bbox_opencv_onnx

bbox_opencv_onnx.py

import argparse
import cv2
import numpy as np

CLASSES = {0: "big", 1: "small"}
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_video):
    model: cv2.dnn.Net = cv2.dnn.readNetFromONNX(onnx_model)
    cap = cv2.VideoCapture(input_video)

    while True:
        ret, original_image = cap.read()
        if not ret:
            break

        height, width, _ = original_image.shape
        left_crop = int(0.2 * width)
        right_crop = int(0.8 * width)
        cropped_image = original_image[:, left_crop:right_crop]

        [height, width, _] = cropped_image.shape
        length = max((height, width))
        image = np.zeros((length, length, 3), np.uint8)
        image[0:height, 0:width] = cropped_image
        scale = length / 512

        blob = cv2.dnn.blobFromImage(image, scalefactor=1 / 255, size=(512, 512), 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)

        for i in range(len(result_boxes)):
            index = result_boxes[i]
            box = boxes[index]
            draw_bounding_box(cropped_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('video', cropped_image)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

    cap.release()
    cv2.destroyAllWindows()


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--model', default='yolov8n.onnx', help='Input your onnx model.')
    parser.add_argument('--video', default='assets/sample_video.mp4', help='Path to input video.')
    args = parser.parse_args()
    main(args.model, args.video)