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import argparse
import cv2.dnn
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']
colors = np.random.uniform(0, 255, size=(len(CLASSES), 3))
def draw_bounding_box(img, class_id, confidence, x, y, x_plus_w, y_plus_h):
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)
def main(onnx_model, input_image):
model: cv2.dnn.Net = cv2.dnn.readNetFromONNX(onnx_model)
original_image: np.ndarray = cv2.imread(input_image)
[height, width, _] = original_image.shape
length = max((height, width))
image = np.zeros((length, length, 3), np.uint8)
image[0:height, 0:width] = original_image
scale = length / 640
blob = cv2.dnn.blobFromImage(image, scalefactor=1 / 255, size=(640, 640), swapRB=True)
model.setInput(blob)
outputs = model.forward()
outputs = np.array([cv2.transpose(outputs[0])])
rows = outputs.shape[1]
boxes = []
scores = []
class_ids = []
for i in range(rows):
classes_scores = outputs[0][i][4:]
(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]]
boxes.append(box)
scores.append(maxScore)
class_ids.append(maxClassIndex)
result_boxes = cv2.dnn.NMSBoxes(boxes, scores, 0.25, 0.45, 0.5)
detections = []
for i in range(len(result_boxes)):
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}
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))
cv2.imshow('image', original_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
return detections
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.')
args = parser.parse_args()
main(args.model, args.img)
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