import numpy as np import cv2 from multiprocessing import Pool import multiprocessing import random def bruit(image_orig): h, w, c=image_orig.shape n=np.random.randn(h, w, c)*random.randint(5, 30) return np.clip(image_orig+n, 0, 255).astype(np.uint8) def change_gamma(image, alpha=1.0, beta=0.0): return np.clip(alpha*image+beta, 0, 255).astype(np.uint8) def modif_img(img): h, w, c=img.shape r_color=[np.random.randint(255), np.random.randint(255), np.random.randint(255)] img=np.where(img==[142, 142, 142], r_color, img).astype(np.uint8) if np.random.randint(3): k_max=3 kernel_blur=np.random.randint(k_max)*2+1 img=cv2.GaussianBlur(img, (kernel_blur, kernel_blur), 0) M=cv2.getRotationMatrix2D((int(w/2), int(h/2)), random.randint(-10, 10), 1) img=cv2.warpAffine(img, M, (w, h)) if np.random.randint(2): a=int(max(w, h)/5)+1 pts1=np.float32([[0, 0], [w, 0], [0, h], [w, h]]) pts2=np.float32([[0+random.randint(-a, a), 0+random.randint(-a, a)], [w-random.randint(-a, a), 0+random.randint(-a, a)], [0+random.randint(-a, a), h-random.randint(-a, a)], [w-random.randint(-a, a), h-random.randint(-a, a)]]) M=cv2.getPerspectiveTransform(pts1,pts2) img=cv2.warpPerspective(img, M, (w, h)) if np.random.randint(2): r=random.randint(0, 5) h2=int(h*0.9) w2=int(w*0.9) if r==0: img=img[0:w2, 0:h2] elif r==1: img=img[w-w2:w, 0:h2] elif r==2: img=img[0:w2, h-h2:h] elif r==3: img=img[w-w2:w, h-h2:h] img=cv2.resize(img, (h, w)) if np.random.randint(2): r=random.randint(1, int(max(w, h)*0.15)) img=img[r:w-r, r:h-r] img=cv2.resize(img, (h, w)) if not np.random.randint(4): t=np.empty((h, w, c) , dtype=np.float32) for i in range(h): for j in range(w): for k in range(c): t[i][j][k]=(i/h) M=cv2.getRotationMatrix2D((int(w/2), int(h/2)), np.random.randint(4)*90, 1) t=cv2.warpAffine(t, M, (w, h)) img=(cv2.multiply((img/255).astype(np.float32), t)*255).astype(np.uint8) img=change_gamma(img, random.uniform(0.6, 1.0), -np.random.randint(50)) if not np.random.randint(4): p=(15+np.random.randint(10))/100 img=(img*p+50*(1-p)).astype(np.uint8)+np.random.randint(100) img=bruit(img) return img def create_lot_img(image, nbr, nbr_thread=None): if nbr_thread is None: nbr_thread=multiprocessing.cpu_count() lot_original=np.repeat([image], nbr, axis=0) with Pool(nbr_thread) as p: lot_result=p.map(modif_img, lot_original) p.close() return lot_result