-
Notifications
You must be signed in to change notification settings - Fork 0
/
Python code.py
226 lines (170 loc) · 7.31 KB
/
Python code.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
#!/usr/bin/env python
# coding: utf-8
# In[1]:
## LOADINGDATA SETS FROM THE PATH. THE USER IMPUTS IMAGE NUMBER IN FOLDER
## IMAGE PROCESSING TECHINIQUESAPPLIED TO LOADED IMAGE
#
# LOAD ALL REQUIRED LIBRARIES
#
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import cv2
import scipy.io
import os
import re
from stack import stackImages # STACKING MULTIPLE IMAGES
from read_file import read_image # READ INPUT IMAGE FUNCTION
from contours import getContours # CONTOURS FUNCTION
from contours import empty
from Contour import getContour
_nsre = re.compile('([0-9]+)')
def natural_sort_key(s):
return [int(text) if text.isdigit() else text.lower()
for text in re.split(_nsre, s)]
#
# lOADING IMAGES
#
path = "./Sample Images"
def loadImages(path):
return [os.path.join(path,f) for f in os.listdir(path) if f.endswith('.png')]
#
# OUTPUT THE TOTAL IMAGES CURRENTLY IN FOLDER
# SORTED EACH IMAGE
#
filenames = loadImages(path)
filenames.sort(key=natural_sort_key)
images = []
for file in filenames:
images.append(cv2.imread(file,cv2.IMREAD_UNCHANGED))
print("Loaded " + str(len(images)))
T =True
while(T):
try:
i = int(input("what is image do you want to load: "))
img = images[i]
T =False
except:
print(" Unable to find the image you are looking for")
# gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
cv2.imshow("Walk_Data_Original",img)
cv2.waitKey(0)
cv2.destroyAllWindows()
# noise removal
kernel = np.ones((3,3),np.uint8)
opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 3)
# sure background area
sure_bg = cv2.dilate(opening,kernel,iterations=3) #1 or 2
dist_transform = cv2.distanceTransform(opening,cv2.DIST_L2,5)
ret, sure_fg = cv2.threshold(dist_transform,0.08*dist_transform.max(),255,0)
# Finding unknown region
sure_fg = np.uint8(sure_fg)
unknown = cv2.subtract(sure_bg,sure_fg)
imgContour1 = img.copy()
imgContour2 = img.copy()
# In[2]:
# FUNCTIONS TO DETERMINE THE CONTOUR
# ONE OF FUNCTIONS DETERMINE THE INNER /OUTER TYPE CONTOUR
def empty(a):
pass
def getContours(threshold,imgContour):
# OTHER POSSIBLE OPTIONS
#contours,_ = cv2.findContours(threshold,cv2.RETR_CCOMP,cv2.CHAIN_APPROX_NONE)
#contours,_ = cv2.findContours(threshold,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
contours,_ = cv2.findContours(threshold,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
# POINTS IN IMAGE
axis_x =[]
axis_y =[]
for cnt in contours:
# OTHER POSSIBLE OPTIONS
#areaMin = cv2.getTrackbarPos("Min_Area","Parameters")
#areaMax = cv2.getTrackbarPos("Max_Area","Parameters")
area =cv2.contourArea(cnt)
if area > 1000:
cv2.drawContours(imgContour,cnt,-1,(255,0,255),2)
# CONTOUR APPROXIMATION
peri =cv2.arcLength(cnt,True)
approx = cv2.approxPolyDP(cnt,0.1*peri,True)
x,y,w,h = cv2.boundingRect(approx)
cv2.rectangle(imgContour,(x,y),(x+w,y+h),(0,255,0),2)
#DISPLAYING TEXT ON IMAGE CONTOUR
cv2.putText(imgContour,"X: "+ str(int(x)),(x + w + 20 ,y +20 ),cv2.FONT_HERSHEY_SIMPLEX,0.7,(0,255,0),2)
cv2.putText(imgContour,"Y: "+ str(int(y)),(x + w + 20 ,y +40 ),cv2.FONT_HERSHEY_SIMPLEX,0.7,(0,255,0),2)
cv2.putText(imgContour,"W: "+ str(int(w)),(x + w + 20 ,y +60 ),cv2.FONT_HERSHEY_SIMPLEX,0.7,(0,255,0),2)
cv2.putText(imgContour,"H: "+ str(int(h)),(x + w + 20 ,y +80 ),cv2.FONT_HERSHEY_SIMPLEX,0.7,(0,255,0),2)
axis_x.append(x)
axis_y.append(y)
return axis_x,axis_y
def getContour(threshold,imgContour):
# OTHER POSSIBLE OPTIONS
#contours,_ = cv2.findContours(threshold,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
#contours,_ = cv2.findContours(threshold,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
#contours,_ = cv2.findContours(threshold,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
#contours,_ = cv2.findContours(threshold,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
contours,_ = cv2.findContours(threshold,cv2.RETR_CCOMP,cv2.CHAIN_APPROX_NONE)
i =1
m =0
for cnt in contours:
# OTHER POSSIBLE OPTIONS
#areaMin = cv2.getTrackbarPos("Min_Area","Parameters")
#areaMax = cv2.getTrackbarPos("Max_Area","Parameters")
area =cv2.contourArea(cnt)
if area > 1000:
rect = cv2.minAreaRect(cnt)
box = cv2.boxPoints(rect)
box = np.int0(box)
x,y,w,h = cv2.boundingRect(box)
cv2.drawContours(imgContour,[box],0,(255,0,255),1)
if(i % 2 != 0):
cv2.putText(imgContour,"OuterBox",(x + w + 20,y + m),cv2.FONT_HERSHEY_SIMPLEX,0.7,(0,255,0),2)
else:
cv2.putText(imgContour,"InnerBox",(x + w + 20,y + m),cv2.FONT_HERSHEY_SIMPLEX,0.7,(0,255,0),2)
cv2.putText(imgContour,"X: "+ str(int(x)),(x + w + 20 ,y +20 +m),cv2.FONT_HERSHEY_SIMPLEX,0.7,(0,255,0),2)
cv2.putText(imgContour,"Y: "+ str(int(y)),(x + w + 20 ,y +40 +m),cv2.FONT_HERSHEY_SIMPLEX,0.7,(0,255,0),2)
cv2.putText(imgContour,"W: "+ str(int(w)),(x + w + 20 ,y +60 +m),cv2.FONT_HERSHEY_SIMPLEX,0.7,(0,255,0),2)
cv2.putText(imgContour,"H: "+ str(int(h)),(x + w + 20 ,y +80 +m),cv2.FONT_HERSHEY_SIMPLEX,0.7,(0,255,0),2)
m =m +100
if(i % 2 != 0):
print("Box Outer")
else:
print("Box inner")
m=0
print("Values =" + "X: "+ str(int(x)) + ","+"Y: "+ str(int(y))+","+"W: "+ str(int(w))+","+"H: "+ str(int(h)))
i=i+1
# In[3]:
X,Y = getContours(unknown,imgContour1)
getContour(unknown,imgContour2)
cv2.imshow("imgContour1",imgContour1)
cv2.imshow("imgContour2",imgContour2)
cv2.imshow("Image Boundary",unknown)
plott = imgContour1.copy()
cv2.waitKey(0)
cv2.destroyAllWindows()
print(imgContour1.shape)
# In[4]:
# CALCULATING THE VELOCITY USING FUNCTION
# DONE BY TAKING THE VALUES (X0,Y0) (X2,Y2) to CALCULATE VELOCITY
def velocity(x1, y1, x2, y2,Constant1,Constant2):
return abs((float)((x2-x1)*Constant1)/((y2-y1)*Constant2))
Constant1 = 100/496
Constant2 = 10/369
try:
velocity = velocity(X[0],Y[0],X[2],Y[2],Constant1,Constant2)
step_length = ((X[2] - X[1]) * Constant1)
stride_length = ((X[2] - X[0]) * Constant1)
cv2.line(plott,(X[0],Y[0]),(X[2],Y[2]),(0,0,255),1)
cv2.putText(plott,"Velocity(m/s): "+ str(round(velocity/39.3701,2)),(50 ,250),cv2.FONT_HERSHEY_SIMPLEX,0.7,(0,255,0),1)
cv2.putText(plott,"step_length(m): "+ str(round(step_length/39.3701,2)),(50 ,300),cv2.FONT_HERSHEY_SIMPLEX,0.7,(0,255,0),1)
cv2.putText(plott,"stride_length(m): "+ str(round(stride_length/39.3701,2)),(50 ,350),cv2.FONT_HERSHEY_SIMPLEX,0.7,(0,255,0),1)
cv2.imshow("Calculating the slope",plott)
cv2.waitKey(0)
cv2.destroyAllWindows()
except:
print('Cannot calculate the value of velocity,steplength,stride left')
#PRINTING VELOCITY ,STRIDE LENGTH, STEP LENGTH
print('Velocity :' ,round(velocity/39.3701,2))
print('Stride Length :',round(step_length/39.3701,2))
print('Step Length :',round(stride_length/39.3701,2))
# In[ ]: