Resizes the input image and outputs it to the Image object.
For details about the API, see resize.
Before calling the resizing APIs, prepare the image object to be resized.

Key steps are demonstrated as follows:
Construct the ImageProcessor object. You need to specify the device ID during the construction.
Decode the image based on services to generate an Image object that can be resized. Subsequently, the image can be processed by using the image processing APIs to generate a final Image object to be resized.
Construct the corresponding input Size and output Image based on service requirements.
The following is a code example of key steps of functions and features, which is for reference only and cannot be directly copied for execution.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | from mindx.sdk import base from mindx.sdk.base import ImageProcessor, Size, Image def process(): # Image decoding # Initialize the ImageProcessor object. imageProcessor = ImageProcessor(device_id) image_path = "image_data/test_image.jpg" # Read the image path for decoding. The decoding format is nv12 (YUV_SP_420). decoded_image = imageProcessor.decode(image_path, base.nv12) # Resize an image. # Resizing size size_para = Size(224, 224) # Resize the decoded Image class by size. The resizing mode is the high-order filtering algorithm developed by Huawei (huaweiu_high_order_filter). resized_image = imageProcessor.resize(decoded_image, size_para, base.huaweiu_high_order_filter) if __name__ == "__main__": base.mx_init() # Initialize resources. process() base.mx_deinit() # Deinitialize resources. |