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ultralytics 8.0.239 Ultralytics Actions and hub-sdk adoption (#7431)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>
Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>
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Glenn Jocher 2024-01-10 03:16:08 +01:00 • committed by GitHub
parent e795277391
commit fe27db2f6e
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41
examples/YOLOv8-OpenCV-ONNX-Python/main.py
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@ -6,7 +6,7 @@ import numpy as np
from ultralytics.utils import ASSETS, yaml_load
from ultralytics.utils.checks import check_yaml
CLASSES = yaml_load(check_yaml('coco128.yaml'))['names']
CLASSES = yaml_load(check_yaml("coco128.yaml"))["names"]
colors = np.random.uniform(0, 255, size=(len(CLASSES), 3))
@ -23,7 +23,7 @@ def draw_bounding_box(img, class_id, confidence, x, y, x_plus_w, y_plus_h):
x_plus_w (int): X-coordinate of the bottom-right corner of the bounding box.
y_plus_h (int): Y-coordinate of the bottom-right corner of the bounding box.
"""
label = f'{CLASSES[class_id]} ({confidence:.2f})'
label = f"{CLASSES[class_id]} ({confidence:.2f})"
color = colors[class_id]
cv2.rectangle(img, (x, y), (x_plus_w, y_plus_h), color, 2)
cv2.putText(img, label, (x - 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
@ -76,8 +76,11 @@ def main(onnx_model, input_image):
(minScore, maxScore, minClassLoc, (x, maxClassIndex)) = cv2.minMaxLoc(classes_scores)
if maxScore >= 0.25:
box = [
outputs[0][i][0] - (0.5 * outputs[0][i][2]), outputs[0][i][1] - (0.5 * outputs[0][i][3]),
outputs[0][i][2], outputs[0][i][3]]
outputs[0][i][0] - (0.5 * outputs[0][i][2]),
outputs[0][i][1] - (0.5 * outputs[0][i][3]),
outputs[0][i][2],
outputs[0][i][3],
]
boxes.append(box)
scores.append(maxScore)
class_ids.append(maxClassIndex)
@ -92,26 +95,34 @@ def main(onnx_model, input_image):
index = result_boxes[i]
box = boxes[index]
detection = {
'class_id': class_ids[index],
'class_name': CLASSES[class_ids[index]],
'confidence': scores[index],
'box': box,
'scale': scale}
"class_id": class_ids[index],
"class_name": CLASSES[class_ids[index]],
"confidence": scores[index],
"box": box,
"scale": scale,
}
detections.append(detection)
draw_bounding_box(original_image, class_ids[index], scores[index], round(box[0] * scale), round(box[1] * scale),
round((box[0] + box[2]) * scale), round((box[1] + box[3]) * scale))
draw_bounding_box(
original_image,
class_ids[index],
scores[index],
round(box[0] * scale),
round(box[1] * scale),
round((box[0] + box[2]) * scale),
round((box[1] + box[3]) * scale),
)
# Display the image with bounding boxes
cv2.imshow('image', original_image)
cv2.imshow("image", original_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
return detections
if __name__ == '__main__':
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--model', default='yolov8n.onnx', help='Input your ONNX model.')
parser.add_argument('--img', default=str(ASSETS / 'bus.jpg'), help='Path to input image.')
parser.add_argument("--model", default="yolov8n.onnx", help="Input your ONNX model.")
parser.add_argument("--img", default=str(ASSETS / "bus.jpg"), help="Path to input image.")
args = parser.parse_args()
main(args.model, args.img)