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#!/usr/bin/env python3
# -*- coding:utf-8 -*-
# Copyright (c) Megvii Inc. All rights reserved.
import cv2
import numpy as np
COCO_CLASSES = ("red", "green", "yellow", "empty", "straight", "left", "right", "other")
__all__ = ["vis"]
def is_nearby(box1, box2, threshold=40):
# Compute the centroid of both boxes
cx1 = (box1[0] + box1[2]) / 2
cy1 = (box1[1] + box1[3]) / 2
cx2 = (box2[0] + box2[2]) / 2
cy2 = (box2[1] + box2[3]) / 2
# Compute the distance between centroids
distance = ((cx1 - cx2) ** 2 + (cy1 - cy2) ** 2) ** 0.5
return distance < threshold
def vis(img, boxes, scores, cls_ids, conf, class_names):
arrow_offsets = {}
seen_boxes = []
for i in range(len(boxes)):
box = boxes[i]
cls_id = int(cls_ids[i])
score = scores[i]
if score < conf:
continue
x0, y0, x1, y1 = map(int, box)
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]
if cls_id < 4:
overlay = img.copy()
cv2.rectangle(overlay, (x0, y0), (x1, y1), color, -1) # -1 fills the rectangle
alpha = 0.4 # Transparency factor.
cv2.addWeighted(overlay, alpha, img, 1 - alpha, 0, img)
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)
else:
nearby_box_idx = None
for idx, seen_box in enumerate(seen_boxes):
if is_nearby(seen_box, box):
nearby_box_idx = idx
break
offset = 0
if nearby_box_idx is not None:
arrow_offsets[nearby_box_idx] = arrow_offsets.get(nearby_box_idx, 0) + 1
offset = arrow_offsets[nearby_box_idx] * (txt_size[1] + 5)
else:
seen_boxes.append(box)
txt_bk_color = (_COLORS[cls_id] * 255 * 0.7).astype(np.uint8).tolist()
cv2.rectangle(
img,
(x0, y1 + 1 + offset),
(x0 + txt_size[0] + 1, y1 + int(1.5 * txt_size[1]) + offset),
txt_bk_color,
-1,
)
cv2.putText(
img, text, (x0, y1 + txt_size[1] + offset), font, 0.4, txt_color, thickness=1
)
return img
_COLORS = np.array(
[ # B , G , R
0.000, 0.000, 1.000,
1.000, 0.300, 0.000,
0.000, 1.000, 1.000,
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)
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