[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>
2025-10-06 17:17:14 +08:00 · 2023-03-10 14:42:32 +08:00
parent cb7c8a07d4
commit c6480de736
22 changed files with 530 additions and 34 deletions
--- a/tutorials/vision_processor/python/preprocess.py
+++ b/tutorials/vision_processor/python/preprocess.py
@@ -0,0 +1,89 @@
+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) + ': ')