| /cts/apps/CameraITS/tests/inprog/ |
| test_param_black_level_lock.py | 22 import numpy namespace
58 hist,_ = numpy.histogram(yimg*255, 256, (0,256))
59 modes.append(numpy.argmax(hist))
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| test_burst_sameness_fullres_auto.py | 20 import numpy namespace
61 imgs = numpy.empty([FRAMES,h/4,w/4,3])
84 delta_max_pos = numpy.max(deltas)
85 delta_max_neg = numpy.min(deltas)
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| /cts/apps/CameraITS/tests/scene1/ |
| test_tonemap_sequence.py | 20 import numpy namespace
61 deltas = [numpy.max(numpy.fabs(means[i+1]-means[i])) \
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| test_linearity.py | 20 import numpy namespace
43 gamma_lut = numpy.array(
45 inv_gamma_lut = numpy.array(
92 line,residuals,_,_,_ = numpy.polyfit(range(5),means,1,full=True)
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| test_param_noise_reduction.py | 22 import numpy namespace
98 rgb_snrs = [numpy.mean(r_snrs), numpy.mean(g_snrs), numpy.mean(b_snrs)]
136 assert(numpy.isclose(snrs[j][4], snrs[j][3],
140 assert(numpy.isclose(snrs[j][4], snrs[j][0],
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| test_reprocess_noise_reduction.py | 23 import numpy namespace
118 rgb_snrs = [numpy.mean(r_snrs),
119 numpy.mean(g_snrs),
120 numpy.mean(b_snrs)]
159 assert(numpy.isclose(snrs[j][4], snrs[j][3],
163 assert(numpy.isclose(snrs[j][4], snrs[j][0],
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| test_param_shading_mode.py | 20 import numpy namespace
106 assert(numpy.allclose(shading_maps[0][i], reference_maps[0]))
111 assert(numpy.allclose(shading_maps[mode][i],
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| /external/autotest/client/site_tests/hardware_TLBMissCost/ |
| hardware_TLBMissCost.py | 7 import numpy namespace
61 results['pw-cycles-per-miss'] = numpy.average(pw_cycles_per_miss)
65 results['pw-cycle-percent'] = numpy.average(pw_cycle_percent)
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| perf_measurement.py | 7 import numpy namespace
|
| /external/opencv3/doc/py_tutorials/py_core/py_optimization/ |
| py_optimization.markdown | 110 You can see that, x = 5 ; y = x\*x is fastest and it is around 20x faster compared to Numpy. If you
111 consider the array creation also, it may reach upto 100x faster. Cool, right? *(Numpy devs are
114 @note Python scalar operations are faster than Numpy scalar operations. So for operations including
115 one or two elements, Python scalar is better than Numpy arrays. Numpy takes advantage when size of
128 See, OpenCV function is nearly 25x faster than Numpy function.
130 @note Normally, OpenCV functions are faster than Numpy functions. So for same operation, OpenCV
131 functions are preferred. But, there can be exceptions, especially when Numpy works with views
144 There are several techniques and coding methods to exploit maximum performance of Python and Numpy.
151 2. Vectorize the algorithm/code to the maximum possible extent because Numpy and OpenCV ar
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| /external/autotest/client/cros/audio/ |
| audio_analysis.py | 8 import numpy namespace
35 @returns: A numpy array containing normalized signal. The normalized signal
39 signal = numpy.array(signal)
73 signal_rms = numpy.linalg.norm(signal) / numpy.sqrt(len(signal))
80 y_conv_w = signal * numpy.hanning(len(signal))
86 y_f = 2.0 / length * numpy.fft.rfft(y_conv_w)
89 abs_y_f = numpy.abs(y_f)
117 @returns: A numpy array containing frequency corresponding to
118 numpy.fft.rfft result at each index
[all...] |
| /external/opencv3/samples/python2/ |
| gaussian_mix.py | 3 import numpy as np
4 from numpy import random
|
| /cts/apps/CameraITS/tests/sensor_fusion/ |
| test_sensor_fusion.py | 27 import numpy namespace
96 # RGB images as numpy arrays.
165 candidates = numpy.arange(-50,50.5,0.5).tolist()
185 a,b,c = numpy.polyfit(candidates, dists, 2)
193 xfit = numpy.arange(candidates[0], candidates[-1], 0.05).tolist()
239 all_times = numpy.array([e["time"] for e in gyro_events])
240 all_rots = numpy.array([e["z"] for e in gyro_events])
270 gyro_rots = numpy.array(gyro_rots)
280 frames: List of N images (as RGB numpy arrays).
287 frame = (frame * 255.0).astype(numpy.uint8
[all...] |
| /external/opencv3/doc/py_tutorials/py_imgproc/py_transforms/py_fourier_transform/ |
| py_fourier_transform.markdown | 9 - To utilize the FFT functions available in Numpy
38 Fourier Transform in Numpy
41 First we will see how to find Fourier Transform using Numpy. Numpy has an FFT package to do this.
54 import numpy as np
103 region of the spectrum. Anyway we have seen how to find DFT, IDFT etc in Numpy. Now let's see how to
120 import numpy as np
169 @note As usual, OpenCV functions **cv2.dft()** and **cv2.idft()** are faster than Numpy
170 counterparts. But Numpy functions are more user-friendly. For more details about performance issues,
180 manually pad zeros. But for Numpy, you specify the new size of FFT calculation, and it wil
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| /external/opencv3/doc/py_tutorials/py_core/py_basic_ops/ |
| py_basic_ops.markdown | 14 Almost all the operations in this section is mainly related to Numpy rather than OpenCV. A good
15 knowledge of Numpy is required to write better optimized code with OpenCV.
25 >>> import numpy as np
50 Numpy is a optimized library for fast array calculations. So simply accessing each and every pixel
54 last 3 columns like that. For individual pixel access, Numpy array methods, array.item() and
108 ROI is again obtained using Numpy indexing. Here I am selecting the ball and copying it to another
133 to zero. You can simply use Numpy indexing, and that is more faster.
141 for Numpy indexing.
171 import numpy as np
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| /external/autotest/client/cros/cellular/mbim_compliance/ |
| mbim_channel_endpoint.py | 10 import numpy namespace
189 numpy.set_printoptions(formatter={'int':lambda x: hex(int(x))},
192 len(response), numpy.array(response))
209 numpy.set_printoptions(formatter={'int':lambda x: hex(int(x))},
213 actual_written, len(payload), numpy.array(payload))
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| /external/autotest/client/site_tests/power_SuspendStress/ |
| power_SuspendStress.py | 5 import logging, numpy, random, time namespace
68 keyvals[key + '_mean'] = numpy.mean(values)
69 keyvals[key + '_stddev'] = numpy.std(values)
70 keyvals[key + '_min'] = numpy.amin(values)
71 keyvals[key + '_max'] = numpy.amax(values)
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| /hardware/intel/common/omx-components/videocodec/libvpx_internal/libvpx/vp9/common/ |
| vp9_tapify.py | 11 import sys,string,os,re,math,numpy namespace
45 r,c = numpy.dot(affine,[y-radius, x-radius])
64 r,c = numpy.dot(affine,[y-7.5, x-7.5])
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| /cts/apps/CameraITS/tests/ |
| tutorial.py | 35 # Modules from the numpy, scipy, and matplotlib libraries. These are used for
36 # the image processing code, and images are represented as numpy arrays.
38 import numpy namespace
127 # Images are numpy arrays. The dimensions are (h,w,3) when indexing,
151 lut = numpy.array([2*i for i in xrange(65536)])
156 # is a numpy array, in row major order, and the pixel values are right-
159 mat = numpy.array([[1, 0, 0 ],
167 hist,_ = numpy.histogram(yimg*255, 256, (0,256))
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| /cts/suite/audio_quality/test_description/processing/ |
| calc_delay.py | 17 import numpy as np
18 import numpy.linalg namespace
|
| /external/opencv3/doc/py_tutorials/py_imgproc/py_histograms/py_histogram_begins/ |
| py_histogram_begins.markdown | 8 - Find histograms, using both OpenCV and Numpy functions
35 Now we have an idea on what is histogram, we can look into how to find this. Both OpenCV and Numpy
87 ### 2. Histogram Calculation in Numpy
89 Numpy also provides you a function, **np.histogram()**. So instead of calcHist() function, you can
94 hist is same as we calculated before. But bins will have 257 elements, because Numpy calculates bins
98 @sa Numpy has another function, **np.bincount()** which is much faster than (around 10X)
122 import numpy as np
136 import numpy as np
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| /external/opencv3/samples/cpp/ |
| cout_mat.cpp | 20 << "Output can be formated to OpenCV, matlab, python, numpy, csv and \n"
41 cout << "r (numpy) = \n" << format(r, Formatter::FMT_NUMPY) << ";" << endl << endl;
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| /system/extras/tests/sdcard/ |
| README | 21 If you want to graph the results you need gnuplot and numpy:
23 sudo apt-get install gnuplot python-numpy python-numeric
|
| /external/autotest/client/cros/camera/ |
| camera_utils.py | 6 import numpy as np
|
| /external/opencv3/doc/tutorials/introduction/linux_install/ |
| linux_install.markdown | 14 - Python 2.6 or later and Numpy 1.5 or later with developer packages (python-dev, python-numpy)
25 [optional] sudo apt-get install python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc1394-22-dev
89 /usr/lib/python\<version\>/dist-packages/numpy/core/include/
|
