#!/usr/bin/env python3
# Copyright (c) Megvii Inc. All rights reserved.
import cv2
import numpy as np
__all__ = ['vis']
[ドキュメント]
def vis(img, boxes, scores, cls_ids, conf=0.5, class_names=None):
# OpenCVでエラーが出ないように、imgを連続メモリとして扱えるようにする
img = np.ascontiguousarray(img).copy()
for i in range(len(boxes)):
box = boxes[i]
cls_id = int(cls_ids[i])
score = scores[i]
if score < conf:
continue
x0 = int(box[0])
y0 = int(box[1])
x1 = int(box[2])
y1 = int(box[3])
color = (_COLORS[cls_id] * 255).astype(np.uint8).tolist()
text = '{}:{:.1f}%'.format(class_names[cls_id], score * 100)
txt_color = (0, 0, 0) if np.mean(_COLORS[cls_id]) > 0.5 else (255, 255,
255)
font = cv2.FONT_HERSHEY_SIMPLEX
txt_size = cv2.getTextSize(text, font, 0.4, 1)[0]
cv2.rectangle(img, (x0, y0), (x1, y1), color, 2)
txt_bk_color = (_COLORS[cls_id] * 255 * 0.7).astype(np.uint8).tolist()
cv2.rectangle(img, (x0, y0 + 1),
(x0 + txt_size[0] + 1, y0 + int(1.5 * txt_size[1])),
txt_bk_color, -1)
cv2.putText(img,
text, (x0, y0 + txt_size[1]),
font,
0.4,
txt_color,
thickness=1)
return img
_COLORS = np.array([
0.000, 0.447, 0.741, 0.850, 0.325, 0.098, 0.929, 0.694, 0.125, 0.494,
0.184, 0.556, 0.466, 0.674, 0.188, 0.301, 0.745, 0.933, 0.635, 0.078,
0.184, 0.300, 0.300, 0.300, 0.600, 0.600, 0.600, 1.000, 0.000, 0.000,
1.000, 0.500, 0.000, 0.749, 0.749, 0.000, 0.000, 1.000, 0.000, 0.000,
0.000, 1.000, 0.667, 0.000, 1.000, 0.333, 0.333, 0.000, 0.333, 0.667,
0.000, 0.333, 1.000, 0.000, 0.667, 0.333, 0.000, 0.667, 0.667, 0.000,
0.667, 1.000, 0.000, 1.000, 0.333, 0.000, 1.000, 0.667, 0.000, 1.000,
1.000, 0.000, 0.000, 0.333, 0.500, 0.000, 0.667, 0.500, 0.000, 1.000,
0.500, 0.333, 0.000, 0.500, 0.333, 0.333, 0.500, 0.333, 0.667, 0.500,
0.333, 1.000, 0.500, 0.667, 0.000, 0.500, 0.667, 0.333, 0.500, 0.667,
0.667, 0.500, 0.667, 1.000, 0.500, 1.000, 0.000, 0.500, 1.000, 0.333,
0.500, 1.000, 0.667, 0.500, 1.000, 1.000, 0.500, 0.000, 0.333, 1.000,
0.000, 0.667, 1.000, 0.000, 1.000, 1.000, 0.333, 0.000, 1.000, 0.333,
0.333, 1.000, 0.333, 0.667, 1.000, 0.333, 1.000, 1.000, 0.667, 0.000,
1.000, 0.667, 0.333, 1.000, 0.667, 0.667, 1.000, 0.667, 1.000, 1.000,
1.000, 0.000, 1.000, 1.000, 0.333, 1.000, 1.000, 0.667, 1.000, 0.333,
0.000, 0.000, 0.500, 0.000, 0.000, 0.667, 0.000, 0.000, 0.833, 0.000,
0.000, 1.000, 0.000, 0.000, 0.000, 0.167, 0.000, 0.000, 0.333, 0.000,
0.000, 0.500, 0.000, 0.000, 0.667, 0.000, 0.000, 0.833, 0.000, 0.000,
1.000, 0.000, 0.000, 0.000, 0.167, 0.000, 0.000, 0.333, 0.000, 0.000,
0.500, 0.000, 0.000, 0.667, 0.000, 0.000, 0.833, 0.000, 0.000, 1.000,
0.000, 0.000, 0.000, 0.143, 0.143, 0.143, 0.286, 0.286, 0.286, 0.429,
0.429, 0.429, 0.571, 0.571, 0.571, 0.714, 0.714, 0.714, 0.857, 0.857,
0.857, 0.000, 0.447, 0.741, 0.314, 0.717, 0.741, 0.50, 0.5, 0
]).astype(np.float32).reshape(-1, 3)