1 # Copyright 2014 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 os.path 16 import its.caps 17 import its.device 18 import its.image 19 import its.objects 20 import its.target 21 22 from matplotlib import pylab 23 import matplotlib.pyplot 24 import numpy 25 26 BURST_LEN = 50 27 BURSTS = 5 28 COLORS = ["R", "G", "B"] 29 FRAMES = BURST_LEN * BURSTS 30 NAME = os.path.basename(__file__).split(".")[0] 31 SPREAD_THRESH = 0.03 32 33 34 def main(): 35 """Take long bursts of images and check that they're all identical. 36 37 Assumes a static scene. Can be used to idenfity if there are sporadic 38 frames that are processed differently or have artifacts. Uses manual 39 capture settings. 40 """ 41 42 with its.device.ItsSession() as cam: 43 44 # Capture at the smallest resolution. 45 props = cam.get_camera_properties() 46 its.caps.skip_unless(its.caps.compute_target_exposure(props) and 47 its.caps.per_frame_control(props)) 48 debug = its.caps.debug_mode() 49 50 _, fmt = its.objects.get_fastest_manual_capture_settings(props) 51 e, s = its.target.get_target_exposure_combos(cam)["minSensitivity"] 52 req = its.objects.manual_capture_request(s, e) 53 w, h = fmt["width"], fmt["height"] 54 55 # Capture bursts of YUV shots. 56 # Get the mean values of a center patch for each. 57 # Also build a 4D array, which is an array of all RGB images. 58 r_means = [] 59 g_means = [] 60 b_means = [] 61 imgs = numpy.empty([FRAMES, h, w, 3]) 62 for j in range(BURSTS): 63 caps = cam.do_capture([req]*BURST_LEN, [fmt]) 64 for i, cap in enumerate(caps): 65 n = j*BURST_LEN + i 66 imgs[n] = its.image.convert_capture_to_rgb_image(cap) 67 tile = its.image.get_image_patch(imgs[n], 0.45, 0.45, 0.1, 0.1) 68 means = its.image.compute_image_means(tile) 69 r_means.append(means[0]) 70 g_means.append(means[1]) 71 b_means.append(means[2]) 72 73 # Dump all images if debug 74 if debug: 75 print "Dumping images" 76 for i in range(FRAMES): 77 its.image.write_image(imgs[i], "%s_frame%03d.jpg"%(NAME, i)) 78 79 # The mean image. 80 img_mean = imgs.mean(0) 81 its.image.write_image(img_mean, "%s_mean.jpg"%(NAME)) 82 83 # Plot means vs frames 84 frames = range(FRAMES) 85 pylab.title(NAME) 86 pylab.plot(frames, r_means, "-ro") 87 pylab.plot(frames, g_means, "-go") 88 pylab.plot(frames, b_means, "-bo") 89 pylab.ylim([0, 1]) 90 pylab.xlabel("frame number") 91 pylab.ylabel("RGB avg [0, 1]") 92 matplotlib.pyplot.savefig("%s_plot_means.png" % (NAME)) 93 94 # PASS/FAIL based on center patch similarity. 95 for plane, means in enumerate([r_means, g_means, b_means]): 96 spread = max(means) - min(means) 97 msg = "%s spread: %.5f, SPREAD_THRESH: %.3f" % ( 98 COLORS[plane], spread, SPREAD_THRESH) 99 print msg 100 assert spread < SPREAD_THRESH, msg 101 102 if __name__ == "__main__": 103 main() 104 105
