[CVCUDA] Vision Processor Python API and Tutorial (#1394)

* bind success

* bind success fix

* FDMat pybind, ResizeByShort pybind

* FDMat pybind, ResizeByShort pybind, remove initialized_

* override BindProcessorManager::Run in python is available

* PyProcessorManager done

* vision_pybind fix

* manager.py fix

* add tutorials

* remove Apply() bind

* remove Apply() bind and fix

* fix reviewed problem

* fix reviewed problem

* fix reviewed problem readme

* fix reviewed problem readme etc

* apply return outputs

* nits

* update readme

* fix FDMatbatch

* add op pybind: CenterCrop, Pad

* add op overload for pass FDMatBatch

---------

Co-authored-by: Wang Xinyu <shaywxy@gmail.com>

tutorials/vision_processor/python/README.md

@@ -0,0 +1,19 @@
+English | [中文](README_CN.md)
+
+# Preprocessor Python Demo
+
+1. [build FastDeploy（Python）](../../../docs/cn/build_and_install), or download[FastDeploy prebuilt library（Python）](../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
+
+2. Run the Demo
+```bash
+# Download the test image
+wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+
+# Run the Demo
+
+# OpenCV
+python preprocess.py
+
+# CV-CUDA
+python preprocess.py --use_cvcuda True
+```

tutorials/vision_processor/python/README_CN.md

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+中文 | [English](README.md)
+
+# Preprocessor Python 示例代码
+
+1. [编译FastDeploy（Python）](../docs/cn/build_and_install), 或直接下载[FastDeploy预编译库（Python）](../docs/cn/build_and_install/download_prebuilt_libraries.md)
+
+2. 运行示例代码
+```bash
+# 下载测试图片
+wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+
+# 运行示例代码
+
+# OpenCV
+python preprocess.py
+
+# CV-CUDA
+python preprocess.py --use_cvcuda True
+```

tutorials/vision_processor/python/preprocess.py

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+import fastdeploy as fd
+import cv2
+
+from fastdeploy.vision.common.manager import PyProcessorManager
+
+
+def parse_arguments():
+    import argparse
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--use_cvcuda",
+        required=False,
+        type=bool,
+        help="Use CV-CUDA in preprocess")
+    return parser.parse_args()
+
+
+# define CustomProcessor
+class CustomProcessor(PyProcessorManager):
+    def __init__(self) -> None:
+        super().__init__()
+        # create op
+        hw = [500, 500]
+        self.resize_op = fd.C.vision.processors.ResizeByShort(100, 1, True, hw)
+
+        mean = [0.485, 0.456, 0.406]
+        std = [0.229, 0.224, 0.225]
+        is_scale = True
+        min = []
+        max = []
+        swap_rb = False
+        self.normalize_permute_op = fd.C.vision.processors.NormalizeAndPermute(
+            mean, std, is_scale, min, max, swap_rb)
+
+        width = 50
+        height = 50
+        self.centercrop_op = fd.C.vision.processors.CenterCrop(width, height)
+
+        top = 5
+        bottom = 5
+        left = 5
+        right = 5
+        pad_value = [225, 225, 225]
+        self.pad_op = fd.C.vision.processors.Pad(top, bottom, left, right,
+                                                 pad_value)
+
+    def apply(self, image_batch):
+        outputs = []
+        self.resize_op(image_batch)
+        self.centercrop_op(image_batch)
+        self.pad_op(image_batch)
+        self.normalize_permute_op(image_batch)
+
+        for i in range(len(image_batch.mats)):
+            outputs.append(image_batch.mats[i])
+
+        return outputs
+
+
+if __name__ == "__main__":
+
+    # read jpg
+    im1 = cv2.imread('ILSVRC2012_val_00000010.jpeg')
+    im2 = cv2.imread('ILSVRC2012_val_00000010.jpeg')
+
+    mat1 = fd.C.vision.FDMat()
+    mat1.from_numpy(im1)
+    mat2 = fd.C.vision.FDMat()
+    mat2.from_numpy(im2)
+    images = [mat1, mat2]
+
+    args = parse_arguments()
+    # creae processor
+    preprocessor = CustomProcessor()
+
+    # use CV-CUDA
+    if args.use_cvcuda:
+        preprocessor.use_cuda(True, -1)
+
+    # show input
+    for i in range(len(images)):
+        images[i].print_info('images' + str(i) + ': ')
+
+    # run the Processer with CVCUDA
+    outputs = preprocessor(images)
+
+    # show output
+    for i in range(len(outputs)):
+        outputs[i].print_info('outputs' + str(i) + ': ')