              crop_number = 0
              predict_batch = []
              for rect in rectangles:
                  crop_img = image[int(rect[1]):int(rect[3]), int(rect[0]):int(rect[2])]
                  scale_img = cv2.resize(crop_img, (48, 48))  # pylint:disable=no-member
                  predict_batch.append(scale_img)
                  crop_number += 1
              predict_batch = np.array(predict_batch)
              output = self.onet.predict(predict_batch, batch_size=128)
              cls_prob = output[0]
 
</s> remove                 scale_img = cv2.resize(crop_img, (24, 24))  # pylint:disable=no-member
 </s> add                 scale_img = cv2.resize(crop_img, (24, 24)) </s> remove                 batch[idx, ...] = cv2.resize(images[idx, ...],  # pylint:disable=no-member
                                             (rwidth, rheight))
 </s> add                 batch[idx, ...] = cv2.resize(images[idx, ...], (rwidth, rheight)) </s> remove         retval = cv2.copyMakeBorder(image.copy(),  # pylint: disable=no-member
 </s> add         retval = cv2.copyMakeBorder(image.copy(), </s> remove         font = cv2.FONT_HERSHEY_SIMPLEX  # pylint: disable=no-member
 </s> add         font = cv2.FONT_HERSHEY_SIMPLEX
                scale_img = cv2.resize(crop_img, (48, 48))
              crop_number = 0
              predict_batch = []
              for rect in rectangles:
                  crop_img = image[int(rect[1]):int(rect[3]), int(rect[0]):int(rect[2])]
                scale_img = cv2.resize(crop_img, (48, 48))
                  predict_batch.append(scale_img)
                  crop_number += 1
              predict_batch = np.array(predict_batch)
              output = self.onet.predict(predict_batch, batch_size=128)
              cls_prob = output[0]
keep keep keep keep replace keep keep keep keep keep
 # pylint:disable=no-member cleanup (#927)

# pylint:disable=no-member cleanup 
plugins/extract/detect/mtcnn.py