Delete python/python

python/python/infer.py

@@ -1,50 +0,0 @@
-import argparse
-import cv2
-import numpy as np
-import axengine as axe
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--img",
-        type=str,
-        required=True,
-        help="Path to input image.",
-    )
-    parser.add_argument(
-        "--model",
-        type=str,
-        required=True,
-        help="Path to axmodel model.",
-    )
-    
-    return parser.parse_args()
-
-
-def infer(img: str, model: str, viz: bool = False):
-    img_raw = cv2.imread(img)
-    image = cv2.cvtColor(img_raw, cv2.COLOR_BGR2RGB) 
-    orig_h, orig_w = image.shape[:2]
-    image = cv2.resize(image, (518,518))
-    image = image[None]
-
-    session = axe.InferenceSession(model)
-    
-    depth = session.run(None, {"input": image})[0]
-
-    depth = cv2.resize(depth[0, 0], (orig_w, orig_h))
-    depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
-    depth = depth.astype(np.uint8)
-
-    depth_color = cv2.applyColorMap(depth, cv2.COLORMAP_INFERNO)
-
-    combined_result = cv2.hconcat([img_raw,  depth_color])
-            
-    cv2.imwrite("output-ax.png", combined_result)
-    
-    return depth
-
-
-if __name__ == "__main__":
-    args = parse_args()
-    infer(**vars(args))

python/python/infer_onnx.py

@@ -1,56 +0,0 @@
-import argparse
-import cv2
-import numpy as np
-import onnxruntime as ort
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--img",
-        type=str,
-        required=True,
-        help="Path to input image.",
-    )
-    parser.add_argument(
-        "--model",
-        type=str,
-        required=True,
-        help="Path to ONNX model.",
-    )
-    
-    return parser.parse_args()
-
-
-def infer(img: str, model: str, viz: bool = False):
-    img_raw = cv2.imread(img)
-    image = cv2.cvtColor(img_raw, cv2.COLOR_BGR2RGB) 
-    orig_h, orig_w = image.shape[:2]
-    image = cv2.resize(image, (518,518) )
-    mean = np.array([123.675, 116.28, 103.53],dtype=np.float32).reshape(1,1,3)
-    std = np.array([58.395, 57.12, 57.375],dtype=np.float32).reshape(1,1,3)
-
-    image = (image-mean)/std 
-    image = image.transpose(2,0,1)
-    image = image[None]
-
-    session = ort.InferenceSession(
-        model, providers=["CUDAExecutionProvider", "CPUExecutionProvider"]
-    )
-    depth = session.run(None, {"input": image})[0]
-
-    depth = cv2.resize(depth[0, 0], (orig_w, orig_h))
-    depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
-    depth = depth.astype(np.uint8)
-
-    depth_color = cv2.applyColorMap(depth, cv2.COLORMAP_INFERNO)
-
-    combined_result = cv2.hconcat([img_raw,  depth_color])
-            
-    cv2.imwrite("output-onnx.png", combined_result)
-    
-    return depth
-
-
-if __name__ == "__main__":
-    args = parse_args()
-    infer(**vars(args))

python/python/requirements.txt

@@ -1,3 +0,0 @@
-onnx
-onnxruntime
-opencv-python