-
Notifications
You must be signed in to change notification settings - Fork 1
/
eval_0_liubang.py
162 lines (136 loc) · 5.99 KB
/
eval_0_liubang.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
#!/usr/bin/python
# -*- coding:utf-8 -*-
# coding=utf-8
# Author: houkai
# Mail: houkai.hk@alibaba-inc.com
# Created Time: 2018-10-08 15:21
# Filename: eval_liubang.py
# Description:
#
import sys
import os, os.path
import cv2
import random
import math
import numpy as np
import tensorflow as tf
slim = tf.contrib.slim
from tensorflow.contrib.slim.python.slim import queues
from datasets import dataset_factory
from nets import nets_factory
from preprocessing import preprocessing_factory
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import matplotlib.cm as mpcm
# =========================================================================== #
# Some colormaps.
# =========================================================================== #
def colors_subselect(colors, num_classes=21):
dt = len(colors) // num_classes
sub_colors = []
for i in range(num_classes):
color = colors[i*dt]
if isinstance(color[0], float):
sub_colors.append([int(c * 255) for c in color])
else:
sub_colors.append([c for c in color])
return sub_colors
colors_plasma = colors_subselect(mpcm.plasma.colors, num_classes=21)
colors_tableau = [(255, 255, 255), (31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),
(44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),
(148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),
(227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),
(188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]
# =========================================================================== #
# OpenCV drawing.
# =========================================================================== #
def draw_lines(img, lines, color=[255, 0, 0], thickness=2):
"""Draw a collection of lines on an image.
"""
for line in lines:
for x1, y1, x2, y2 in line:
cv2.line(img, (x1, y1), (x2, y2), color, thickness)
def draw_rectangle(img, p1, p2, color=[255, 0, 0], thickness=2):
cv2.rectangle(img, p1[::-1], p2[::-1], color, thickness)
def draw_bbox(img, bbox, shape, label, color=[255, 0, 0], thickness=2):
p1 = (int(bbox[0] * shape[0]), int(bbox[1] * shape[1]))
p2 = (int(bbox[2] * shape[0]), int(bbox[3] * shape[1]))
cv2.rectangle(img, p1[::-1], p2[::-1], color, thickness)
p1 = (p1[0]+15, p1[1])
cv2.putText(img, str(label), p1[::-1], cv2.FONT_HERSHEY_DUPLEX, 0.5, color, 1)
def bboxes_draw_on_img(img, classes, scores, bboxes, colors, thickness=2):
shape = img.shape
for i in range(bboxes.shape[0]):
bbox = bboxes[i]
color = colors[classes[i]]
# Draw bounding box...
p1 = (int(bbox[0] * shape[0]), int(bbox[1] * shape[1]))
p2 = (int(bbox[2] * shape[0]), int(bbox[3] * shape[1]))
cv2.rectangle(img, p1[::-1], p2[::-1], color, thickness)
# Draw text...
s = '%s/%.3f' % (classes[i], scores[i])
p1 = (p1[0]-5, p1[1])
cv2.putText(img, s, p1[::-1], cv2.FONT_HERSHEY_DUPLEX, 0.4, color, 1)
# =========================================================================== #
# Matplotlib show...
# =========================================================================== #
def plt_bboxes(img, classes, scores, bboxes, figsize=(10,10), linewidth=1.5):
"""Visualize bounding boxes. Largely inspired by SSD-MXNET!
"""
fig = plt.figure(figsize=figsize)
plt.imshow(img)
height = img.shape[0]
width = img.shape[1]
colors = dict()
for i in range(classes.shape[0]):
cls_id = int(classes[i])
if cls_id >= 0:
score = scores[i]
if cls_id not in colors:
colors[cls_id] = (random.random(), random.random(), random.random())
ymin = int(bboxes[i, 0] * height)
xmin = int(bboxes[i, 1] * width)
ymax = int(bboxes[i, 2] * height)
xmax = int(bboxes[i, 3] * width)
rect = plt.Rectangle((xmin, ymin), xmax - xmin,
ymax - ymin, fill=False,
edgecolor=colors[cls_id],
linewidth=linewidth)
plt.gca().add_patch(rect)
class_name = str(cls_id)
plt.gca().text(xmin, ymin - 2,
'{:s} | {:.3f}'.format(class_name, score),
bbox=dict(facecolor=colors[cls_id], alpha=0.5),
fontsize=12, color='white')
plt.show()
isess = tf.InteractiveSession()
from datasets import pascalvoc_2007
DATASET_DIR = '/mogu/liubang/mytf/tfrecords/'
SPLIT_NAME = 'val'
BATCH_SIZE = 16
# Dataset provider loading data from the dataset.
dataset = pascalvoc_2007.get_split(SPLIT_NAME, DATASET_DIR)
provider = slim.dataset_data_provider.DatasetDataProvider(dataset,
shuffle=False,
# num_epochs=1,
common_queue_capacity=2 * BATCH_SIZE,
common_queue_min=BATCH_SIZE)
[image, shape, bboxes, labels] = provider.get(['image', 'shape', 'object/bbox', 'object/label'])
print('Dataset:', dataset.data_sources, '|', dataset.num_samples)
# with queues.QueueRunners(sess):
# Start populating queues.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
# Draw groundtruth bounding boxes using TF routine.
image_bboxes = tf.squeeze(tf.image.draw_bounding_boxes(tf.expand_dims(tf.to_float(image) / 255., 0),
tf.expand_dims(bboxes, 0)))
# Eval and display the image + bboxes.
rimg, rshape, rbboxes, rlabels = isess.run([image_bboxes, shape, bboxes, labels])
print('Image shape:', rimg.shape, rshape)
print('Bounding boxes:', rbboxes)
print('Labels:', rlabels)
fig = plt.figure(figsize = (12,12))
plt.imshow(rimg)
plt.savefig("tt.png")