对图像进行数据增强

这是原始图像：
　　　　

 

1. 改变图像的光照程度。
   

   def transform_ei(img_arr):
       def gamma_transform(im_array, gamma):
           res = exposure.adjust_gamma(im_array,gamma)
           return res
       
       low0 = 0.1
       high0 = 0.7
       low1 = 3 
       high1 = 5
       gamma0 = rand.uniform(low0, high0)
       gamma1 = rand.uniform(low1, high1)
       gamma = rand.choice([gamma0, gamma1])
       res = gamma_transform(img_arr,gamma)
       return res

   测试代码为：

   if __name__ == '__main__':
       img = Image.open('./iu.jpeg')
       img_arr = np.asarray(img)
       # res, _ = transform_mb(img_arr, img_arr)
       res = transform_ei(img_arr)
       im = Image.fromarray(res)
       im.save('iu1.jpeg')

   改变后的效果：

   
2. 在图像上增加遮挡。
   

   def transform_occ(img_arr):
       occ_obj_w = rand.randrange(1, 200)
       occ_obj_h = rand.randrange(1, 200)
       x_center = rand.randrange(1, 200)
       y_center = rand.randrange(1, 200)
       temp = np.array([rand.randrange(-10,10),rand.randrange(-10,10),rand.randrange(-10,10)],dtype=int)
       
       rgb_color = np.mean(img_arr, 0)
       random_color_rgb = np.mean(rgb_color, 0)
       random_color_rgb = random_color_rgb.astype(int)
       random_color_rgb = random_color_rgb - temp
       occ_obj = np.ones((occ_obj_h, occ_obj_w, 3), dtype=np.uint8) 
       for i in range(3):
           occ_obj[:,:,i] = occ_obj[:,:,i] * random_color_rgb[i]
       res = img_arr
       if res.shape[0] <= y_center + occ_obj_h // 2:
           diff = y_center + occ_obj_h - res.shape[0]
           occ_obj = occ_obj[0:occ_obj_h-diff,:]
           
       if res.shape[1] <= x_center + occ_obj_w // 2:
           diff = x_center + occ_obj_w - res.shape[1]
           occ_obj = occ_obj[:,0:occ_obj_w-diff]
       res[y_center:y_center+occ_obj_h,x_center:x_center+occ_obj_w] = occ_obj
       img_arr = res
       
       return img_arr

   测试代码：

   if __name__ == '__main__':
       img = Image.open('./iu.jpeg')
       img_arr = np.asarray(img)
       res = transform_occ(img_arr)
       im = Image.fromarray(res)
       im.save('iu1.jpeg')

   效果：
   
   

3. 增加图像的模糊效果。
   

   def transform_mb(img_arr):
       
       def motion_blur(im_array, length,angle):
           EPS = np.finfo(float).eps                                 
           alpha = (angle - math.floor(angle / 180) * 180) / 180 * math.pi
           cosalpha = math.cos(alpha)  
           sinalpha = math.sin(alpha)  
           if cosalpha < 0:
               xsign = -1
           elif angle == 90:
               xsign = 0
           else:  
               xsign = 1
           psfwdt = 1;  
           
           sx = int(math.fabs(length * cosalpha + psfwdt * xsign - length * EPS))  
           sy = int(math.fabs(length * sinalpha + psfwdt - length * EPS))
           sx = max(sx, 1)
           sy = max(sy, 1)
           psf1 = np.zeros((sy, sx))
           
           half = length / 2
           for i in range(0, sy):
               for j in range(0, sx):
                   psf1[i][j] = i * math.fabs(cosalpha) - j * sinalpha
                   rad = math.sqrt(i * i + j * j) 
                   if  rad >= half and math.fabs(psf1[i][j]) <= psfwdt:  
                       temp = half - math.fabs((j + psf1[i][j] * sinalpha) / cosalpha)  
                       psf1[i][j] = math.sqrt(psf1[i][j] * psf1[i][j] + temp * temp)
                   psf1[i][j] = psfwdt + EPS - math.fabs(psf1[i][j]);  
                   if psf1[i][j] < 0:
                       psf1[i][j] = 0
           
           anchor=(0,0)
           
           if angle < 90 and angle > 0:
               psf1 = np.fliplr(psf1)
               anchor = (psf1.shape[1] - 1, 0)
           elif angle < -90 :
               psf1 = np.flipud(psf1)
               psf1 = np.fliplr(psf1)
               anchor = (psf1.shape[1] - 1, psf1.shape[0] - 1)
           elif angle > -90 and angle < 0:
               psf1 = np.flipud(psf1)
               anchor = (0, psf1.shape[0] - 1)
           psf1 = psf1 / psf1.sum()
           res = cv2.filter2D(im_array, -1, psf1, anchor=anchor)
           return res
       
       length = rand.randrange(40,100)
       angle = rand.randrange(-179,179)
       img_arr = motion_blur(img_arr, length, angle)
       return img_arr

   测试代码：

   if __name__ == '__main__':
       img = Image.open('./iu.jpeg')
       img_arr = np.asarray(img)
       res = transform_mb(img_arr)
       im = Image.fromarray(res)
       im.save('iu1.jpeg')

   效果：