| """ |
| Run GridDehazeNet axmodel inference on a single image. |
| Output: side-by-side image (Hazy | Dehazed), result resized to original size. |
| """ |
| import argparse |
| import os |
|
|
| import numpy as np |
| import axengine as axe |
| from PIL import Image, ImageDraw, ImageFont |
|
|
|
|
| def _get_font(size): |
| for path in ( |
| "/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf", |
| "/usr/share/fonts/truetype/liberation/LiberationSans-Bold.ttf", |
| "/usr/share/fonts/truetype/freefont/FreeSansBold.ttf", |
| ): |
| if os.path.exists(path): |
| return ImageFont.truetype(path, size) |
| return ImageFont.load_default() |
|
|
|
|
| def _label_image(image, text, font): |
| draw = ImageDraw.Draw(image) |
| bbox = draw.textbbox((0, 0), text, font=font) |
| w, h = bbox[2] - bbox[0], bbox[3] - bbox[1] |
| pad = 6 |
| draw.rectangle([(0, 0), (w + pad * 2, h + pad * 2)], fill=(0, 0, 0, 180)) |
| draw.text((pad, pad), text, fill=(255, 255, 255), font=font) |
|
|
|
|
| def _tensor_to_image(tensor): |
| arr = np.squeeze(tensor, axis=0).transpose(1, 2, 0) |
| arr = np.clip(arr, 0.0, 1.0) |
| return Image.fromarray((arr * 255.0 + 0.5).astype(np.uint8)) |
|
|
|
|
| def main(): |
| parser = argparse.ArgumentParser(description="GridDehazeNet axmodel single-image inference") |
| parser.add_argument("-axmodel", default="GridDehazeNet.axmodel") |
| parser.add_argument("-input", default="./0001_0.8_0.2.jpg", help="image file") |
| parser.add_argument("-output", default="axmodel_result.png") |
| parser.add_argument("-height", type=int, default=480) |
| parser.add_argument("-width", type=int, default=640) |
| args = parser.parse_args() |
|
|
| font = _get_font(24) |
| session = axe.InferenceSession(args.axmodel, providers=["AxEngineExecutionProvider"]) |
| input_name = session.get_inputs()[0].name |
| output_name = session.get_outputs()[0].name |
|
|
| original = Image.open(args.input).convert("RGB") |
| orig_w, orig_h = original.size |
|
|
| image = Image.open(args.input).convert("RGB") |
| if image.size != (args.width, args.height): |
| image = image.resize((args.width, args.height), Image.BICUBIC) |
| arr = np.asarray(image).astype(np.float32) |
| arr = arr.transpose(2, 0, 1)[None, ...] |
| arr = arr.astype(np.uint8) |
|
|
| out = session.run([output_name], {input_name: arr})[0] |
| dehazed = _tensor_to_image(out) |
| if dehazed.size != (orig_w, orig_h): |
| dehazed = dehazed.resize((orig_w, orig_h), Image.BICUBIC) |
|
|
| hazy_labeled = original.copy() |
| dehazed_labeled = dehazed.copy() |
| _label_image(hazy_labeled, "Hazy", font) |
| _label_image(dehazed_labeled, "Dehazed", font) |
|
|
| concat = Image.new("RGB", (orig_w * 2, orig_h)) |
| concat.paste(hazy_labeled, (0, 0)) |
| concat.paste(dehazed_labeled, (orig_w, 0)) |
| concat.save(args.output) |
| print(args.output) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
