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import math
import sys
from argparse import ArgumentParser
from pathlib import Path
import cv2
import onnxruntime
from config import (CLASS_COLORS, CLASS_NAMES, ModelType, YOLOv5_ANCHORS,
YOLOv7_ANCHORS)
from cv2_nms import non_max_suppression
from numpy_coder import Decoder
from preprocess import Preprocess
from tqdm import tqdm
# Add __FILE__ to sys.path
sys.path.append(str(Path(__file__).resolve().parents[0]))
IMG_EXTENSIONS = ('.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif',
'.tiff', '.webp')
def path_to_list(path: str):
path = Path(path)
if path.is_file() and path.suffix in IMG_EXTENSIONS:
res_list = [str(path.absolute())]
elif path.is_dir():
res_list = [
str(p.absolute()) for p in path.iterdir()
if p.suffix in IMG_EXTENSIONS
]
else:
raise RuntimeError
return res_list
def parse_args():
parser = ArgumentParser()
parser.add_argument(
'img', help='Image path, include image file, dir and URL.')
parser.add_argument('onnx', type=str, help='Onnx file')
parser.add_argument('--type', type=str, help='Model type')
parser.add_argument(
'--img-size',
nargs='+',
type=int,
default=[640, 640],
help='Image size of height and width')
parser.add_argument(
'--out-dir', default='./output', type=str, help='Path to output file')
parser.add_argument(
'--show', action='store_true', help='Show the detection results')
parser.add_argument(
'--score-thr', type=float, default=0.3, help='Bbox score threshold')
parser.add_argument(
'--iou-thr', type=float, default=0.7, help='Bbox iou threshold')
args = parser.parse_args()
return args
def main():
args = parse_args()
out_dir = Path(args.out_dir)
model_type = ModelType(args.type.lower())
if not args.show:
out_dir.mkdir(parents=True, exist_ok=True)
files = path_to_list(args.img)
session = onnxruntime.InferenceSession(
args.onnx, providers=['CPUExecutionProvider'])
preprocessor = Preprocess(model_type)
decoder = Decoder(model_type, model_only=True)
if model_type == ModelType.YOLOV5:
anchors = YOLOv5_ANCHORS
elif model_type == ModelType.YOLOV7:
anchors = YOLOv7_ANCHORS
else:
anchors = None
for file in tqdm(files):
image = cv2.imread(file)
image_h, image_w = image.shape[:2]
img, (ratio_w, ratio_h) = preprocessor(image, args.img_size)
features = session.run(None, {'images': img})
decoder_outputs = decoder(
features,
args.score_thr,
num_labels=len(CLASS_NAMES),
anchors=anchors)
nmsd_boxes, nmsd_scores, nmsd_labels = non_max_suppression(
*decoder_outputs, args.score_thr, args.iou_thr)
for box, score, label in zip(nmsd_boxes, nmsd_scores, nmsd_labels):
x0, y0, x1, y1 = box
x0 = math.floor(min(max(x0 / ratio_w, 1), image_w - 1))
y0 = math.floor(min(max(y0 / ratio_h, 1), image_h - 1))
x1 = math.ceil(min(max(x1 / ratio_w, 1), image_w - 1))
y1 = math.ceil(min(max(y1 / ratio_h, 1), image_h - 1))
cv2.rectangle(image, (x0, y0), (x1, y1), CLASS_COLORS[label], 2)
cv2.putText(image, f'{CLASS_NAMES[label]}: {score:.2f}',
(x0, y0 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
(0, 255, 255), 2)
if args.show:
cv2.imshow('result', image)
cv2.waitKey(0)
else:
cv2.imwrite(f'{out_dir / Path(file).name}', image)
if __name__ == '__main__':
main()
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