ncnn/python/examples/yolact.py

# Copyright 2020 Tencent
# SPDX-License-Identifier: BSD-3-Clause

import sys
import cv2
import numpy as np
from ncnn.model_zoo import get_model
from ncnn.utils import draw_detection_objects


def draw_result(image, class_names, boxes, masks, classes, scores):
    colors = [
        [56, 0, 255],
        [226, 255, 0],
        [0, 94, 255],
        [0, 37, 255],
        [0, 255, 94],
        [255, 226, 0],
        [0, 18, 255],
        [255, 151, 0],
        [170, 0, 255],
        [0, 255, 56],
        [255, 0, 75],
        [0, 75, 255],
        [0, 255, 169],
        [255, 0, 207],
        [75, 255, 0],
        [207, 0, 255],
        [37, 0, 255],
        [0, 207, 255],
        [94, 0, 255],
        [0, 255, 113],
        [255, 18, 0],
        [255, 0, 56],
        [18, 0, 255],
        [0, 255, 226],
        [170, 255, 0],
        [255, 0, 245],
        [151, 255, 0],
        [132, 255, 0],
        [75, 0, 255],
        [151, 0, 255],
        [0, 151, 255],
        [132, 0, 255],
        [0, 255, 245],
        [255, 132, 0],
        [226, 0, 255],
        [255, 37, 0],
        [207, 255, 0],
        [0, 255, 207],
        [94, 255, 0],
        [0, 226, 255],
        [56, 255, 0],
        [255, 94, 0],
        [255, 113, 0],
        [0, 132, 255],
        [255, 0, 132],
        [255, 170, 0],
        [255, 0, 188],
        [113, 255, 0],
        [245, 0, 255],
        [113, 0, 255],
        [255, 188, 0],
        [0, 113, 255],
        [255, 0, 0],
        [0, 56, 255],
        [255, 0, 113],
        [0, 255, 188],
        [255, 0, 94],
        [255, 0, 18],
        [18, 255, 0],
        [0, 255, 132],
        [0, 188, 255],
        [0, 245, 255],
        [0, 169, 255],
        [37, 255, 0],
        [255, 0, 151],
        [188, 0, 255],
        [0, 255, 37],
        [0, 255, 0],
        [255, 0, 170],
        [255, 0, 37],
        [255, 75, 0],
        [0, 0, 255],
        [255, 207, 0],
        [255, 0, 226],
        [255, 245, 0],
        [188, 255, 0],
        [0, 255, 18],
        [0, 255, 75],
        [0, 255, 151],
        [255, 56, 0],
        [245, 255, 0],
    ]

    color_index = 0

    for box, mask, label, score in zip(boxes, masks, classes, scores):
        if score < 0.15:
            continue

        print(
            "%s = %.5f at %.2f %.2f %.2f x %.2f\n"
            % (label, score, box[0], box[1], box[2], box[3])
        )

        cv2.rectangle(
            image,
            (int(box[0]), int(box[1])),
            (int(box[0] + box[2]), int(int(box[1] + box[3]))),
            (255, 0, 0),
        )

        text = "%s %.1f%%" % (class_names[int(label)], score * 100)

        label_size, baseLine = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)

        x = box[0]
        y = box[1] - label_size[1] - baseLine
        if y < 0:
            y = 0
        if x + label_size[0] > image.shape[1]:
            x = image.shape[1] - label_size[0]

        cv2.rectangle(
            image,
            (int(x), int(y)),
            (int(x + label_size[0]), int(y + label_size[1] + baseLine)),
            (255, 255, 255),
            -1,
        )

        cv2.putText(
            image,
            text,
            (int(x), int(y + label_size[1])),
            cv2.FONT_HERSHEY_SIMPLEX,
            0.5,
            (0, 0, 0),
        )

        image[mask] = image[mask] * 0.5 + np.array(colors[color_index]) * 0.5
        color_index += 1

    cv2.imshow("image", image)
    cv2.waitKey(0)


if __name__ == "__main__":
    if len(sys.argv) != 2:
        print("Usage: %s [imagepath]" % (sys.argv[0]))
        sys.exit(0)

    imagepath = sys.argv[1]
    m = cv2.imread(imagepath)
    if m is None:
        print("cv2.imread %s failed\n" % (imagepath))
        sys.exit(0)

    net = get_model(
        "yolact",
        target_size=550,
        confidence_threshold=0.05,
        nms_threshold=0.5,
        keep_top_k=200,
        num_threads=4,
        use_gpu=True,
    )

    boxes, masks, classes, scores = net(m)

    draw_result(m, net.class_names, boxes, masks, classes, scores)