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15 def _predict(self, X, **kwargs): 16 ''' 17 Keras returns class tuples (proba equivalent) so cast to single prediction 18 ''' 19 return np.argmax(self.external_model.predict(X), axis=1)
31 def predict(args): 32 # load the trained convolutional neural network 33 print("[INFO] loading network...") 34 model = load_model(args["model"]) 35 stride = args['stride'] 36 for n in range(len(TEST_SET)): 37 path = TEST_SET[n] 38 #load the image 39 image = cv2.imread('./test/' + path) 40 # pre-process the image for classification 41 #image = image.astype("float") / 255.0 42 #image = img_to_array(image) 43 h,w,_ = image.shape 44 padding_h = (h//stride + 1) * stride 45 padding_w = (w//stride + 1) * stride 46 padding_img = np.zeros((padding_h,padding_w,3),dtype=np.uint8) 47 padding_img[0:h,0:w,:] = image[:,:,:] 48 padding_img = padding_img.astype("float") / 255.0 49 padding_img = img_to_array(padding_img) 50 print 'src:',padding_img.shape 51 mask_whole = np.zeros((padding_h,padding_w),dtype=np.uint8) 52 for i in range(padding_h//stride): 53 for j in range(padding_w//stride): 54 crop = padding_img[:3,i*stride:i*stride+image_size,j*stride:j*stride+image_size] 55 _,ch,cw = crop.shape 56 if ch != 256 or cw != 256: 57 print 'invalid size!' 58 continue 59 60 crop = np.expand_dims(crop, axis=0) 61 #print 'crop:',crop.shape 62 pred = model.predict_classes(crop,verbose=2) 63 pred = labelencoder.inverse_transform(pred[0]) 64 #print (np.unique(pred)) 65 pred = pred.reshape((256,256)).astype(np.uint8) 66 #print 'pred:',pred.shape 67 mask_whole[i*stride:i*stride+image_size,j*stride:j*stride+image_size] = pred[:,:] 68 69 70 cv2.imwrite('./predict/pre'+str(n+1)+'.png',mask_whole[0:h,0:w])
47 @abstractmethod 48 def predict(self, x_test): 49 """Return the results for the testing data predicted by the best neural architecture. 50 51 Dependent on the results of the fit() function. 52 53 Args: 54 x_test: An instance of numpy.ndarray containing the testing data. 55 56 Returns: 57 A numpy.ndarray containing the predicted classes for x_test. 58 """ 59 pass
372 def predict(self, x, batch_size=None, verbose=0, steps=None): 373 target_bounding_boxes = numpy.zeros((x.shape[0], 1, 4)) 374 375 target_categories = numpy.zeros((x.shape[0], 1, self.n_categories)) 376 377 target_mask = numpy.zeros((1, 1, *self.mask_shape)) 378 379 target_metadata = numpy.array([[x.shape[1], x.shape[2], 1.0]]) 380 381 x = [ 382 target_bounding_boxes, 383 target_categories, 384 x, 385 target_mask, 386 target_metadata 387 ] 388 389 return super(RCNN, self).predict(x, batch_size, verbose, steps)