Home » Python » python – ValueError: cannot reshape array of size 1251936 into shape (1118,1118)-Exceptionshub

python – ValueError: cannot reshape array of size 1251936 into shape (1118,1118)-Exceptionshub

Posted by: admin February 24, 2020 Leave a comment

Questions:

It may seem a frequently asked question, but the thing is that I understand this error, but to get 1251936 with a shape of dimension 2, it would require (1118.89945929024,1118.89945929024) but this number must be an integer, then 1118. Which give 1249924, and that’s a problem.

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)

How to&Answers:

You error is connected to line:

predictions = model.predict(features.reshape(-1, features.shape[1]))

Here your array predictions is not square array and has the size 1251936.
That is why when you do

pred_size = int(math.sqrt(features.shape[0]))
inference_img = predictions.reshape(pred_size, pred_size)

you have error. So you should check the shapes before doing reshape. I can guess that the source of error is adding borders. So check the border sizes.
You are adding borders here:

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])