1 # Copyright 2013 The Android Open Source Project 2 # 3 # Licensed under the Apache License, Version 2.0 (the "License"); 4 # you may not use this file except in compliance with the License. 5 # You may obtain a copy of the License at 6 # 7 # http://www.apache.org/licenses/LICENSE-2.0 8 # 9 # Unless required by applicable law or agreed to in writing, software 10 # distributed under the License is distributed on an "AS IS" BASIS, 11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 # See the License for the specific language governing permissions and 13 # limitations under the License. 14 15 import its.image 16 import its.caps 17 import its.device 18 import its.objects 19 import its.target 20 import pylab 21 import numpy 22 import os.path 23 import matplotlib 24 import matplotlib.pyplot 25 26 def main(): 27 """Test that a constant exposure is seen as ISO and exposure time vary. 28 29 Take a series of shots that have ISO and exposure time chosen to balance 30 each other; result should be the same brightness, but over the sequence 31 the images should get noisier. 32 """ 33 NAME = os.path.basename(__file__).split(".")[0] 34 35 THRESHOLD_MAX_OUTLIER_DIFF = 0.1 36 THRESHOLD_MIN_LEVEL = 0.1 37 THRESHOLD_MAX_LEVEL = 0.9 38 THRESHOLD_MAX_LEVEL_DIFF = 0.045 39 THRESHOLD_MAX_LEVEL_DIFF_WIDE_RANGE = 0.06 40 THRESHOLD_ROUND_DOWN_GAIN = 0.1 41 THRESHOLD_ROUND_DOWN_EXP = 0.05 42 43 mults = [] 44 r_means = [] 45 g_means = [] 46 b_means = [] 47 threshold_max_level_diff = THRESHOLD_MAX_LEVEL_DIFF 48 49 with its.device.ItsSession() as cam: 50 props = cam.get_camera_properties() 51 its.caps.skip_unless(its.caps.compute_target_exposure(props) and 52 its.caps.per_frame_control(props)) 53 54 e,s = its.target.get_target_exposure_combos(cam)["minSensitivity"] 55 s_e_product = s*e 56 expt_range = props['android.sensor.info.exposureTimeRange'] 57 sens_range = props['android.sensor.info.sensitivityRange'] 58 59 m = 1.0 60 while s*m < sens_range[1] and e/m > expt_range[0]: 61 mults.append(m) 62 s_test = round(s*m) 63 e_test = s_e_product / s_test 64 print "Testing s:", s_test, "e:", e_test 65 req = its.objects.manual_capture_request( 66 s_test, e_test, 0.0, True, props) 67 cap = cam.do_capture(req) 68 s_res = cap["metadata"]["android.sensor.sensitivity"] 69 e_res = cap["metadata"]["android.sensor.exposureTime"] 70 assert(0 <= s_test - s_res < s_test * THRESHOLD_ROUND_DOWN_GAIN) 71 assert(0 <= e_test - e_res < e_test * THRESHOLD_ROUND_DOWN_EXP) 72 s_e_product_res = s_res * e_res 73 request_result_ratio = s_e_product / s_e_product_res 74 print "Capture result s:", s_test, "e:", e_test 75 img = its.image.convert_capture_to_rgb_image(cap) 76 its.image.write_image(img, "%s_mult=%3.2f.jpg" % (NAME, m)) 77 tile = its.image.get_image_patch(img, 0.45, 0.45, 0.1, 0.1) 78 rgb_means = its.image.compute_image_means(tile) 79 # Adjust for the difference between request and result 80 r_means.append(rgb_means[0] * request_result_ratio) 81 g_means.append(rgb_means[1] * request_result_ratio) 82 b_means.append(rgb_means[2] * request_result_ratio) 83 # Test 3 steps per 2x gain 84 m = m * pow(2, 1.0 / 3) 85 86 # Allow more threshold for devices with wider exposure range 87 if m >= 64.0: 88 threshold_max_level_diff = THRESHOLD_MAX_LEVEL_DIFF_WIDE_RANGE 89 90 # Draw a plot. 91 pylab.plot(mults, r_means, 'r.-') 92 pylab.plot(mults, g_means, 'g.-') 93 pylab.plot(mults, b_means, 'b.-') 94 pylab.ylim([0,1]) 95 matplotlib.pyplot.savefig("%s_plot_means.png" % (NAME)) 96 97 # Check for linearity. Verify sample pixel mean values are close to each 98 # other. Also ensure that the images aren't clamped to 0 or 1 99 # (which would make them look like flat lines). 100 for chan in xrange(3): 101 values = [r_means, g_means, b_means][chan] 102 m, b = numpy.polyfit(mults, values, 1).tolist() 103 max_val = max(values) 104 min_val = min(values) 105 max_diff = max_val - min_val 106 print "Channel %d line fit (y = mx+b): m = %f, b = %f" % (chan, m, b) 107 print "Channel max %f min %f diff %f" % (max_val, min_val, max_diff) 108 assert(max_diff < threshold_max_level_diff) 109 assert(b > THRESHOLD_MIN_LEVEL and b < THRESHOLD_MAX_LEVEL) 110 for v in values: 111 assert(v > THRESHOLD_MIN_LEVEL and v < THRESHOLD_MAX_LEVEL) 112 assert(abs(v - b) < THRESHOLD_MAX_OUTLIER_DIFF) 113 114 if __name__ == '__main__': 115 main() 116
