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80 lines
2.7 KiB
80 lines
2.7 KiB
import argparse |
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import cv2.dnn |
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import numpy as np |
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from ultralytics.utils import ASSETS, yaml_load |
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from ultralytics.utils.checks import check_yaml |
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CLASSES = yaml_load(check_yaml('coco128.yaml'))['names'] |
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colors = np.random.uniform(0, 255, size=(len(CLASSES), 3)) |
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def draw_bounding_box(img, class_id, confidence, x, y, x_plus_w, y_plus_h): |
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label = f'{CLASSES[class_id]} ({confidence:.2f})' |
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color = colors[class_id] |
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cv2.rectangle(img, (x, y), (x_plus_w, y_plus_h), color, 2) |
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cv2.putText(img, label, (x - 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2) |
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def main(onnx_model, input_image): |
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model: cv2.dnn.Net = cv2.dnn.readNetFromONNX(onnx_model) |
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original_image: np.ndarray = cv2.imread(input_image) |
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[height, width, _] = original_image.shape |
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length = max((height, width)) |
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image = np.zeros((length, length, 3), np.uint8) |
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image[0:height, 0:width] = original_image |
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scale = length / 640 |
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blob = cv2.dnn.blobFromImage(image, scalefactor=1 / 255, size=(640, 640), swapRB=True) |
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model.setInput(blob) |
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outputs = model.forward() |
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outputs = np.array([cv2.transpose(outputs[0])]) |
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rows = outputs.shape[1] |
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boxes = [] |
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scores = [] |
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class_ids = [] |
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for i in range(rows): |
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classes_scores = outputs[0][i][4:] |
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(minScore, maxScore, minClassLoc, (x, maxClassIndex)) = cv2.minMaxLoc(classes_scores) |
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if maxScore >= 0.25: |
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box = [ |
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outputs[0][i][0] - (0.5 * outputs[0][i][2]), outputs[0][i][1] - (0.5 * outputs[0][i][3]), |
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outputs[0][i][2], outputs[0][i][3]] |
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boxes.append(box) |
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scores.append(maxScore) |
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class_ids.append(maxClassIndex) |
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result_boxes = cv2.dnn.NMSBoxes(boxes, scores, 0.25, 0.45, 0.5) |
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detections = [] |
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for i in range(len(result_boxes)): |
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index = result_boxes[i] |
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box = boxes[index] |
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detection = { |
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'class_id': class_ids[index], |
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'class_name': CLASSES[class_ids[index]], |
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'confidence': scores[index], |
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'box': box, |
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'scale': scale} |
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detections.append(detection) |
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draw_bounding_box(original_image, class_ids[index], scores[index], round(box[0] * scale), round(box[1] * scale), |
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round((box[0] + box[2]) * scale), round((box[1] + box[3]) * scale)) |
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cv2.imshow('image', original_image) |
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cv2.waitKey(0) |
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cv2.destroyAllWindows() |
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return detections |
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if __name__ == '__main__': |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--model', default='yolov8n.onnx', help='Input your onnx model.') |
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parser.add_argument('--img', default=str(ASSETS / 'bus.jpg'), help='Path to input image.') |
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args = parser.parse_args() |
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main(args.model, args.img)
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