Initial clean project import

2026-06-19 18:41:41 +08:00
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+"""
+Inference ONNX model of MODNet
+
+Arguments:
+    --image-path: path of the input image (a file)
+    --output-path: path for saving the predicted alpha matte (a file)
+    --model-path: path of the ONNX model
+
+Example:
+python inference_onnx.py \
+    --image-path=demo.jpg --output-path=matte.png --model-path=modnet.onnx
+"""
+
+import os
+import cv2
+import argparse
+import numpy as np
+from PIL import Image
+
+import onnx
+import onnxruntime
+
+
+if __name__ == '__main__':
+    # define cmd arguments
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--image-path', type=str, help='path of the input image (a file)')
+    parser.add_argument('--output-path', type=str, help='paht for saving the predicted alpha matte (a file)')
+    parser.add_argument('--model-path', type=str, help='path of the ONNX model')
+    args = parser.parse_args()
+
+    # check input arguments
+    if not os.path.exists(args.image_path):
+        print('Cannot find the input image: {0}'.format(args.image_path))
+        exit()
+    if not os.path.exists(args.model_path):
+        print('Cannot find the ONXX model: {0}'.format(args.model_path))
+        exit()
+
+    ref_size = 512
+
+    # Get x_scale_factor & y_scale_factor to resize image
+    def get_scale_factor(im_h, im_w, ref_size):
+
+        if max(im_h, im_w) < ref_size or min(im_h, im_w) > ref_size:
+            if im_w >= im_h:
+                im_rh = ref_size
+                im_rw = int(im_w / im_h * ref_size)
+            elif im_w < im_h:
+                im_rw = ref_size
+                im_rh = int(im_h / im_w * ref_size)
+        else:
+            im_rh = im_h
+            im_rw = im_w
+
+        im_rw = im_rw - im_rw % 32
+        im_rh = im_rh - im_rh % 32
+
+        x_scale_factor = im_rw / im_w
+        y_scale_factor = im_rh / im_h
+
+        return x_scale_factor, y_scale_factor
+
+    ##############################################
+    #  Main Inference part
+    ##############################################
+
+    # read image
+    im = cv2.imread(args.image_path)
+    im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
+
+    # unify image channels to 3
+    if len(im.shape) == 2:
+        im = im[:, :, None]
+    if im.shape[2] == 1:
+        im = np.repeat(im, 3, axis=2)
+    elif im.shape[2] == 4:
+        im = im[:, :, 0:3]
+
+    # normalize values to scale it between -1 to 1
+    im = (im - 127.5) / 127.5   
+
+    im_h, im_w, im_c = im.shape
+    x, y = get_scale_factor(im_h, im_w, ref_size) 
+
+    # resize image
+    im = cv2.resize(im, None, fx = x, fy = y, interpolation = cv2.INTER_AREA)
+
+    # prepare input shape
+    im = np.transpose(im)
+    im = np.swapaxes(im, 1, 2)
+    im = np.expand_dims(im, axis = 0).astype('float32')
+
+    # Initialize session and get prediction
+    session = onnxruntime.InferenceSession(args.model_path, None)
+    input_name = session.get_inputs()[0].name
+    output_name = session.get_outputs()[0].name
+    result = session.run([output_name], {input_name: im})
+
+    # refine matte
+    matte = (np.squeeze(result[0]) * 255).astype('uint8')
+    matte = cv2.resize(matte, dsize=(im_w, im_h), interpolation = cv2.INTER_AREA)
+
+    cv2.imwrite(args.output_path, matte)