| /external/opencv3/samples/cpp/tutorial_code/core/mat_the_basic_image_container/ |
| mat_the_basic_image_container.cpp | 16 << "Shows how output can be formated to OpenCV, python, numpy, csv and C styles." << endl
80 //! [out-numpy]
81 cout << "R (numpy) = " << endl << format(R, Formatter::FMT_NUMPY ) << endl << endl;
82 //! [out-numpy]
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| /external/opencv3/samples/python2/ |
| color_histogram.py | 3 import numpy as np
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| distrans.py | 15 import numpy as np
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| inpaint.py | 18 import numpy as np
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| kmeans.py | 13 import numpy as np
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| mser.py | 17 import numpy as np
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| texture_flow.py | 13 import numpy as np
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| browse.py | 15 import numpy as np
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| calibrate.py | 3 import numpy as np
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| floodfill.py | 17 import numpy as np
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| /external/opencv3/doc/py_tutorials/py_imgproc/py_histograms/py_histogram_equalization/ |
| py_histogram_equalization.markdown | 26 after reading that. Instead, here we will see its Numpy implementation. After that, we will see
30 import numpy as np
53 as given in wiki page. But I have used here, the masked array concept array from Numpy. For masked
54 array, all operations are performed on non-masked elements. You can read more about it from Numpy
124 import numpy as np
143 2. [Masked Arrays in Numpy](http://docs.scipy.org/doc/numpy/reference/maskedarray.html)
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| /external/autotest/server/brillo/feedback/ |
| closed_loop_audio_client.py | 8 import numpy namespace
383 fft_golden = numpy.fft.rfft(golden_data)
384 fft_rec = numpy.fft.rfft(rec_data)
385 fft_freqs_golden = numpy.fft.rfftfreq(
387 fft_freqs_rec = numpy.fft.rfftfreq(len(rec_data),
391 freq_golden = fft_freqs_golden[numpy.argmax(numpy.abs(fft_golden))]
392 abs_fft_rec = numpy.abs(fft_rec)
393 freq_rec = fft_freqs_rec[numpy.argmax(abs_fft_rec)]
403 fft_rec_peak_val = numpy.max(abs_fft_rec
[all...] |
| /cts/apps/CameraITS/tests/scene3/ |
| test_reprocess_edge_enhancement.py | 23 import numpy namespace
77 ret["sharpness"] = numpy.mean(sharpness_list)
168 assert(numpy.isclose(sharpness_regular[3], sharpness_regular[0],
185 assert(numpy.isclose(sharpnesses_reprocess[reprocess_format][3],
201 assert(numpy.isclose(sharpnesses_reprocess[reprocess_format][2] /
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| /external/opencv3/doc/py_tutorials/py_imgproc/py_histograms/py_2d_histogram/ |
| py_2d_histogram.markdown | 39 import numpy as np
48 2D Histogram in Numpy
51 Numpy also provides a specific function for this : **np.histogram2d()**. (Remember, for 1D histogram
55 import numpy as np
89 import numpy as np
|
| /external/v8/tools/ignition/ |
| bytecode_dispatches_report.py | 13 import numpy namespace
122 counters_matrix = numpy.empty([len(labels), len(labels)], dtype=int)
131 counters_matrix = numpy.flipud(counters_matrix)
149 ticks=numpy.arange(0.5, len(xlabels)),
157 ticks=numpy.arange(0.5, len(ylabels)),
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| /cts/apps/CameraITS/tests/inprog/ |
| test_burst_sameness_auto.py | 20 import numpy namespace
63 imgs = numpy.empty([FRAMES,h,w,3])
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| /cts/apps/CameraITS/tests/scene1/ |
| test_burst_sameness_manual.py | 21 import numpy namespace
56 imgs = numpy.empty([FRAMES,h,w,3])
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| test_ev_compensation_advanced.py | 23 import numpy namespace
93 avg_diff = abs(numpy.array(luma_diffs)).mean()
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| test_capture_result.py | 20 import numpy namespace
101 xs = numpy.array([range(w_map)] * h_map).reshape(h_map, w_map)
102 ys = numpy.array([[i]*w_map for i in range(h_map)]).reshape(
104 zs = numpy.array(lsc_map[ch::4]).reshape(h_map, w_map)
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| /external/autotest/client/cros/ |
| http_speed.py | 9 import numpy.random namespace
40 data = numpy.random.bytes(size)
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| /external/autotest/client/site_tests/power_CameraSuspend/ |
| power_CameraSuspend.py | 6 import numpy namespace
79 if last_image is not None and numpy.array_equal(image, last_image):
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| /cts/suite/audio_quality/test_description/processing/ |
| playback_thd.py | 18 import numpy as np
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| /external/chromium-trace/catapult/experimental/statistical_analysis/ |
| results_stats_unittest.py | 14 import numpy as np
128 raise ImportError('This function requires Numpy.')
138 self.skipTest("Numpy is not installed.")
173 self.skipTest("Numpy is not installed.")
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| /external/chromium-trace/catapult/telemetry/telemetry/internal/util/ |
| external_modules.py | 11 'numpy': (version.StrictVersion('1.6.1'), None),
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| /external/opencv3/doc/py_tutorials/py_ml/py_knn/py_knn_opencv/ |
| py_knn_opencv.markdown | 22 import numpy as np
32 # Make it into a Numpy array. It size will be (50,100,20,20)
59 read this data from a file and start classification. You can do it with the help of some Numpy
91 import numpy as np
|
