这似乎是一个经常被问到的问题,但问题是我理解了这个错误,但是要获得尺寸为2的形状的1251936,则需要(1118.89945929024,1118.89945929024),但此数字必须是整数,然后是1118。 1249924,这是一个问题。
def create_features(img):
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
features, _ = train.create_features(img, img_gray, label=None, train=False)
return features
def compute_prediction(img, model):
border = 5 # (haralick neighbourhood - 1) / 2
img = cv2.copyMakeBorder(img, top=border, bottom=border, \
left=border, right=border, \
borderType = cv2.BORDER_CONSTANT, \
value=[0, 0, 0])
features = create_features(img)
predictions = model.predict(features.reshape(-1, features.shape[1]))
pred_size = int(math.sqrt(features.shape[0]))
inference_img = predictions.reshape(pred_size, pred_size)
return inference_img
def infer_images(image_dir, model_path, output_dir):
filelist = glob(os.path.join(image_dir,'*.png'))
print ('[INFO] Running inference on %s test images' %len(filelist))
model = pkl.load(open( model_path, "rb" ) )
for file in filelist:
print ('[INFO] Processing images:', os.path.basename(file))
inference_img = compute_prediction(cv2.imread(file, 1), model)
cv2.imwrite(os.path.join(output_dir, os.path.basename(file)), inference_img)
最佳答案
您的错误已连接到线路:predictions = model.predict(features.reshape(-1, features.shape[1]))
在这里,您的数组predictions
不是正方形数组,大小为1251936
。
这就是为什么当你这样做
pred_size = int(math.sqrt(features.shape[0]))
inference_img = predictions.reshape(pred_size, pred_size)
你有错误。因此,您应该在进行重塑之前检查形状。我可以猜测错误的来源是添加了边框。因此,请检查边框尺寸。
您在此处添加边框:
border = 5 # (haralick neighbourhood - 1) / 2
img = cv2.copyMakeBorder(img, top=border, bottom=border, \
left=border, right=border, \
borderType = cv2.BORDER_CONSTANT, \
value=[0, 0, 0])