File size: 2,871 Bytes
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Run GridDehazeNet ONNX 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 onnxruntime as ort
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 ONNX single-image inference")
parser.add_argument("-onnx", default="griddehazenet_outdoor_1x3x480x640_npu_safe.onnx")
parser.add_argument("-input", default="data/test/SOTS/outdoor/hazy/0001_0.8_0.2.jpg", help="image file")
parser.add_argument("-output", default="onnx_result_concat.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 = ort.InferenceSession(args.onnx, providers=["CUDAExecutionProvider", "CPUExecutionProvider"])
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) / 255.0
arr = (arr - 0.5) / 0.5
arr = arr.transpose(2, 0, 1)[None, ...]
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()
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