Solving OpenCV Error: Assertion failed (ssize.width ＞ 0 && ssize.height ＞ 0) in cv::resize, file C:\proj

Table of Contents

Solve OpenCV Error: Assertion failed (ssize.width > 0 & amp; & ssize.height > 0) in cv::resize, file C:\proj

1. Wrong image size

2. Data type error

3. Wrong number of image channels

4. Other possible reasons

Solution

Solve OpenCV Error: Assertion failed (ssize.width > 0 & amp; & amp; ssize.height > 0) in cv::resize, file C:\proj

When we use OpenCV for image processing, we sometimes encounter the following error message: OpenCV Error: Assertion failed (ssize.width > 0 & amp; & amp; ssize.height > 0) in cv::resize, file C :\proj. This error is usually caused by the image having a width or height of 0. In this article, I’ll go over some of the possible causes of this error and provide some solutions.

1. Wrong image size

One common reason is that there is a problem with the dimensions of the input image when calling the cv::resize function. Possible reasons include:

Image loading failure: When calling the ??cv::imread?? function, the image may not be loaded successfully, resulting in an image size of 0. We can verify that the image was loaded successfully by checking if the image object is empty.
Wrong image path: When calling the ??cv::imread?? function, an incorrect image path may be provided, causing the image to fail to load. We should make sure the path is correct.
Corrupted image data: In some cases, image files may be corrupted or malformed, causing loading to fail. We can try using other image files for testing, or open the file using image editing software to confirm whether the image is intact.

2. Data type error

Another cause of errors is that the input image has an incorrect data type. The ??cv::resize?? function requires the data type of the input image to be ??CV_8U??, ??CV_16U??,? One of CV_32F or CV_64F. If the image’s data type does not match these types, it will cause the above error. We can check the data type of the image by calling the ??cv::Mat::type()?? function and use ??cv::Mat::convertTo?? function to convert it to the correct data type.

3. The number of image channels is wrong

The ??cv::resize?? function will process each channel of the image by default. In some cases, the input image may not have the required number of channels, causing an error. For example, if the input image is a grayscale image (single channel) but we try to do bilinear interpolation on it, we will get an error. We can check the number of channels of the image by calling the ??cv::Mat::channels()?? function and use ??cv::cvtColor? as needed ? function converts it to the correct number of channels.

4. Other possible reasons

In addition to the above reasons, there are some other reasons that may cause the error, such as:

Insufficient memory: When processing large images, there may be insufficient memory, causing errors. We can try reducing the size of the image, or perform a memory cleanup operation before processing the image.
OpenCV version incompatibility: Different versions of OpenCV may have differences in the behavior of certain functions, leading to incompatibility issues. We can try to update the OpenCV version and see if there are any relevant fixes or changes.

Solution

Based on the above possible causes, we can try the following solutions:

Check if the image loaded successfully and make sure the image path is correct.
Check that the image has the correct data type and perform the necessary conversion using the ??cv::Mat::convertTo?? function.
Check that the image has the correct number of channels and perform the necessary conversions using the ??cv::cvtColor?? function.
Optimize memory usage, reduce image size or perform memory cleaning operations.
Update the OpenCV version and see if there is a relevant solution or fix. With the above method, we should be able to solve the OpenCV Error: Assertion failed (ssize.width > 0 & amp; & amp; ssize.height > 0) in cv::resize error. Hope this article is helpful to you! If you have any questions or other questions, please feel free to ask.

When encountering OpenCV Error: Assertion failed (ssize.width > 0 & amp; & ssize.height > 0) in cv::resize, file C:\proj error, we can check the size and data type of the image, and handle accordingly. Here is a sample code from a real-life scenario to resolve this error and resize the image:

pythonCopy codeimport cv2
def resize_image(image_path, new_width, new_height):
    # read image
    image = cv2.imread(image_path)
    
    # Check whether the image is read successfully
    if image is None:
        print("Unable to load image!")
        return
    
    # Check image size
    if image.shape[0] == 0 or image.shape[1] == 0:
        print("Image size is wrong!")
        return
    
    # Check image data type and convert
    if image.dtype != 'uint8':
        image = image.astype('uint8')
    
    #Adjust image size
    resized_image = cv2.resize(image, (new_width, new_height))
    
    # Display original image and adjusted image
    cv2.imshow('Original Image', image)
    cv2.imshow('Resized Image', resized_image)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
# Call example
image_path = 'path/to/your/image.jpg'
new_width = 400
new_height = 300
resize_image(image_path, new_width, new_height)

In this example, we first read the image using the ??cv2.imread?? function. Then, we check the image’s dimensions and data type. If the image fails to load or is of incorrect dimensions, we output an error message accordingly and end the function. If the data type of the image is not ??uint8??, we convert it to ??uint8??type to conform to ??cv2.resize?? function requirements. Finally, we resize the image using the cv2.resize function and display the original and resized images using the cv2.imshow function.

The ??cv::resize?? function is a function used to resize images in OpenCV. It can scale, enlarge or crop the image according to the specified target size. The prototype of the function is as follows:

cppCopy codevoid resize(InputArray src, OutputArray dst, Size dsize, double fx = 0, double fy = 0, int interpolation = INTER_LINEAR)

Parameter Description:

??src??: Input image, which can be a single-channel or multi-channel image. The data type can be ??CV_8U??, ??CV_16U? ?, ??CV_16S??, ??CV_32F?? or ??CV_64F??.
??dst??: Output image with size and depth matching the target size.
??dsize??: The size of the target image, which can be specified by ??Size(width, height)??or ??Size(scale\_factor\ _width, scale\_factor\_height)?? to define. If ??dsize?? is ??(0,0)?? and ??fx?? and ??fy ?? is not 0, then the target size is calculated by the scaling factor.
??fx??: Scaling factor in the horizontal direction.
??fy??: scaling factor in the vertical direction.
?interpolation??: Flag of the interpolation method. Commonly used interpolation methods are:

??INTER_NEAREST??: Nearest neighbor interpolation.
??INTER_LINEAR??: Bilinear interpolation.
??INTER_CUBIC??: Bicubic interpolation.
??INTER_AREA??: Pixel area resampling. The ??cv::resize?? function performs corresponding scaling operations on the input image according to the given target size or scaling factor. Specifically, if a target size is used, it is scaled by the specified size; if a scaling factor is used, the size of the input image is multiplied by the scaling factor to get the target size. The interpolation method controls how new pixel values are calculated so that they fit into the new dimensions. Finally, the results are stored in the output image??dst??. The ??cv::resize?? function is widely used in image processing, computer vision, machine learning and other fields. For example, in image classification tasks, it is often necessary to uniformly adjust images to a fixed size in order to facilitate input into the classification model. In addition, this function is often used in tasks such as image augmentation, image thumbnailing, and image contour extraction.

The knowledge points of the article match the official knowledge files, and you can further learn related knowledge. OpenCV skill treeHomepageOverview 23627 people are learning the system