# --------------------------------------------------------
# #Flow-Guided-Feature-Aggregation
# Copyright (c) 2017 Microsoft
# Licensed under The Apache-2.0 License [see LICENSE for details]
# Written by Yi Li, Haocheng Zhang, Xizhou Zhu
# --------------------------------------------------------
import matplotlib.pyplot as plt
import cv2
import random
def show_boxes(im, dets, classes, scale = 1.0):
plt.cla()
plt.axis("off")
plt.imshow(im)
for cls_idx, cls_name in enumerate(classes):
cls_dets = dets[cls_idx]
for det in cls_dets:
bbox = det[:4] * scale
color = (random.random(), random.random(), random.random())
rect = plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1], fill=False,
edgecolor=color, linewidth=2.5)
plt.gca().add_patch(rect)
if cls_dets.shape[1] == 5:
score = det[-1]
plt.gca().text(bbox[0], bbox[1],
'{:s} {:.3f}'.format(cls_name, score),
bbox=dict(facecolor=color, alpha=0.5), fontsize=9, color='white')
plt.show()
return im
def draw_boxes(im, dets, classes, scale = 1.0):
color_white = (255, 255, 255)
for cls_idx, cls_name in enumerate(classes):
cls_dets = dets[cls_idx]
for det in cls_dets:
bbox = det[:4] * scale
bbox = map(int, bbox)
color = (random.randint(0, 256), random.randint(0, 256), random.randint(0, 256))
cv2.rectangle(im, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color=color, thickness=3)
if cls_dets.shape[1] == 5:
score = det[-1]
cv2.putText(im, '%s %.3f' % (cls_name, score), (bbox[0], bbox[1]+10),
color=color_white, fontFace=cv2.FONT_HERSHEY_COMPLEX, fontScale=1, thickness=2)
return im
