv2.merge((l2,a,b)) # merge channels
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inputImage = cv2.cvtColor(inputImage, cv2.COLOR_LAB2BGR) # convert from LAB to BGR
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elif(choice%3 == 2):
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fI = inputImage/255.0
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gamma = random.uniform(0.2, 0.4)
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#inputImage = np.power(fI, gamma)
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else:
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cv2.addWeighted( inputImage, 0.5, inputImage, 0.5, brightness, inputImage)
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choice_1 = random.randint(0, 10)
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if (choice_1%2 == 1):
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gauss = random.randint(1, 2)*2-1
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inputImage = cv2.GaussianBlur(inputImage, (gauss,gauss), 0)
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cv2.imwrite(image_path+nameWithoutExtention+"_aug"+str(i)+".jpg", inputImage)
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