| # Copyright 2014 The Android Open Source Project |
| # |
| # Licensed under the Apache License, Version 2.0 (the "License"); |
| # you may not use this file except in compliance with the License. |
| # You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| |
| import os.path |
| import its.caps |
| import its.device |
| import its.image |
| import its.objects |
| import its.target |
| |
| from matplotlib import pylab |
| import matplotlib.pyplot |
| import numpy |
| |
| BURST_LEN = 50 |
| BURSTS = 5 |
| COLORS = ["R", "G", "B"] |
| FRAMES = BURST_LEN * BURSTS |
| NAME = os.path.basename(__file__).split(".")[0] |
| SPREAD_THRESH = 0.03 |
| |
| |
| def main(): |
| """Take long bursts of images and check that they're all identical. |
| |
| Assumes a static scene. Can be used to idenfity if there are sporadic |
| frames that are processed differently or have artifacts. Uses manual |
| capture settings. |
| """ |
| |
| with its.device.ItsSession() as cam: |
| |
| # Capture at the smallest resolution. |
| props = cam.get_camera_properties() |
| its.caps.skip_unless(its.caps.compute_target_exposure(props) and |
| its.caps.per_frame_control(props)) |
| debug = its.caps.debug_mode() |
| |
| _, fmt = its.objects.get_fastest_manual_capture_settings(props) |
| e, s = its.target.get_target_exposure_combos(cam)["minSensitivity"] |
| req = its.objects.manual_capture_request(s, e) |
| w, h = fmt["width"], fmt["height"] |
| |
| # Capture bursts of YUV shots. |
| # Get the mean values of a center patch for each. |
| # Also build a 4D array, which is an array of all RGB images. |
| r_means = [] |
| g_means = [] |
| b_means = [] |
| imgs = numpy.empty([FRAMES, h, w, 3]) |
| for j in range(BURSTS): |
| caps = cam.do_capture([req]*BURST_LEN, [fmt]) |
| for i, cap in enumerate(caps): |
| n = j*BURST_LEN + i |
| imgs[n] = its.image.convert_capture_to_rgb_image(cap) |
| tile = its.image.get_image_patch(imgs[n], 0.45, 0.45, 0.1, 0.1) |
| means = its.image.compute_image_means(tile) |
| r_means.append(means[0]) |
| g_means.append(means[1]) |
| b_means.append(means[2]) |
| |
| # Dump all images if debug |
| if debug: |
| print "Dumping images" |
| for i in range(FRAMES): |
| its.image.write_image(imgs[i], "%s_frame%03d.jpg"%(NAME, i)) |
| |
| # The mean image. |
| img_mean = imgs.mean(0) |
| its.image.write_image(img_mean, "%s_mean.jpg"%(NAME)) |
| |
| # Plot means vs frames |
| frames = range(FRAMES) |
| pylab.title(NAME) |
| pylab.plot(frames, r_means, "-ro") |
| pylab.plot(frames, g_means, "-go") |
| pylab.plot(frames, b_means, "-bo") |
| pylab.ylim([0, 1]) |
| pylab.xlabel("frame number") |
| pylab.ylabel("RGB avg [0, 1]") |
| matplotlib.pyplot.savefig("%s_plot_means.png" % (NAME)) |
| |
| # PASS/FAIL based on center patch similarity. |
| for plane, means in enumerate([r_means, g_means, b_means]): |
| spread = max(means) - min(means) |
| msg = "%s spread: %.5f, SPREAD_THRESH: %.3f" % ( |
| COLORS[plane], spread, SPREAD_THRESH) |
| print msg |
| assert spread < SPREAD_THRESH, msg |
| |
| if __name__ == "__main__": |
| main() |
| |