# 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 import numpy CROP_FULL_ERROR_THRESHOLD = 3 # pixels CROP_REGION_ERROR_THRESHOLD = 0.01 # reltol DIFF_THRESH = 0.05 # reltol NAME = os.path.basename(__file__).split(".")[0] def main(): """Test that raw streams are not croppable.""" with its.device.ItsSession() as cam: props = cam.get_camera_properties() its.caps.skip_unless(its.caps.compute_target_exposure(props) and its.caps.raw16(props) and its.caps.per_frame_control(props) and not its.caps.mono_camera(props)) # Calculate the active sensor region for a full (non-cropped) image. a = props["android.sensor.info.activeArraySize"] ax, ay = a["left"], a["top"] aw, ah = a["right"] - a["left"], a["bottom"] - a["top"] print "Active sensor region: (%d,%d %dx%d)" % (ax, ay, aw, ah) full_region = { "left": 0, "top": 0, "right": aw, "bottom": ah } # Calculate a center crop region. zoom = min(3.0, its.objects.get_max_digital_zoom(props)) assert(zoom >= 1) cropw = aw / zoom croph = ah / zoom crop_region = { "left": aw / 2 - cropw / 2, "top": ah / 2 - croph / 2, "right": aw / 2 + cropw / 2, "bottom": ah / 2 + croph / 2 } # Capture without a crop region. # Use a manual request with a linear tonemap so that the YUV and RAW # should look the same (once converted by the its.image module). e, s = its.target.get_target_exposure_combos(cam)["minSensitivity"] req = its.objects.manual_capture_request(s,e, 0.0, True, props) cap1_raw, cap1_yuv = cam.do_capture(req, cam.CAP_RAW_YUV) # Capture with a crop region. req["android.scaler.cropRegion"] = crop_region cap2_raw, cap2_yuv = cam.do_capture(req, cam.CAP_RAW_YUV) # Check the metadata related to crop regions. # When both YUV and RAW are requested, the crop region that's # applied to YUV should be reported. # Note that the crop region returned by the cropped captures doesn't # need to perfectly match the one that was requested. imgs = {} for s, cap, cr_expected, err_delta in [ ("yuv_full", cap1_yuv, full_region, CROP_FULL_ERROR_THRESHOLD), ("raw_full", cap1_raw, full_region, CROP_FULL_ERROR_THRESHOLD), ("yuv_crop", cap2_yuv, crop_region, CROP_REGION_ERROR_THRESHOLD), ("raw_crop", cap2_raw, crop_region, CROP_REGION_ERROR_THRESHOLD)]: # Convert the capture to RGB and dump to a file. img = its.image.convert_capture_to_rgb_image(cap, props=props) its.image.write_image(img, "%s_%s.jpg" % (NAME, s)) imgs[s] = img # Get the crop region that is reported in the capture result. cr_reported = cap["metadata"]["android.scaler.cropRegion"] x, y = cr_reported["left"], cr_reported["top"] w = cr_reported["right"] - cr_reported["left"] h = cr_reported["bottom"] - cr_reported["top"] print "Crop reported on %s: (%d,%d %dx%d)" % (s, x, y, w, h) # Test that the reported crop region is the same as the expected # one, for a non-cropped capture, and is close to the expected one, # for a cropped capture. ex = aw * err_delta ey = ah * err_delta assert ((abs(cr_expected["left"] - cr_reported["left"]) <= ex) and (abs(cr_expected["right"] - cr_reported["right"]) <= ex) and (abs(cr_expected["top"] - cr_reported["top"]) <= ey) and (abs(cr_expected["bottom"] - cr_reported["bottom"]) <= ey)) # Also check the image content; 3 of the 4 shots should match. # Note that all the shots are RGB below; the variable names correspond # to what was captured. # Shrink the YUV images 2x2 -> 1 to account for the size reduction that # the raw images went through in the RGB conversion. imgs2 = {} for s,img in imgs.iteritems(): h,w,ch = img.shape if s in ["yuv_full", "yuv_crop"]: img = img.reshape(h/2,2,w/2,2,3).mean(3).mean(1) img = img.reshape(h/2,w/2,3) imgs2[s] = img # Strip any border pixels from the raw shots (since the raw images may # be larger than the YUV images). Assume a symmetric padded border. xpad = (imgs2["raw_full"].shape[1] - imgs2["yuv_full"].shape[1]) / 2 ypad = (imgs2["raw_full"].shape[0] - imgs2["yuv_full"].shape[0]) / 2 wyuv = imgs2["yuv_full"].shape[1] hyuv = imgs2["yuv_full"].shape[0] imgs2["raw_full"]=imgs2["raw_full"][ypad:ypad+hyuv:,xpad:xpad+wyuv:,::] imgs2["raw_crop"]=imgs2["raw_crop"][ypad:ypad+hyuv:,xpad:xpad+wyuv:,::] print "Stripping padding before comparison:", xpad, ypad for s,img in imgs2.iteritems(): its.image.write_image(img, "%s_comp_%s.jpg" % (NAME, s)) # Compute diffs between images of the same type. # The raw_crop and raw_full shots should be identical (since the crop # doesn't apply to raw images), and the yuv_crop and yuv_full shots # should be different. diff_yuv = numpy.fabs((imgs2["yuv_full"] - imgs2["yuv_crop"])).mean() diff_raw = numpy.fabs((imgs2["raw_full"] - imgs2["raw_crop"])).mean() print "YUV diff (crop vs. non-crop):", diff_yuv print "RAW diff (crop vs. non-crop):", diff_raw assert(diff_yuv > DIFF_THRESH) assert(diff_raw < DIFF_THRESH) if __name__ == '__main__': main()