-
Notifications
You must be signed in to change notification settings - Fork 0
/
raster.py
209 lines (187 loc) · 10.2 KB
/
raster.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
from __future__ import print_function
import time
import argparse
import numpy as np
import picamera
from openflexure_stage import OpenFlexureStage
from openflexure_microscope import load_microscope
from openflexure_microscope.microscope import picamera_supports_lens_shading
import scipy
from scipy import ndimage, signal
from contextlib import contextmanager, closing
import data_file
import cv2
from camera_stuff import find_template
from PIL import Image
def measure_txy(ms, start_t, templ8):
txy = np.zeros((1, 3))
txy[0, 0] = time.time() - start_t
frame = ms.rgb_image().astype(np.float32)
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
txy[0, 1:], corr = find_template(templ8, frame - np.mean(frame), return_corr = True, fraction=0.5)
camera.stop_preview()
cv2.imshow("corr", corr * 255.0 / np.max(corr))
cv2.waitKey(1000)
camera.start_preview()
return txy, frame, corr
def raster_standard(ms, area, step, start_t, templ8, backlash, experiment_group, templ8_position):
def move_overlay(cx, cy):
"""move the overlay to show a shift of cx,cy camera pixels"""
x = int(960 + (cx - templ8_position[0, 1] - 176)*2.25)
y = int(540 + (cy - templ8_position[0, 2] - 96)*2.25)
overlay.window = (x, y, int(352*2.25), int(192*2.25))
if backlash == 0:
stage.backlash = False
else:
stage.backlash = backlash
standard_group = df.new_group("standard_raster", "standard_grid_scan", parent = experiment_group)
standard_group.attrs['area'] = area
standard_group.attrs['step'] = step
stage.move_rel([-area[0]/2, 0, -area[1]/2])
for i in range(0, area[1], step):
for j in range(0, area[0], step):
data_group = df.new_group("data", "from standard_grid_scan", parent = standard_group)
data_group['stage_position'] = stage.position
txy, frame, corr = measure_txy(ms, start_t, templ8)
move_overlay(*txy[0, 1:3])
data_group['cam_position'] = txy
stage.move_rel([step, 0, 0])
time.sleep(0.1)
imgfile_location = "/home/pi/summer/drift/raster/standard_raster_%s.jpg" % time.strftime("%02Y.%02m.%02d_%02H:%02M:%02S")
cv2.imwrite(imgfile_location, frame)
imgfile_location = "/home/pi/summer/drift/raster/standard_raster_corr_%s.jpg" % time.strftime("%02Y.%02m.%02d_%02H:%02M:%02S")
cv2.imwrite(imgfile_location, corr * 255.0 / np.max(corr))
data_group = df.new_group("data", "from standard_grid_scan", parent = standard_group)
data_group['stage_position'] = stage.position
txy, frame, corr = measure_txy(ms, start_t, templ8)
move_overlay(*txy[0, 1:3])
data_group['cam_position'] = txy
stage.move_rel([-area[0], 0, step])
time.sleep(0.1)
imgfile_location = "/home/pi/summer/drift/raster/standard_raster_%s.jpg" % time.strftime("%02Y.%02m.%02d_%02H:%02M:%02S")
cv2.imwrite(imgfile_location, frame)
imgfile_location = "/home/pi/summer/drift/raster/standard_raster_corr_%s.jpg" % time.strftime("%02Y.%02m.%02d_%02H:%02M:%02S")
cv2.imwrite(imgfile_location, corr * 255.0 / np.max(corr))
for j in range(0, area[0], step):
data_group = df.new_group("data", "from standard_grid_scan", parent = standard_group)
data_group['stage_position'] = stage.position
txy, frame, corr = measure_txy(ms, start_t, templ8)
move_overlay(*txy[0, 1:3])
data_group['cam_position'] = txy
stage.move_rel([step, 0, 0])
time.sleep(0.1)
imgfile_location = "/home/pi/summer/drift/raster/standard_raster_%s.jpg" % time.strftime("%02Y.%02m.%02d_%02H:%02M:%02S")
cv2.imwrite(imgfile_location, frame)
imgfile_location = "/home/pi/summer/drift/raster/standard_raster_corr_%s.jpg" % time.strftime("%02Y.%02m.%02d_%02H:%02M:%02S")
cv2.imwrite(imgfile_location, corr * 255.0 / np.max(corr))
data_group = df.new_group("data", "from standard_grid_scan", parent = standard_group)
data_group['stage_position'] = stage.position
txy, frame, corr = measure_txy(ms, start_t, templ8)
move_overlay(*txy[0, 1:3])
data_group['cam_position'] = txy
stage.move_abs(initial_stage_position)
def raster_snake(ms, area, step, start_t, templ8, backlash, expeiment_group, templ8_position):
def move_overlay(cx, cy):
"""move the overlay to show a shift of cx,cy camera pixels"""
x = int(960 + (cx - templ8_position[0, 1] - 176)*2.25)
y = int(540 + (cy - templ8_position[0, 2] - 96)*2.25)
overlay.window = (x, y, int(352*2.25), int(192*2.25))
if backlash == 0:
stage.backlash = False
else:
stage.backlash = backlash
snake_group = df.new_group("snake_raster", "snake_grid_scan", parent = experiment_group)
snake_group.attrs['area'] = area
snake_group.attrs['step'] = step
stage.move_rel([-area[0]/2, 0, -area[1]/2])
for i in range(0, area[1]/2, step):
for j in range(0, area[0], step):
data_group = df.new_group("data", "from snake_grid_scan", parent = snake_group)
data_group['stage_position'] = stage.position
txy, frame, corr = measure_txy(ms, start_t, templ8)
move_overlay(*txy[0, 1:3])
data_group['cam_position'] = txy
stage.move_rel([step, 0, 0])
time.sleep(0.1)
imgfile_location = "/home/pi/summer/drift/raster/snake_raster_%s.jpg" % time.strftime("%02Y.%02m.%02d_%02H:%02M:%02S")
cv2.imwrite(imgfile_location, frame)
imgfile_location = "/home/pi/summer/drift/raster/snake_raster_corr_%s.jpg" % time.strftime("%02Y.%02m.%02d_%02H:%02M:%02S")
cv2.imwrite(imgfile_location, corr * 255.0 / np.max(corr))
data_group = df.new_group("data", "from snake_grid_scan", parent = snake_group)
data_group['stage_position'] = stage.position
txy, frame, corr = measure_txy(ms, start_t, templ8)
move_overlay(*txy[0, 1:3])
data_group['cam_position'] = txy
stage.move_rel([0, 0, step])
time.sleep(0.1)
imgfile_location = "/home/pi/summer/drift/raster/snake_raster_%s.jpg" % time.strftime("%02Y.%02m.%02d_%02H:%02M:%02S")
cv2.imwrite(imgfile_location, frame)
imgfile_location = "/home/pi/summer/drift/raster/snake_raster_corr_%s.jpg" % time.strftime("%02Y.%02m.%02d_%02H:%02M:%02S")
cv2.imwrite(imgfile_location, corr * 255.0 / np.max(corr))
for j in range(0, area[0], step):
data_group = df.new_group("data", "from snake_grid_scan", parent = snake_group)
data_group['stage_position'] = stage.position
txy, frame, corr = measure_txy(ms, start_t, templ8)
move_overlay(*txy[0, 1:3])
data_group['cam_position'] = txy
stage.move_rel([-step, 0, 0])
time.sleep(0.1)
imgfile_location = "/home/pi/summer/drift/raster/snake_raster_%s.jpg" % time.strftime("%02Y.%02m.%02d_%02H:%02M:%02S")
cv2.imwrite(imgfile_location, frame)
imgfile_location = "/home/pi/summer/drift/raster/snake_raster_corr_%s.jpg" % time.strftime("%02Y.%02m.%02d_%02H:%02M:%02S")
cv2.imwrite(imgfile_location, corr * 255.0 / np.max(corr))
data_group = df.new_group("data", "from snake_grid_scan", parent = snake_group)
data_group['stage_position'] = stage.position
txy, frame, corr = measure_txy(ms, start_t, templ8)
move_overlay(*txy[0, 1:3])
data_group['cam_position'] = txy
stage.move_rel([0, 0, step])
time.sleep(0.1)
imgfile_location = "/home/pi/summer/drift/raster/snake_raster_%s.jpg" % time.strftime("%02Y.%02m.%02d_%02H:%02M:%02S")
cv2.imwrite(imgfile_location, frame)
imgfile_location = "/home/pi/summer/drift/raster/snake_raster_corr_%s.jpg" % time.strftime("%02Y.%02m.%02d_%02H:%02M:%02S")
cv2.imwrite(imgfile_location, corr * 255.0 / np.max(corr))
stage.move_abs(initial_stage_position)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Grid scan")
parser.add_argument("area", type=int, nargs = 2, help="Area of scan measured in steps")
parser.add_argument("step", type=int, help="Displacement between measurments measured in steps")
parser.add_argument("--backlash", type=int, default = 256, help="Backlash correction on or off")
args = parser.parse_args()
with load_microscope("microscope_settings.npz", dummy_stage = False) as ms, \
closing(data_file.Datafile(filename = "raster.hdf5")) as df:
assert picamera_supports_lens_shading(), "You need the updated picamera module with lens shading!"
camera = ms.camera
stage = ms.stage
area = args.area
step = args.step
backlash = args.backlash
camera.resolution = (640, 480)
camera.start_preview(resolution=(640,480))
initial_stage_position = stage.position
stage.move_rel([-backlash, -backlash, -backlash])
stage.move_rel([backlash, backlash, backlash])
experiment_group = df.new_group("raster", "performes grid scan")
image = ms.rgb_image().astype(np.float32)
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
mean = np.mean(image)
templ8 = (image - mean)[144:-144, 144:-144]
experiment_group['template_image'] = templ8
experiment_group['sample_image'] = image
imgfile_location = "/home/pi/summer/drift/calibration/raster_templ8.jpg"
cv2.imwrite(imgfile_location, templ8)
imgfile_location = "/home/pi/summer/drift/calibration/raster_image.jpg"
cv2.imwrite(imgfile_location, image)
img = Image.open("/home/pi/summer/drift/calibration/raster_templ8.jpg")
pad = Image.new('RGB', (352, 192)) #Tuple must be multiples of 32 and 16
pad.paste(img, (0, 0))
overlay = camera.add_overlay(pad.tobytes(), size = (352, 192))
overlay.alpha = 128
overlay.fullscreen = False
overlay.layer = 3
start_t = time.time()
templ8_position, frame, corr = measure_txy(ms, start_t, templ8)
raster_standard(ms, area, step, start_t, templ8, backlash, experiment_group, templ8_position)
raster_snake(ms, area, step, start_t, templ8, backlash, experiment_group, templ8_position)
camera.stop_preview()
print("Done")