You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
75 lines
2.4 KiB
75 lines
2.4 KiB
import cv2.dnn
|
|
import numpy as np
|
|
|
|
from ultralytics.yolo.utils import ROOT, yaml_load
|
|
from ultralytics.yolo.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():
|
|
model: cv2.dnn.Net = cv2.dnn.readNetFromONNX('yolov8n.onnx')
|
|
original_image: np.ndarray = cv2.imread(str(ROOT / 'assets/bus.jpg'))
|
|
[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))
|
|
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__':
|
|
main()
|