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
|
# Copyright 2014 The Android Open Source Project
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import its.image
import its.caps
import its.device
import its.objects
import its.target
import numpy
import os.path
def main():
"""Test that raw streams are not croppable.
"""
NAME = os.path.basename(__file__).split(".")[0]
DIFF_THRESH = 0.05
with its.device.ItsSession() as cam:
props = cam.get_camera_properties()
if (not its.caps.compute_target_exposure(props) or
not its.caps.raw16(props)):
print "Test skipped"
return
a = props['android.sensor.info.activeArraySize']
ax, ay = a["left"], a["top"]
aw, ah = a["right"] - a["left"], a["bottom"] - a["top"]
print "Active sensor region: (%d,%d %dx%d)" % (ax, ay, aw, ah)
# Capture without a crop region.
# Use a manual request with a linear tonemap so that the YUV and RAW
# should look the same (once converted by the its.image module).
e, s = its.target.get_target_exposure_combos(cam)["minSensitivity"]
req = its.objects.manual_capture_request(s,e, True)
cap1_raw, cap1_yuv = cam.do_capture(req, cam.CAP_RAW_YUV)
# Capture with a center crop region.
req["android.scaler.cropRegion"] = {
"top": ay + ah/3,
"left": ax + aw/3,
"right": ax + 2*aw/3,
"bottom": ay + 2*ah/3}
cap2_raw, cap2_yuv = cam.do_capture(req, cam.CAP_RAW_YUV)
reported_crops = []
imgs = {}
for s,cap in [("yuv_full",cap1_yuv), ("raw_full",cap1_raw),
("yuv_crop",cap2_yuv), ("raw_crop",cap2_raw)]:
img = its.image.convert_capture_to_rgb_image(cap, props=props)
its.image.write_image(img, "%s_%s.jpg" % (NAME, s))
r = cap["metadata"]["android.scaler.cropRegion"]
x, y = a["left"], a["top"]
w, h = a["right"] - a["left"], a["bottom"] - a["top"]
reported_crops.append((x,y,w,h))
imgs[s] = img
print "Crop on %s: (%d,%d %dx%d)" % (s, x,y,w,h)
# The metadata should report uncropped for all shots (since there is
# at least 1 uncropped stream in each case).
for (x,y,w,h) in reported_crops:
assert((ax,ay,aw,ah) == (x,y,w,h))
# Also check the image content; 3 of the 4 shots should match.
# Note that all the shots are RGB below; the variable names correspond
# to what was captured.
# Average the images down 4x4 -> 1 prior to comparison to smooth out
# noise.
# Shrink the YUV images an additional 2x2 -> 1 to account for the size
# reduction that the raw images went through in the RGB conversion.
imgs2 = {}
for s,img in imgs.iteritems():
h,w,ch = img.shape
m = 4
if s in ["yuv_full", "yuv_crop"]:
m = 8
img = img.reshape(h/m,m,w/m,m,3).mean(3).mean(1).reshape(h/m,w/m,3)
imgs2[s] = img
print s, img.shape
# Strip any border pixels from the raw shots (since the raw images may
# be larger than the YUV images). Assume a symmetric padded border.
xpad = (imgs2["raw_full"].shape[1] - imgs2["yuv_full"].shape[1]) / 2
ypad = (imgs2["raw_full"].shape[0] - imgs2["yuv_full"].shape[0]) / 2
wyuv = imgs2["yuv_full"].shape[1]
hyuv = imgs2["yuv_full"].shape[0]
imgs2["raw_full"]=imgs2["raw_full"][ypad:ypad+hyuv:,xpad:xpad+wyuv:,::]
imgs2["raw_crop"]=imgs2["raw_crop"][ypad:ypad+hyuv:,xpad:xpad+wyuv:,::]
print "Stripping padding before comparison:", xpad, ypad
for s,img in imgs2.iteritems():
its.image.write_image(img, "%s_comp_%s.jpg" % (NAME, s))
# Compute image diffs.
diff_yuv = numpy.fabs((imgs2["yuv_full"] - imgs2["yuv_crop"])).mean()
diff_raw = numpy.fabs((imgs2["raw_full"] - imgs2["raw_crop"])).mean()
print "YUV diff (crop vs. non-crop):", diff_yuv
print "RAW diff (crop vs. non-crop):", diff_raw
assert(diff_yuv > DIFF_THRESH)
assert(diff_raw < DIFF_THRESH)
if __name__ == '__main__':
main()
|
