def main(image_files, output_folder, exposure_times, resize=False):
""" Generate an HDR from the images in the source folder """
image_files = sorted(image_files)
# Print the information associated with each image -- use this
# to verify that the correct exposure time is associated with each
# image, or else you will get very poor results
print "{:^30} {:>15}".format("Filename", "Exposure Time")
print "\n".join([
"{:>30} {:^15.4f}".format(*v) for v in zip(image_files, exposure_times)
])
img_stack = [
cv2.imread(name) for name in image_files
if path.splitext(name)[-1][1:].lower() in EXTENSIONS
]
if not all(img_stack):
raise RuntimeError("One or more input files failed to load.")
# Subsampling the images can reduce runtime for large files
if resize:
img_stack = [img[::4, ::4] for img in img_stack]
log_exposure_times = np.log(exposure_times)
hdr_image = a9.computeHDR(img_stack, log_exposure_times)
cv2.imwrite(path.join(output_folder, "output.png"), hdr_image)
print "Done!"
print "getYXLocations testing passed."
return True
if __name__ == "__main__":
print "Performing unit test."
if not test_normalizeImage():
print "normalizeImage function failed. Halting testing."
sys.exit()
if not test_linearWeight():
print "linearWeight function failed. Halting testing."
sys.exit()
if not test_getYXLocations():
print "getYXLocations function failed. Halting testing."
sys.exit()
print "Unit tests passed."
print "We do not run a unit test for computeResponseCurve so make sure " + \
"you look at the HDR output (or see why the function isn't " + \
"running) to assess your progress."
image_dir = "input"
output_dir = "output"
exposure_times = np.float64(
[1 / 160.0, 1 / 125.0, 1 / 80.0, 1 / 60.0, 1 / 40.0, 1 / 15.0])
log_exposure_times = np.log(exposure_times)
np.random.seed()
hdr = assignment9.computeHDR(image_dir, log_exposure_times)
cv2.imwrite(output_dir + "/hdr.jpg", hdr)
if not test_linearWeight():
print "linearWeight function failed. Halting testing."
sys.exit()
if not test_sampleIntensities():
print "sampleIntensities function. Halting testing."
sys.exit()
if not test_computeResponseCurve():
print "computeResponseCurve function failed. Halting testing."
sys.exit()
if not test_computeRadianceMap():
print "ccomputeRadianceMap function failed. Halting testing."
sys.exit()
print "Unit tests passed."
print "Generating HDR from sample images. (This may take a while.)"
image_dir = "input"
output_dir = "output"
exposure_times = np.float64(
[1 / 160.0, 1 / 125.0, 1 / 80.0, 1 / 60.0, 1 / 40.0, 1 / 15.0])
log_exposure_times = np.log(exposure_times)
np.random.seed()
small_images = assignment9.readImages(image_dir)
hdr = assignment9.computeHDR(small_images, log_exposure_times)
print u"Expected avg pixel value for the test images is 125\u00B1" + u"1.",
print "Your value was {}.".format(int(np.average(hdr)))
if not (124 <= int(np.average(hdr)) <= 126):
print "Please check your code for errors and inspect your output."
cv2.imwrite(output_dir + "/hdr.jpg", hdr)
print "We do not run a unit test for computeResponseCurve so make sure " + \
"you look at the HDR output (or see why the function isn't " + \
"running) to assess your progress."
parser = argparse.ArgumentParser(description='Create an HDR image from an input directory of images.')
parser.add_argument('--input', help='directory where the input images are stored', action='store', dest="input")
parser.add_argument('--output', help='path to the output file', action='store', dest="output")
args = parser.parse_args()
#exposure_times = np.float64(exposures(args.input))
exposure_times = np.float64([1/2.0, 2.0, 8.0])
log_exposure_times = np.log(exposure_times)
np.random.seed()
hdr = assignment9.computeHDR(args.input, log_exposure_times)
cv2.imwrite(args.output, hdr)
#hdr = cv2.cvtColor(hdr, cv2.COLOR_BGR2RGB)
#h, w = hdr.shape[:2]
#app = wx.App()
#frame = MyFrame(title="HDR Image")
#panel = wx.Panel(frame)
#image = wx.ImageFromBuffer(w, h, hdr)
#bitmap = wx.StaticBitmap(panel, -1, wx.BitmapFromImage(image))
#frame.SetClientSize(bitmap.GetSize())
#frame.Show()
#app.MainLoop()
np.testing.assert_array_equal(y_locs, y_ans)
np.testing.assert_array_equal(x_locs, x_ans)
print "getYXLocations testing passed."
return True
if __name__ == "__main__":
print "Performing unit test."
if not test_normalizeImage():
print "normalizeImage function failed. Halting testing."
sys.exit()
if not test_linearWeight():
print "linearWeight function failed. Halting testing."
sys.exit()
if not test_getYXLocations():
print "getYXLocations function failed. Halting testing."
sys.exit()
print "Unit tests passed."
print "We do not run a unit test for computeResponseCurve so make sure " + "you look at the HDR output (or see why the function isn't " + "running) to assess your progress."
image_dir = "input"
output_dir = "output"
exposure_times = np.float64([1 / 160.0, 1 / 125.0, 1 / 80.0, 1 / 60.0, 1 / 40.0, 1 / 15.0])
log_exposure_times = np.log(exposure_times)
np.random.seed()
hdr = assignment9.computeHDR(image_dir, log_exposure_times)
cv2.imwrite(output_dir + "/hdr.jpg", hdr)
