onnx_inference.py

import argparse
import os

import cv2
import numpy as np

from loguru import logger

import time

import onnxruntime

from utils import image_preprocess, mkdir, multiclass_nms, demo_postprocess, vis

CLASSES = (
    'baseball','basketball','croquet_ball','golf_ball','ping-pong_ball','puck','rugby_ball','soccer_ball','tennis_ball','volleyball',
)

def make_parser():
    parser = argparse.ArgumentParser("onnxruntime inference sample")
    parser.add_argument(
        "--model",
        type=str,
        default="model.onnx",
        help="Input your onnx model.",
    )
    parser.add_argument(
        "--mode",
        type=str,
        default="image",
        help="mode type, eg. image, video and webcam.",
    )
    parser.add_argument(
        "--input_path",
        type=str,
        default='test.jpg',
        help="Path to your input image.",
    )
    parser.add_argument(
        "--camid", 
        type=int, 
        default=0, 
        help="webcam demo camera id",
    )
    parser.add_argument(
        "--output_path",
        type=str,
        default='outputs',
        help="Path to your output directory.",
    )
    parser.add_argument(
        "-s",
        "--score_thr",
        type=float,
        default=0.3,
        help="Score threshould to filter the result.",
    )
    parser.add_argument(
        "--input_shape",
        type=str,
        default="640,640",
        help="Specify an input shape for inference.",
    )
    parser.add_argument(
        "--with_p6",
        action="store_true",
        help="Whether your model uses p6 in FPN/PAN.",
    )
    return parser


def inference(args, origin_img):
    t0 = time.time()
    input_shape = tuple(map(int, args.input_shape.split(',')))

    img, ratio = image_preprocess(origin_img, input_shape)

    session = onnxruntime.InferenceSession(args.model)

    ort_inputs = {session.get_inputs()[0].name: img[None, :, :, :]}
    output = session.run(None, ort_inputs)
    print(output[0].shape)
    predictions = demo_postprocess(output[0], input_shape, p6=args.with_p6)[0]

    boxes = predictions[:, :4]
    scores = predictions[:, 4:5] * predictions[:, 5:]

    boxes_xyxy = np.ones_like(boxes)
    boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2]/2.
    boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3]/2.
    boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2]/2.
    boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3]/2.
    boxes_xyxy /= ratio
    dets = multiclass_nms(boxes_xyxy, scores, nms_thr=0.45, score_thr=0.1)
    if dets is not None:
        final_boxes, final_scores, final_cls_inds = dets[:, :4], dets[:, 4], dets[:, 5]
        origin_img = vis(origin_img, final_boxes, final_scores, final_cls_inds,
                         conf=args.score_thr, class_names=CLASSES)
    
    logger.info("Infer time: {:.4f}s".format(time.time() - t0))
    return origin_img

def image_process(args):
    origin_img = cv2.imread(args.input_path)
    origin_img = inference(args, origin_img)
    mkdir(args.output_path)
    output_path = os.path.join(args.output_path, args.input_path.split("/")[-1])
    logger.info("Saving detection result in {}".format(output_path))
    cv2.imwrite(output_path, origin_img)

def imageflow_demo(args):
    cap = cv2.VideoCapture(args.input_path if args.mode == "video" else args.camid)
    width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)  # float
    height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)  # float
    fps = cap.get(cv2.CAP_PROP_FPS)

    mkdir(args.output_path)
    current_time = time.localtime()
    save_folder = os.path.join(
        args.output_path, time.strftime("%Y_%m_%d_%H_%M_%S", current_time)
    )
    os.makedirs(save_folder, exist_ok=True)
    if args.mode == "video":
        save_path = os.path.join(save_folder, args.input_path.split("/")[-1])
    else:
        save_path = os.path.join(save_folder, "camera.mp4")

    logger.info(f"video save_path is {save_path}")
    vid_writer = cv2.VideoWriter(
        save_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (int(width), int(height))
    )
    while True:
        ret_val, frame = cap.read()
        if ret_val:
            result_frame = inference(args, frame)
            vid_writer.write(result_frame)
            ch = cv2.waitKey(1)
            if ch == 27 or ch == ord("q") or ch == ord("Q"):
                break
        else:
            break

if __name__ == '__main__':
    args = make_parser().parse_args()
    if args.mode == "image":
        image_process(args)
    elif args.mode == "video" or args.mode == "webcam":
        imageflow_demo(args)