Skip to content
This repository was archived by the owner on Sep 24, 2023. It is now read-only.

Commit 6bcdc7f

Browse files
julzerinosjulzerinos
committed
Initial commit
0 parents  commit 6bcdc7f

File tree

3 files changed

+248
-0
lines changed

3 files changed

+248
-0
lines changed

Diff for: ‎.gitignore

+3
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,3 @@
1+
venv/
2+
multi_plant/
3+
*.vscode

Diff for: ‎PlantDetector.py

+245
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,245 @@
1+
import cv2 as cv
2+
import numpy as np
3+
import os
4+
import matplotlib.pyplot as plot
5+
import copy
6+
7+
8+
class constants:
9+
class HSV:
10+
max_value = 255
11+
max_value_H = 360//2
12+
13+
low_H = 30
14+
low_S = 30
15+
low_V = 30
16+
high_H = 75
17+
high_S = 255
18+
high_V = 255
19+
20+
low_H_name = 'Low H'
21+
low_S_name = 'Low S'
22+
low_V_name = 'Low V'
23+
high_H_name = 'High H'
24+
high_S_name = 'High S'
25+
high_V_name = 'High V'
26+
27+
class window:
28+
window1 = 'Altered'
29+
window2 = 'Original'
30+
31+
class asth:
32+
font = cv.FONT_HERSHEY_SIMPLEX
33+
34+
class cntr:
35+
next_k = ord('m')
36+
prev_k = ord('n')
37+
exit_k = 27
38+
39+
m1_k = ord('1')
40+
m2_k = ord('2')
41+
m3_k = ord('3')
42+
m4_k = ord('4')
43+
m5_k = ord('5')
44+
45+
class flow:
46+
HSV_filtering_and_watershed = True
47+
bg_fg_segm = False
48+
49+
50+
class PlantDetector:
51+
52+
def on_low_H_thresh_trackbar(self, val):
53+
self.c.HSV.low_H = val
54+
self.c.HSV.low_H = min(self.c.HSV.high_H-1, self.c.HSV.low_H)
55+
cv.setTrackbarPos(self.c.HSV.low_H_name, self.window1, self.c.HSV.low_H)
56+
def on_high_H_thresh_trackbar(self, val):
57+
self.c.HSV.high_H = val
58+
self.c.HSV.high_H = max(self.c.HSV.high_H, self.c.HSV.low_H+1)
59+
cv.setTrackbarPos(self.c.HSV.high_H_name, self.window1, self.c.HSV.high_H)
60+
def on_low_S_thresh_trackbar(self, val):
61+
self.c.HSV.low_S = val
62+
self.c.HSV.low_S = min(self.c.HSV.high_S-1, self.c.HSV.low_S)
63+
cv.setTrackbarPos(self.c.HSV.low_S_name, self.window1, self.c.HSV.low_S)
64+
def on_high_S_thresh_trackbar(self, val):
65+
self.c.HSV.high_S = val
66+
self.c.HSV.high_S = max(self.c.HSV.high_S, self.c.HSV.low_S+1)
67+
cv.setTrackbarPos(self.c.HSV.high_S_name, self.window1, self.c.HSV.high_S)
68+
def on_low_V_thresh_trackbar(self, val):
69+
self.c.HSV.low_V = val
70+
self.c.HSV.low_V = min(self.c.HSV.high_V-1, self.c.HSV.low_V)
71+
cv.setTrackbarPos(self.c.HSV.low_V_name, self.window1, self.c.HSV.low_V)
72+
def on_high_V_thresh_trackbar(self, val):
73+
self.c.HSV.high_V = val
74+
self.c.HSV.high_V = max(self.c.HSV.high_V, self.c.HSV.low_V+1)
75+
cv.setTrackbarPos(self.c.HSV.high_V_name, self.window1, self.c.HSV.high_V)
76+
77+
def prepare_plant_collection(self, src = 'multi_plant'):
78+
plants = []
79+
plant_groups = dict()
80+
files = os.listdir( src )
81+
files.sort()
82+
for fl in files:
83+
input_im = cv.imread(src + '/' + fl, cv.IMREAD_COLOR)
84+
if ( input_im is None ):
85+
exit()
86+
87+
plants.append({
88+
'p': input_im,
89+
'n': fl
90+
})
91+
if fl.split('_')[2] not in plant_groups:
92+
plant_groups[fl.split('_')[2]] = []
93+
plant_groups[fl.split('_')[2]].append(input_im)
94+
95+
return plants, plant_groups
96+
97+
def __init__(self, src = 'multi_plant'):
98+
self.c = constants()
99+
self.window1 = self.c.window.window1
100+
self.window2 = self.c.window.window2
101+
102+
cv.namedWindow(self.window1)
103+
cv.namedWindow(self.window2)
104+
105+
cv.moveWindow(self.window2, 800, 0)
106+
107+
cv.createTrackbar(self.c.HSV.low_H_name, self.window1 , self.c.HSV.low_H, self.c.HSV.max_value_H, self.on_low_H_thresh_trackbar)
108+
cv.createTrackbar(self.c.HSV.high_H_name, self.window1 , self.c.HSV.high_H,self.c.HSV.max_value_H, self.on_high_H_thresh_trackbar)
109+
cv.createTrackbar(self.c.HSV.low_S_name, self.window1 , self.c.HSV.low_S, self.c.HSV.max_value, self.on_low_S_thresh_trackbar)
110+
cv.createTrackbar(self.c.HSV.high_S_name, self.window1 , self.c.HSV.high_S,self.c.HSV.max_value, self.on_high_S_thresh_trackbar)
111+
cv.createTrackbar(self.c.HSV.low_V_name, self.window1 , self.c.HSV.low_V, self.c.HSV.max_value, self.on_low_V_thresh_trackbar)
112+
cv.createTrackbar(self.c.HSV.high_V_name, self.window1 , self.c.HSV.high_V,self.c.HSV.max_value, self.on_high_V_thresh_trackbar)
113+
114+
self.plants, self.plant_groups = self.prepare_plant_collection(src)
115+
116+
if self.c.flow.bg_fg_segm is False:
117+
return
118+
119+
if False:
120+
backSub = cv.createBackgroundSubtractorMOG2()
121+
else:
122+
backSub = cv.createBackgroundSubtractorKNN()
123+
124+
for key in self.plant_groups:
125+
if True:
126+
backSub = cv.createBackgroundSubtractorMOG2(history = 60, detectShadows=True)
127+
else:
128+
backSub = cv.createBackgroundSubtractorKNN()
129+
fgMask = None
130+
for i, image in enumerate(self.plant_groups[key]):
131+
fgMask = backSub.apply(image)
132+
self.plant_groups[key][i] = fgMask
133+
# self.plant_groups[key] = backSub.getBackgroundImage()
134+
135+
def parse(self):
136+
key = 0
137+
mode = 0
138+
i = 0
139+
140+
while key != self.c.cntr.exit_k:
141+
142+
image = copy.deepcopy(self.plants[i]['p'])
143+
144+
if self.c.flow.HSV_filtering_and_watershed:
145+
if not self.c.flow.bg_fg_segm:
146+
wt_image, markers, im_threshold = self.HSV_filtering_and_watershed(image)
147+
else:
148+
wt_image, markers, im_threshold = self.HSV_filtering_and_watershed(
149+
cv.cvtColor(self.plant_groups[self.plants[i]['n'].split('_')[2]][i % 60], cv.COLOR_GRAY2BGR)
150+
)
151+
152+
if mode == 5 and self.c.flow.bg_fg_segm:
153+
alt = image
154+
mask = self.plant_groups[self.plants[i]['n'].split('_')[2]]
155+
alt[mask == 255] = [0, 0, 0]
156+
elif mode == 4 and self.c.flow.bg_fg_segm:
157+
alt = self.plant_groups[self.plants[i]['n'].split('_')[2]][i % 60]
158+
text = f'Fore/Background segmentation {self.plants[i]["n"]}'
159+
tcol = (255,255,255)
160+
elif mode == 3 and self.c.flow.HSV_filtering_and_watershed:
161+
alt = markers
162+
text = f'Watershed algorithm areas {self.plants[i]["n"]}'
163+
tcol = (0, 0, 0)
164+
165+
elif mode == 2 and self.c.flow.HSV_filtering_and_watershed:
166+
alt = wt_image
167+
text = f'Watershed algorithm borders {self.plants[i]["n"]}'
168+
tcol = (0, 0, 0)
169+
elif mode == 1 and self.c.flow.HSV_filtering_and_watershed:
170+
alt = im_threshold
171+
text = f'HSV inRange threshold {self.plants[i]["n"]}'
172+
tcol = (255,255,255)
173+
else:
174+
alt = self.plants[i]['p']
175+
text = f'Original {self.plants[i]["n"]}'
176+
tcol = (0, 0, 0)
177+
178+
cv.putText(alt, text, (0,20), self.c.asth.font, .5, tcol, 1)
179+
cv.putText(self.plants[i]['p'], 'Original', (0,20), self.c.asth.font, .5, (0,0,0), 1)
180+
181+
cv.imshow(self.window1, alt)
182+
cv.imshow(self.window2, self.plants[i]['p'])
183+
184+
key = cv.waitKey(10)
185+
186+
if key == self.c.cntr.prev_k and i > 0:
187+
i -= 1
188+
if key == self.c.cntr.next_k and i < len(self.plants) - 1:
189+
i += 1
190+
191+
if key == self.c.cntr.m1_k:
192+
mode = 1
193+
elif key == self.c.cntr.m2_k:
194+
mode = 2
195+
elif key == self.c.cntr.m3_k:
196+
mode = 3
197+
elif key == self.c.cntr.m4_k:
198+
mode = 4
199+
elif key == self.c.cntr.m5_k:
200+
mode = 5
201+
202+
def HSV_filtering_and_watershed(self, input_im):
203+
204+
im_threshold = cv.inRange(cv.cvtColor(input_im, cv.COLOR_BGR2HSV), (self.c.HSV.low_H, self.c.HSV.low_S, self.c.HSV.low_V), (self.c.HSV.high_H, self.c.HSV.high_S, self.c.HSV.high_V))
205+
206+
kernel = np.ones((3,3),np.uint8)
207+
opening = cv.morphologyEx(im_threshold,cv.MORPH_OPEN,kernel, iterations = 2)
208+
209+
sure_bg = cv.dilate(opening,kernel,iterations=7)
210+
211+
dist_transform = cv.distanceTransform(opening,cv.DIST_L2,5)
212+
_, sure_fg = cv.threshold(dist_transform,0.3*dist_transform.max(),255,0)
213+
214+
sure_fg = np.uint8(sure_fg)
215+
unknown = cv.subtract(sure_bg,sure_fg)
216+
217+
# Marker labelling
218+
_, markers = cv.connectedComponents(sure_fg)
219+
# Add one to all labels so that sure background is not 0, but 1
220+
markers = markers+1
221+
# Now, mark the region of unknown with zero
222+
markers[unknown==255] = 0
223+
224+
markers = cv.watershed(input_im,markers)
225+
input_im[markers == -1] = [255,0,0]
226+
227+
fig = plot.figure()
228+
plot.imshow(markers)
229+
fig.canvas.draw()
230+
231+
img = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8,
232+
sep='')
233+
img = img.reshape(fig.canvas.get_width_height()[::-1] + (3,))
234+
markers = cv.cvtColor(img,cv.COLOR_RGB2BGR)
235+
236+
plot.close(fig)
237+
238+
return input_im, markers, im_threshold
239+
240+
241+
# Main
242+
243+
plDt = PlantDetector()
244+
plDt.parse()
245+

Diff for: ‎__init__.py

Whitespace-only changes.

0 commit comments

Comments
 (0)