| """Single-image FFA-Net ONNX inference with side-by-side comparison output.""" |
|
|
| import argparse |
| import sys |
| from pathlib import Path |
|
|
| import numpy as np |
| import onnxruntime as ort |
| from PIL import Image, ImageDraw, ImageFont |
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|
| FILE = Path(__file__).resolve() |
| NET_DIR = FILE.parent |
| ROOT_DIR = NET_DIR.parent |
| sys.path.insert(0, str(NET_DIR)) |
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|
| def parse_args(): |
| parser = argparse.ArgumentParser(description="FFA-Net ONNX single-image inference.") |
| parser.add_argument("--onnx", default='onnx/ffa_ots_512x512.onnx', help="ONNX model path.") |
| parser.add_argument("--input", default='outdoor_natural/nh(2).jpg', help="Path to input hazy image.") |
| parser.add_argument("--output", default="onnx_compare.png", help="Output comparison image path (hazy | dehazed).") |
| parser.add_argument("--height", type=int, default=512, help="ONNX input height.") |
| parser.add_argument("--width", type=int, default=512, help="ONNX input width.") |
| parser.add_argument("--no_label", action="store_true", help="Do not draw hazy/dehazed labels.") |
| return parser.parse_args() |
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|
| MEAN = np.array([0.64, 0.6, 0.58], dtype=np.float32).reshape(3, 1, 1) |
| STD = np.array([0.14, 0.15, 0.152], dtype=np.float32).reshape(3, 1, 1) |
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|
| def preprocess(image_path, height, width): |
| image = Image.open(image_path).convert("RGB") |
| image = image.resize((width, height), Image.BICUBIC) |
| arr = np.asarray(image).astype(np.float32) / 255.0 |
| arr = arr.transpose(2, 0, 1) |
| arr = (arr - MEAN) / STD |
| return arr[None, ...].astype(np.float32) |
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|
|
| def postprocess(output): |
| arr = np.squeeze(output, 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)) |
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|
| def draw_label(img, text): |
| draw = ImageDraw.Draw(img) |
| try: |
| font = ImageFont.truetype("DejaVuSans-Bold.ttf", max(16, img.height // 40)) |
| except Exception: |
| font = ImageFont.load_default() |
| padding = max(5, img.height // 140) |
| bbox = draw.textbbox((0, 0), text, font=font) |
| box_w = bbox[2] - bbox[0] + padding * 2 |
| box_h = bbox[3] - bbox[1] + padding * 2 |
| draw.rectangle([0, 0, box_w, box_h], fill=(0, 0, 0)) |
| draw.text((padding, padding), text, fill=(255, 255, 255), font=font) |
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|
| def make_compare(hazy, dehazed, with_label=True): |
| hazy = hazy.convert("RGB") |
| dehazed = dehazed.convert("RGB") |
| if with_label: |
| hazy = hazy.copy() |
| dehazed = dehazed.copy() |
| draw_label(hazy, "hazy") |
| draw_label(dehazed, "dehazed") |
| canvas = Image.new("RGB", (hazy.width + dehazed.width, hazy.height), color=(255, 255, 255)) |
| canvas.paste(hazy, (0, 0)) |
| canvas.paste(dehazed, (hazy.width, 0)) |
| return canvas |
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|
|
| def main(): |
| args = parse_args() |
|
|
| hazy_img = Image.open(args.input).convert("RGB").resize((args.width, args.height), Image.BICUBIC) |
| inp = preprocess(args.input, args.height, args.width) |
| session = ort.InferenceSession(args.onnx, providers=["CPUExecutionProvider"]) |
| input_name = session.get_inputs()[0].name |
| out = session.run(None, {input_name: inp})[0] |
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|
| output_path = Path(args.output) |
| output_path.parent.mkdir(parents=True, exist_ok=True) |
| dehazed = postprocess(out) |
| compare = make_compare(hazy_img, dehazed, with_label=not args.no_label) |
| compare.save(str(output_path)) |
| print(f"Saved: {output_path}") |
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|
|
| if __name__ == "__main__": |
| main() |
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|
