/cts/apps/CameraITS/utils/ |
D | noise_model_utils.py | 28 import numpy as np namespace 68 def check_noise_model_shape(noise_model: np.ndarray) -> None: 87 noise_model: np.ndarray, 133 gains: np.ndarray, 134 sens_max_analog: np.ndarray, 135 ) -> np.ndarray: 149 digital_gains = np.maximum(gains / sens_max_analog, 1) 150 if not np.all(digital_gains == 1): 189 mean_img: np.ndarray, 190 var_img: np.ndarray, [all …]
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D | sensor_fusion_utils.py | 28 import numpy as np namespace 138 first_derivative = np.polyder(coefficients, m=1) 139 second_derivative = np.polyder(coefficients, m=2) 140 extrema = np.roots(first_derivative) 143 if np.polyval(second_derivative, extremum) > 0: 387 rotations_sum = np.cumsum(rotations) 410 gyro_times = np.array([e['time'] for e in gyro_events]) 411 all_gyro_rots = np.array([e['z'] for e in gyro_events]) 451 gyro_rots = np.array(gyro_rots) 470 x0 = (x-x.mean(0)) / np.sqrt(((x-x.mean(0))**2.0).sum()) [all …]
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D | capture_read_noise_utils.py | 32 import numpy as np namespace 124 iso_sq_values = np.linspace(iso_low**2, iso_high**2, _LINEAR_FIT_NUM_SAMPLES) 161 y_values = np.array( 165 x_ticks = np.linspace(iso_low**2, iso_high**2, _PLOT_AXIS_TICKS) 166 y_ticks = np.linspace(np.min(y_values), np.max(y_values), _PLOT_AXIS_TICKS) 217 mean = np.mean(channel_img[:, :, ch]) 218 var = np.var(channel_img[:, :, ch]) 260 coeffs = np.polyfit(iso_sq, norm_var, 1) 265 read_noise_coefficients_a = np.asarray(read_noise_coefficients_a) 266 read_noise_coefficients_b = np.asarray(read_noise_coefficients_b) [all …]
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D | imu_processing_utils_tests.py | 16 import numpy as np namespace 45 self.assertTrue(np.allclose(imu_processing_utils.calc_rv_drift(d), d_drift),
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D | camera_properties_utils.py | 22 import numpy as np namespace 791 ical = np.array(metadata['android.lens.intrinsicCalibration']) 794 ical = np.array(props['android.lens.intrinsicCalibration']) 827 k = np.array([[ical[0], ical[4], ical[2]], 846 t = np.array(props['android.lens.poseTranslation']) 870 rotation = np.array(props['android.lens.poseRotation']) 884 return np.array([[1-2*y**2-2*z**2, 2*x*y-2*z*w, 2*x*z+2*y*w], 899 dist = np.array(props['android.lens.distortion']) 907 cv2_distort = np.array([dist[0], dist[1], dist[3], dist[4], dist[2]])
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D | sensor_fusion_utils_tests.py | 19 import numpy as np namespace 95 cam_times = np.array(range(pts[0], pts[9], self._CAM_FRAME_TIME)) 99 cam_rots = np.array(cam_rots) 136 self.assertTrue(np.allclose( 169 for x in np.arange(-1.1, 1.1, 0.05):
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/cts/apps/CameraITS/tests/scene2_a/ |
D | test_effects.py | 20 import numpy as np namespace 93 y_min, y_max = np.amin(y)*_YUV_MAX, np.amax(y)*_YUV_MAX 99 u_min, u_max = np.amin(u) * _YUV_MAX, np.amax(u) * _YUV_MAX 100 v_min, v_max = np.amin(v) * _YUV_MAX, np.amax(v) * _YUV_MAX 112 u_min, u_max = np.amin(u)*_YUV_MAX, np.amax(u)*_YUV_MAX 113 v_min, v_max = np.amin(v)*_YUV_MAX, np.amax(v)*_YUV_MAX
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/cts/apps/CameraITS/tests/scene0/ |
D | test_test_patterns.py | 21 import numpy as np namespace 57 np.amax(r_tile), np.amax(gr_tile), np.amax(gb_tile), np.amax(b_tile)) 59 np.amin(r_tile), np.amin(gr_tile), np.amin(gb_tile), np.amin(b_tile)) 86 img = np.fliplr(img) 94 np.allclose(
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D | test_tonemap_curve.py | 20 import numpy as np namespace 114 if np.allclose(_COLOR_CHECKER[color], raw_means, atol=_RAW_ATOL): 166 raw_means = np.array(image_processing_utils.compute_image_means(raw_patch)) 167 raw_vars = np.array( 169 yuv_means = np.array(image_processing_utils.compute_image_means(yuv_patch)) 171 yuv_vars = np.array( 173 if not np.allclose(raw_means, yuv_means, atol=_RGB_MEAN_ATOL): 183 if not np.allclose(raw_vars, yuv_vars, atol=_RGB_VAR_ATOL):
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D | test_vibration_restriction.py | 21 import numpy as np namespace 77 var_w_vibration = np.var(magnitudes) 84 var_wo_vibration = np.var(magnitudes) 99 var_w_vibration_restricted = np.var(magnitudes)
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/cts/apps/CameraITS/tests/scene3/ |
D | test_flip_mirror.py | 22 import numpy as np namespace 75 if np.max(chart_uint8)-np.min(chart_uint8) < 255/8: 80 image_processing_utils.write_image(template[:, :, np.newaxis] / 255, 90 image_processing_utils.write_image(template[:, :, np.newaxis] / 255, 92 image_processing_utils.write_image(chart_uint8[:, :, np.newaxis] / 255, 102 comp_chart = np.flipud(center_uint8) 104 comp_chart = np.fliplr(center_uint8) 106 comp_chart = np.flipud(np.fliplr(center_uint8))
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D | test_imu_drift.py | 23 import numpy as np namespace 139 pylab.ylim([min([np.amin(x), np.amin(y), np.amin(z), -_GYRO_MEAN_THRESH]), 140 max([np.amax(x), np.amax(y), np.amax(x), _GYRO_MEAN_THRESH])]) 200 t = np.array([(e['time'] - events[0]['time'])*t_factor 202 x = np.array([e['x']*xyz_factor for e in events]) 203 y = np.array([e['y']*xyz_factor for e in events]) 204 z = np.array([e['z']*xyz_factor for e in events]) 277 gyro_var = np.var(samples)
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/cts/apps/CameraITS/tests/scene4/ |
D | test_multi_camera_alignment.py | 23 import numpy as np namespace 45 _TRANS_MATRIX_REF = np.array([0, 0, 0]) # translation matrix for ref cam is 000 278 k_x1 = np.dot(k[0, :], r[:, 0]) 279 k_x2 = np.dot(k[0, :], r[:, 1]) 280 k_x3 = z_w * np.dot(k[0, :], r[:, 2]) + np.dot(k[0, :], t) 281 k_y1 = np.dot(k[1, :], r[:, 0]) 282 k_y2 = np.dot(k[1, :], r[:, 1]) 283 k_y3 = z_w * np.dot(k[1, :], r[:, 2]) + np.dot(k[1, :], t) 285 a = np.array([[x*r[2][0]-k_x1, x*r[2][1]-k_x2], 287 b = np.array([[k_x3-x*c_1], [k_y3-y*c_1]]) [all …]
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/cts/tests/tests/uirendering/src/android/uirendering/cts/testclasses/ |
D | NinePatchTests.kt | 51 val np = with(ImageDecoder.createSource(activity.resources, R.drawable.padding_0)) { in <lambda>() constant 59 addCanvasClientWithoutUsingPicture(NinePatchCanvasClient(np, paint), hw) in <lambda>() 64 np.bitmap.recycle() in <lambda>() 69 val np = with(ImageDecoder.createSource(activity.resources, R.drawable.padding_0)) { in <lambda>() constant 80 NinePatchCanvasClient(np, paint).draw(canvas, TEST_WIDTH, TEST_HEIGHT) in <lambda>() 90 for (bitmap in arrayOf(filtered, unfiltered, noPaint, np.bitmap)) { in <lambda>()
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/cts/apps/CameraITS/tests/scene_video/ |
D | test_preview_frame_drop.py | 23 import numpy as np namespace 44 output = max(output, np.average(values[i:i+window_size])) 130 frame_deltas = np.array(video_processing_utils.get_frame_deltas( 132 frame_delta_max = np.max(frame_deltas) 133 frame_delta_min = np.min(frame_deltas) 134 frame_delta_avg = np.average(frame_deltas) 135 frame_delta_var = np.var(frame_deltas)
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/cts/apps/CameraITS/tests/scene9/ |
D | test_jpeg_quality.py | 24 import numpy as np namespace 89 jpeg = np.concatenate((jpeg[0:i], jpeg[length:]), axis=None) 154 luma = np.array(jpeg[luma_start: luma_start + dqt_size]) 155 chroma = np.array(jpeg[chroma_start: chroma_start + dqt_size]) 156 lumas.append(np.mean(luma)) 157 chromas.append(np.mean(chroma)) 168 matrix = np.array(jpeg[start:start + dqt_size]) 170 chromas.append(np.mean(matrix)) 175 lumas.append(np.mean(matrix)) 266 lumas = np.array(lumas) [all …]
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/cts/apps/CameraITS/tests/scene1_1/ |
D | test_auto_vs_manual.py | 21 import numpy as np namespace 127 if not np.allclose(awb_xform, x, atol=_AWB_MANUAL_ATOL, rtol=0): 131 if not np.allclose(awb_gains, g, atol=_AWB_MANUAL_ATOL, rtol=0): 137 if not np.allclose(awb_xform_a, awb_xform, atol=_AWB_AUTO_ATOL, 141 if not np.allclose(awb_gains_a, awb_gains, atol=_AWB_AUTO_ATOL,
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D | test_linearity.py | 23 import numpy as np namespace 43 _GAMMA_LUT = np.array( 45 _INV_GAMMA_LUT = np.array( 130 line, residuals, _, _, _ = np.polyfit(
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/cts/apps/CameraITS/tests/scene_flash/ |
D | test_preview_min_frame_rate.py | 22 import numpy as np namespace 134 frame_deltas = np.array(video_processing_utils.get_frame_deltas( 136 frame_delta_avg = np.average(frame_deltas) 137 frame_delta_var = np.var(frame_deltas) 154 y_avg = np.average(
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/cts/apps/CameraITS/tests/sensor_fusion/ |
D | test_preview_distortion.py | 21 import numpy as np namespace 75 image_diagonal = np.sqrt(image.shape[0]**2 + image.shape[1]**2) 139 ideal_points = np.zeros((pattern_size[0] * pattern_size[1], 3), np.float32) 141 np.mgrid[0:pattern_size[0], 0:pattern_size[1]].T.reshape(-1, 2) 187 rms_error = math.sqrt(np.mean(np.square(distortion_errors))) 266 corners = np.concatenate(corners, axis=0).reshape(-1, 4, 2) 281 arranged_corners = np.array([marker_dict[i] for i in range(len(corners))]) 284 corners = np.expand_dims(arranged_corners, axis=1) 293 ideal_points = np.delete(ideal_points, middle_index, axis=0)
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D | test_sensor_fusion.py | 28 import numpy as np namespace 202 starts = np.array([start for start, exptime, readout in cam_events]) 203 max_frame_delta_ms = (np.amax(np.subtract(starts[1:], starts[0:-1])) / 210 exptimes = np.array([exptime for start, exptime, readout in cam_events]) 211 if not np.all(exptimes == exptimes[0]): 213 readouts = np.array([readout for start, exptime, readout in cam_events]) 214 if not np.all(readouts == readouts[0]): 231 xfit = np.arange(x[0], x[-1], 0.05).tolist() 286 frames.append(np.array(img).reshape((h, w, 3)) / 255)
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/cts/apps/CameraITS/tests/inprog/rolling_shutter_skew/ |
D | test_rolling_shutter_skew.py | 20 import numpy as np namespace 319 np_cluster = np.array([[c.x, c.y] for c in largest_cluster]) 369 img = img.astype(np.uint8) 382 kernel = np.ones((3, 3), np.uint8) 416 self.x = int(np.mean(contour[:, 0, 0])) 417 self.y = int(np.mean(contour[:, 0, 1])) 419 x_r = (np.max(contour[:, 0, 0]) - np.min(contour[:, 0, 0])) / 2.0 420 y_r = (np.max(contour[:, 0, 1]) - np.min(contour[:, 0, 1])) / 2.0 570 points = np.array([[x, y], [x + w, y], [x + w, y + h], [x, y + h]], 571 np.int32)
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/cts/apps/CameraITS/tests/scene1_2/ |
D | test_yuv_plus_dng.py | 21 import numpy as np namespace 32 _UNITY_MATRIX = np.array([1, 0, 0, 91 if not np.allclose(calibration_xform, _UNITY_MATRIX,
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/cts/apps/CameraITS/tests/scene6/ |
D | test_zoom.py | 26 import numpy as np namespace 60 z_list = np.arange(z_min, z_max, (z_max - z_min) / (_NUM_STEPS - 1)) 61 z_list = np.append(z_list, z_max) 63 z_list = np.insert(z_list, 0, 1) # make first (reference) zoom 1x
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/cts/apps/CameraITS/tests/scene_extensions/scene_hdr/ |
D | test_hdr_extension.py | 23 import numpy as np namespace 73 tile = tile.astype(np.uint8) 110 sobel = np.float32(np.hypot(sobel_x, sobel_y)) 114 opencv_processing_utils.find_all_contours(np.uint8(sobel)), key=len) 142 for a in np.arange(*_CONTRAST_ARANGE):
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