test_onnx.py

import os
import cv2
import torch
import argparse
import onnxruntime as ort
import numpy as np
from lib.core.general import non_max_suppression


def resize_unscale(img, new_shape=(640, 640), color=114):
    shape = img.shape[:2]  # current shape [height, width]
    if isinstance(new_shape, int):
        new_shape = (new_shape, new_shape)

    canvas = np.zeros((new_shape[0], new_shape[1], 3))
    canvas.fill(color)
    # Scale ratio (new / old) new_shape(h,w)
    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])

    # Compute padding
    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))  # w,h
    new_unpad_w = new_unpad[0]
    new_unpad_h = new_unpad[1]
    pad_w, pad_h = new_shape[1] - new_unpad_w, new_shape[0] - new_unpad_h  # wh padding

    dw = pad_w // 2  # divide padding into 2 sides
    dh = pad_h // 2

    if shape[::-1] != new_unpad:  # resize
        img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_AREA)

    canvas[dh:dh + new_unpad_h, dw:dw + new_unpad_w, :] = img

    return canvas, r, dw, dh, new_unpad_w, new_unpad_h  # (dw,dh)


def infer_yolop(weight="yolop-640-640.onnx",
                img_path="./inference/images/7dd9ef45-f197db95.jpg"):

    ort.set_default_logger_severity(4)
    onnx_path = f"./weights/{weight}"
    ort_session = ort.InferenceSession(onnx_path)
    print(f"Load {onnx_path} done!")

    outputs_info = ort_session.get_outputs()
    inputs_info = ort_session.get_inputs()

    for ii in inputs_info:
        print("Input: ", ii)
    for oo in outputs_info:
        print("Output: ", oo)

    print("num outputs: ", len(outputs_info))

    save_det_path = f"./pictures/detect_onnx.jpg"
    save_da_path = f"./pictures/da_onnx.jpg"
    save_ll_path = f"./pictures/ll_onnx.jpg"
    save_merge_path = f"./pictures/output_onnx.jpg"

    img_bgr = cv2.imread(img_path)
    height, width, _ = img_bgr.shape

    # convert to RGB
    img_rgb = img_bgr[:, :, ::-1].copy()

    # resize & normalize
    canvas, r, dw, dh, new_unpad_w, new_unpad_h = resize_unscale(img_rgb, (640, 640))

    img = canvas.copy().astype(np.float32)  # (3,640,640) RGB
    img /= 255.0
    img[:, :, 0] -= 0.485
    img[:, :, 1] -= 0.456
    img[:, :, 2] -= 0.406
    img[:, :, 0] /= 0.229
    img[:, :, 1] /= 0.224
    img[:, :, 2] /= 0.225

    img = img.transpose(2, 0, 1)

    img = np.expand_dims(img, 0)  # (1, 3,640,640)

    # inference: (1,n,6) (1,2,640,640) (1,2,640,640)
    det_out, da_seg_out, ll_seg_out = ort_session.run(
        ['det_out', 'drive_area_seg', 'lane_line_seg'],
        input_feed={"images": img}
    )

    det_out = torch.from_numpy(det_out).float()
    boxes = non_max_suppression(det_out)[0]  # [n,6] [x1,y1,x2,y2,conf,cls]
    boxes = boxes.cpu().numpy().astype(np.float32)

    if boxes.shape[0] == 0:
        print("no bounding boxes detected.")
        return

    # scale coords to original size.
    boxes[:, 0] -= dw
    boxes[:, 1] -= dh
    boxes[:, 2] -= dw
    boxes[:, 3] -= dh
    boxes[:, :4] /= r

    print(f"detect {boxes.shape[0]} bounding boxes.")

    img_det = img_rgb[:, :, ::-1].copy()
    for i in range(boxes.shape[0]):
        x1, y1, x2, y2, conf, label = boxes[i]
        x1, y1, x2, y2, label = int(x1), int(y1), int(x2), int(y2), int(label)
        img_det = cv2.rectangle(img_det, (x1, y1), (x2, y2), (0, 255, 0), 2, 2)

    cv2.imwrite(save_det_path, img_det)

    # select da & ll segment area.
    da_seg_out = da_seg_out[:, :, dh:dh + new_unpad_h, dw:dw + new_unpad_w]
    ll_seg_out = ll_seg_out[:, :, dh:dh + new_unpad_h, dw:dw + new_unpad_w]

    da_seg_mask = np.argmax(da_seg_out, axis=1)[0]  # (?,?) (0|1)
    ll_seg_mask = np.argmax(ll_seg_out, axis=1)[0]  # (?,?) (0|1)
    print(da_seg_mask.shape)
    print(ll_seg_mask.shape)

    color_area = np.zeros((new_unpad_h, new_unpad_w, 3), dtype=np.uint8)
    color_area[da_seg_mask == 1] = [0, 255, 0]
    color_area[ll_seg_mask == 1] = [255, 0, 0]
    color_seg = color_area

    # convert to BGR
    color_seg = color_seg[..., ::-1]
    color_mask = np.mean(color_seg, 2)
    img_merge = canvas[dh:dh + new_unpad_h, dw:dw + new_unpad_w, :]
    img_merge = img_merge[:, :, ::-1]

    # merge: resize to original size
    img_merge[color_mask != 0] = \
        img_merge[color_mask != 0] * 0.5 + color_seg[color_mask != 0] * 0.5
    img_merge = img_merge.astype(np.uint8)
    img_merge = cv2.resize(img_merge, (width, height),
                           interpolation=cv2.INTER_LINEAR)
    for i in range(boxes.shape[0]):
        x1, y1, x2, y2, conf, label = boxes[i]
        x1, y1, x2, y2, label = int(x1), int(y1), int(x2), int(y2), int(label)
        img_merge = cv2.rectangle(img_merge, (x1, y1), (x2, y2), (0, 255, 0), 2, 2)

    # da: resize to original size
    da_seg_mask = da_seg_mask * 255
    da_seg_mask = da_seg_mask.astype(np.uint8)
    da_seg_mask = cv2.resize(da_seg_mask, (width, height),
                             interpolation=cv2.INTER_LINEAR)

    # ll: resize to original size
    ll_seg_mask = ll_seg_mask * 255
    ll_seg_mask = ll_seg_mask.astype(np.uint8)
    ll_seg_mask = cv2.resize(ll_seg_mask, (width, height),
                             interpolation=cv2.INTER_LINEAR)

    cv2.imwrite(save_merge_path, img_merge)
    cv2.imwrite(save_da_path, da_seg_mask)
    cv2.imwrite(save_ll_path, ll_seg_mask)

    print("detect done.")


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument('--weight', type=str, default="yolop-640-640.onnx")
    parser.add_argument('--img', type=str, default="./inference/images/9aa94005-ff1d4c9a.jpg")
    args = parser.parse_args()

    infer_yolop(weight=args.weight, img_path=args.img)
    """
    PYTHONPATH=. python3 ./test_onnx.py --weight yolop-640-640.onnx --img test.jpg
    """