forked from matterport/Mask_RCNN
-
Notifications
You must be signed in to change notification settings - Fork 3
/
fish_head.py
327 lines (291 loc) · 12.4 KB
/
fish_head.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
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
"""
Mask R-CNN
Train on the toy My dataset and implement color splash effect.
Copyright (c) 2018 Matterport, Inc.
Licensed under the MIT License (see LICENSE for details)
Written by Waleed Abdulla
------------------------------------------------------------
Usage: import the module (see Jupyter notebooks for examples), or run from
the command line as such:
# Train a new model starting from pre-trained COCO weights
python3 my.py train --dataset=/path/to/my/dataset --weights=coco
# Resume training a model that you had trained earlier
python3 my.py train --dataset=/path/to/my/dataset --weights=last
# Train a new model starting from ImageNet weights
python3 my.py train --dataset=/path/to/my/dataset --weights=imagenet
# Apply color splash to an image
python3 my.py splash --weights=/path/to/weights/file.h5 --image=<URL or path to file>
# Apply color splash to video using the last weights you trained
python3 my.py splash --weights=last --video=<URL or path to file>
"""
import os
import sys
import json, math
import datetime
from datetime import datetime
import numpy as np
import skimage.draw
import time
from skimage.measure import find_contours
from matplotlib import patches, lines
from matplotlib.patches import Polygon
import matplotlib.pyplot as plt
import colorsys
import random
from io import BytesIO
# Root directory of the project
# ROOT_DIR = os.path.abspath(".\\")
# Import Mask RCNN
# sys.path.append(ROOT_DIR) # To find local version of the library
from mrcnn import model as modellib
# Directory to save logs and model checkpoints, if not provided
# through the command line argument --logs
DEFAULT_LOGS_DIR = "logs"
def random_colors(N, bright=True):
"""
Generate random colors.
To get visually distinct colors, generate them in HSV space then
convert to RGB.
"""
brightness = 1.0 if bright else 0.7
hsv = [(i / N, 1, brightness) for i in range(N)]
colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv))
random.shuffle(colors)
return colors
def display_instances(image, points, title="",
figsize=(16, 16), ax=None,
colors=None):
"""
boxes: [num_instance, (y1, x1, y2, x2, class_id)] in image coordinates.
masks: [height, width, num_instances]
class_ids: [num_instances]
class_names: list of class names of the dataset
scores: (optional) confidence scores for each box
title: (optional) Figure title
show_mask, show_bbox: To show masks and bounding boxes or not
figsize: (optional) the size of the image
colors: (optional) An array or colors to use with each object
captions: (optional) A list of strings to use as captions for each object
"""
# If no axis is passed, create one and automatically call show()
if not ax:
_, ax = plt.subplots(1, figsize=figsize)
# _, ax = plt.subplots(1)
# Generate random colors
colors = colors or random_colors(7)
# Show area outside image boundaries.
height, width = image.shape[:2]
ax.set_ylim(height + 10, -10)
ax.set_xlim(-10, width + 10)
ax.axis('off')
ax.set_title(title)
# masked_image = image.astype(np.uint32).copy()
# print(points)
rpts = points.T
ax.plot(rpts[0], rpts[1], 'go-')
ax.imshow(image.astype(np.uint8))
plt.savefig('test.jpg')
figdata = BytesIO()
plt.savefig(figdata, format='png')
return figdata.getvalue()
def polynominal_fitting(points, ploy_threshold):
"""
points: 离散点的集合[[x, y]...]
mse_threshold: 预期的MSE上限
poly_threshold: 拟合的最高项数
return: 拟合结果及其MSE
"""
# 均方差集合
MSEs = np.array([])
coefs = [] # 拟合的集合
for i in range(ploy_threshold):
coef = np.polyfit(points[:,0], points[:, 1], i) # 拟合
coefs.append(coef)
fits = np.polyval(coef, points[:, 0]) # 拟合后的值
# print(len(v1), len(x_fit))
# 原值与拟合值的均方差
MSE = np.linalg.norm(fits - points[:, 0], ord=2)**2/len(v)
MSEs = np.append(MSEs, MSE)
diffMSE = np.diff(MSEs) # 拟合的均方差的差异
co_ind = np.argmin(abs(diffMSE)) # 拟合的MSE差异中选择一个最小的
return coefs[co_ind], MSEs[co_ind]
def osculating_r(points, coef):
# 产生一个均匀的数据集,不要重复值,间隔均匀
v11 = np.arange(points[np.argmin(points, axis=0)[0]][0], points[np.argmax(points, axis=0)[0]][0])
v12 = np.polyval(coef, v11) # 计算拟合多项式的值
# 拟合函数
fx = np.poly1d(coef) # 选择一个合适的
dfx = fx.deriv() # 一阶导
ddfx = dfx.deriv() # 二阶导
r = (1 + dfx(v11)**2)**(3.0/2.0) / abs(ddfx(v11)) # 计算密切圆的曲率
return r
def select_points(points, r):
"""
points: [num_points, [x, y]] 原始的点集
r: [rad] 每个点对应的曲线密切圆
"""
indices = [3] # 最少3个点为一组子序列
while (len(r) - indices[-1]) > 3: # 有足够点分配就循环
a = np.split(r, indices) # 分组 r 曲率
if np.var(a[-2]) < 0.000001: # 方差够小
indices[-1] += 1 # 增加子序列数量
elif np.var(a[-1]) < -0.001: # 最后子序列方差够小则结束循环
print(a[-1])
print(np.var(a[-1]))
break
else:
indices = np.append(indices, indices[-1]+3) # 添加一个子序列
indices = np.insert(indices, 0, 0)
def binomial_fitting(boxes, masks, class_ids, class_names):
"""
boxes: [num_instance, (y1, x1, y2, x2, class_id)] in image coordinates.
masks: [height, width, num_instances]
class_ids: [num_instances]
class_names: list of class names of the dataset
scores: (optional) confidence scores for each box
title: (optional) Figure title
show_mask, show_bbox: To show masks and bounding boxes or not
figsize: (optional) the size of the image
colors: (optional) An array or colors to use with each object
captions: (optional) A list of strings to use as captions for each object
"""
# Number of instances
N = boxes.shape[0]
if not N:
print("\n*** No instances to display *** \n")
else:
assert boxes.shape[0] == masks.shape[-1] == class_ids.shape[0]
v1 = []
for i in range(N):
# Bounding box
if not np.any(boxes[i]):
# Skip this instance. Has no bbox. Likely lost in image cropping.
continue
# Mask
mask = masks[:, :, i]
# Mask Polygon
# Pad to ensure proper polygons for masks that touch image edges.
if class_names[class_ids[i]] != 'fish_head':
continue
padded_mask = np.zeros(
(mask.shape[0] + 2, mask.shape[1] + 2), dtype=np.uint8)
padded_mask[1:-1, 1:-1] = mask
contours = find_contours(padded_mask, 0.5)
for verts in contours:
# Subtract the padding and flip (y, x) to (x, y)
verts = np.fliplr(verts) - 1
# print(verts)
ti = np.argmin(verts, axis=0)
print(ti)
v = verts[ti[1]:,:] # 分割的边界
# 我需要通过拟合得到一个点的集合,XY需要交换一下
v1 = v[:, [1,0]]
# 均方差集合
MSEs = np.array([])
coefs = [] # 拟合的集合
for i in range(1, 5):
coef = np.polyfit(v1[:,0], v1[:, 1], i) # 拟合
coefs.append(coef)
x_fit = np.polyval(coef, v1[:, 0]) # 拟合后的值
# print(len(v1), len(x_fit))
# 原值与拟合值的均方差
MSE = np.linalg.norm(x_fit - v[:, 0], ord=2)**2/len(v)
MSEs = np.append(MSEs, MSE)
diffMSE = np.diff(MSEs) # 拟合的均方差的差异
co_ind = np.where(abs(diffMSE) < 1.0) # 拟合的MSE差异中选择一个 < 1.0 的
fx = np.poly1d(coefs[co_ind[0][0]]) # 选择一个合适的
dfx = fx.deriv() # 一阶导
ddfx = dfx.deriv() # 二阶导
# 产生一个均匀的数据集
v11 = np.arange(v1[np.argmin(v1, axis=0)[0]][0], v1[np.argmax(v1, axis=0)[0]][0])
v12 = np.polyval(coefs[co_ind[0][0]], v11) # 计算拟合多项式的值
r = abs(ddfx(v11))/(1 + dfx(v11)**2)**(3.0/2.0) # 计算密切圆的曲率
indices = [3] # 最少3个点为一组子序列
while (len(r) - indices[-1]) > 3: # 有足够点分配就循环
a = np.split(r, indices) # 分组 r 曲率
if np.var(a[-2]) < 0.000001: # 方差够小
indices[-1] += 1 # 增加子序列数量
elif np.var(a[-1]) < -0.001: # 最后子序列方差够小则结束循环
print(a[-1])
print(np.var(a[-1]))
break
else:
indices = np.append(indices, indices[-1]+3) # 添加一个子序列
indices = np.insert(indices, 0, 0)
x_fit1 = np.array([])
next = indices
while len(next) >= 2:
one, _ = np.split(next, [2])
coef = np.polyfit(v1[one, 0], v1[one, 1], 1) # 拟合一项式
x_fit1 = np.append(x_fit1, np.polyval(coef, v11[one[0]:one[1]]))
print(one, len(x_fit1))
# print(x_fit1, x_fit[indices[0]:indices[1]+1])
# print(v[0:20, :])
_, next = np.split(next, [1])
# 计算结果 MSE
MSE = np.linalg.norm(x_fit1 - v12[:indices[-1]], ord=2)**2/len(x_fit1)
coef = np.polyfit(v1[:,0], v1[:, 1], 2)
x_fit = np.polyval(coef, v1[:, 0])
# 合并成一个集合
v1[:, 1] = x_fit
v1[:, [0, 1]] = v1[:, [1, 0]] # 拟合的边界
# print(v)
# np.savetxt(result_path+'.txt', v)
# plt.show()
return np.array([v12[indices], v11[indices]]).T, MSE
############################################################
# 代码要移动到Redis
############################################################
# 初始化MaskRCNN环境
def init_mask_rcnn(config):
import argparse
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
# Parse command line arguments
parser = argparse.ArgumentParser(
description='Mask R-CNN for Fish Head.')
parser.add_argument("command",
metavar="<command>",
help="'train' or 'splash'")
parser.add_argument('--dataset', required=False,
metavar="/path/to/my/dataset/",
help='Directory of the My dataset')
parser.add_argument('--weights', required=False,
metavar="/path/to/weights.h5",
help="Path to weights .h5 file or 'coco'")
parser.add_argument('--logs', required=False,
default=DEFAULT_LOGS_DIR,
metavar="/path/to/logs/",
help='Logs and checkpoints directory (default=logs/)')
parser.add_argument('--image', required=False,
metavar="path or URL to image",
help='Image to apply the color splash effect on')
parser.add_argument('--label', required=False,
metavar="path or label",
help='label to classes')
parser.add_argument('--imageslist', required=False,
metavar="path imageslist",
help='label to classes')
args = parser.parse_args()
print("Weights: ", args.weights)
print("Dataset: ", args.dataset)
print("Logs: ", args.logs)
# Create model
model = modellib.MaskRCNN(mode="inference", config=config,
model_dir=args.logs)
# Select weights file to load
if args.weights.lower() == "last":
# Find last trained weights
weights_path = model.find_last()
elif args.weights.lower() == "mymask":
weights_path = model.find_last() #""
else:
weights_path = args.weights
# Load weights
print("Loading weights ", weights_path)
if args.weights.lower() == "mymask":
#pass
model.load_weights(weights_path, by_name=True)
model.load_weights(weights_path, by_name=True)
else:
model.load_weights(weights_path, by_name=True)
return model, args.image