Add img_data_to_pb script

tools/img_data_to_pb.py

+import os
+import argparse
+import numpy as np
+from onnx import numpy_helper
+from PIL import Image
+
+
+def normalize(img):
+    return (img - np.min(img)) / (np.max(img) - np.min(img))
+
+
+def process_img(filename, dim0, dim1):
+    # output shape will be [3, 244, 244]
+    test_img = Image.open(filename)
+    test_img = np.array(test_img.resize([dim0, dim1])).T
+    test_img = normalize(test_img)
+    test_img = test_img.astype(np.float32)
+    return test_img
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description=
+        "Process and batch jpg images from a dir to [num_images, 3, dim0, dim1]"
+    )
+    parser.add_argument("test_dir",
+                        type=str,
+                        default=".",
+                        help="folder where the test images are stored")
+    parser.add_argument("--out_name",
+                        type=str,
+                        default="tensor",
+                        help="output filename")
+    parser.add_argument("--dim0",
+                        type=int,
+                        default=224,
+                        help="resize image dim 0")
+    parser.add_argument("--dim1",
+                        type=int,
+                        default=224,
+                        help="resize image dim 1")
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+    img_dir = args.test_dir
+    images = []
+    for x in os.listdir(img_dir):
+        if x.endswith(".jpg") or x.endswith(".jpeg"):
+            images.append(
+                process_img(os.path.join(img_dir, x), args.dim0, args.dim1))
+    batch_images = np.array(images)
+    print("Output tensor shape:")
+    print(batch_images.shape)
+    tensor = numpy_helper.from_array(batch_images)
+    with open(args.out_name + ".pb", "wb") as f:
+        f.write(tensor.SerializeToString())
+
+
+if __name__ == "__main__":
+    main()