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Obj_Detector = obj.Object_Detector(obj_detection_graph)
Tracker = obj.Tracker()
test_vid_path = "chase1Person1View3Point0.mp4"
print('Testing on %s' % test_vid_path)
reader = imageio.get_reader(test_vid_path, 'ffmpeg')
fps = reader.get_meta_data()['fps'] // 2
# out_vid_path = "chase1Person1View3Point0_out.mp4"
# writer = imageio.get_writer(out_vid_path, fps=fps)
# print("Writing output video on %s" %out_vid_path)
actors = [0,1,2]
writers = []
for ii in actors:
writer = imageio.get_writer("person_%i.mp4" % ii, fps=fps)
print("Video writer set for person_%i" % ii)
writers.append(writer)
frame_cnt = 0
for test_img in reader:
frame_cnt += 1
if frame_cnt % 2 == 0:
continue
print("frame_cnt: %i" %frame_cnt)
expanded_img = np.expand_dims(test_img, axis=0)
detection_list = Obj_Detector.detect_objects_in_np(expanded_img)
detection_info = [info[0] for info in detection_list]
Tracker.update_tracker(detection_info, test_img)
if frame_cnt > 30:
print("writing segments")
for actor_no, writer in zip(actors,writers):
#Written by Timothy Seabrook
#timothy.seabrook@cs.ox.ac.uk
import imageio
import glob
filenames = glob.glob('/Users/seabrook/Documents/FDL/Kernel/' + '*.png')
with imageio.get_writer('/Users/seabrook/Documents/FDL/Kernel/kernel.gif', mode='I', duration=0.04) as writer:
for i in range(len(filenames) // 4):
image = imageio.imread(filenames[i])
writer.append_data(image)
tile_sz=size,
wsize=wsize)
pred = (out_img * 255).cpu().numpy().astype(np.uint8)
preds.append(pred)
#imsave(folder/f'{t}.tif', pred[0])
orig = (img[wsize // 2][None] * 255).astype(np.uint8)
origs.append(orig)
if len(preds) > 0:
all_y = img_to_uint8(np.concatenate(preds))
imageio.mimwrite(
pred_out, all_y,
bigtiff=True) #, fps=30, macro_block_size=None) # for mp4
all_y = img_to_uint8(np.concatenate(origs))
imageio.mimwrite(orig_out, all_y,
bigtiff=True) #, fps=30, macro_block_size=None)
def get_img(self, path, norm_size=True, norm_exposure=False):
"""
Prepare an image for image processing tasks
param path: the input image path
type x: str
return: the image
rtype: numpy.ndarray
"""
# flatten returns a 2d grayscale array
img = imageio.imread(path, as_gray=True).astype(int)
# resizing returns float vals 0:255; convert to ints for downstream tasks
if norm_size:
img = skimage.transform.resize(
img, (self.height, self.width), mode="constant", preserve_range=True
)
if norm_exposure:
img = self.normalize_exposure(img)
return img
def test_png():
for isfloat in (False, True):
for crop in (0, 1, 2):
for colors in (0, 1, 3, 4):
fname = fnamebase + "%i.%i.%i.png" % (isfloat, crop, colors)
rim = get_ref_im(colors, crop, isfloat)
imageio.imsave(fname, rim)
im = imageio.imread(fname)
mul = 255 if isfloat else 1
assert_close(rim * mul, im, 0.1) # lossless
# Parameters
im = imageio.imread("imageio:chelsea.png", ignoregamma=True)
imageio.imsave(fnamebase + ".png", im, interlaced=True)
# Parameter fail
raises(TypeError, imageio.imread, "imageio:chelsea.png", notavalidk=True)
raises(TypeError, imageio.imsave, fnamebase + ".png", im, notavalidk=True)
# Compression
imageio.imsave(fnamebase + "1.png", im, compression=0)
imageio.imsave(fnamebase + "2.png", im, compression=9)
s1 = os.stat(fnamebase + "1.png").st_size
s2 = os.stat(fnamebase + "2.png").st_size
assert s2 < s1
# Fail
raises(ValueError, imageio.imsave, fnamebase + ".png", im, compression=12)
# Quantize
def test_random_access():
im1 = imageio.imread("imageio:chelsea.png")
ims1 = [im1, im1 * 0.8, im1 * 0.5]
fname = os.path.join(test_dir, "chelseam.bsdf")
imageio.mimsave(fname, ims1)
r = imageio.get_reader(fname)
for i in (1, 0, 2, 0, 1, 2):
assert np.all(ims1[i] == r.get_data(i))
# Note that if we would not get the data of one image in the series,
im3 = imageio.imread(filename3)
assert im1.ndim == 3
assert im1.shape == im3.shape
assert (im1 == im3).all()
# Ensure imread + imwrite works round trip - volume like
filename3 = os.path.join(test_dir, 'test_tiff2.tiff')
im1 = imageio.volread(filename1)
imageio.volwrite(filename3, im1)
im3 = imageio.volread(filename3)
assert im1.ndim == 4
assert im1.shape == im3.shape
assert (im1 == im3).all()
# Read metadata
md = imageio.get_reader(filename2).get_meta_data()
assert md['is_imagej'] is None
assert md['description'] == 'shape=(2,3,10,10)'
assert md['description1'] == ''
assert md['datetime'] == datetime.datetime(2015, 5, 9, 9, 8, 29)
assert md['software'] == 'tifffile.py'
# Write metadata
dt = datetime.datetime(2018, 8, 6, 15, 35, 5)
w = imageio.get_writer(filename1, software='testsoftware')
w.append_data(np.zeros((10, 10)), meta={'description': 'test desc',
'datetime': dt})
w.close()
r = imageio.get_reader(filename1)
md = r.get_meta_data()
assert 'datetime' in md
assert md['datetime'] == dt
def test_request_file_no_seek():
class File:
def read(self, n):
return b"\x00" * n
def seek(self, i):
raise IOError("Not supported")
def tell(self):
raise Exception("Not supported")
def close(self):
pass
R = Request(File(), "ri")
with raises(IOError):
R.firstbytes
def test_select():
fname1 = get_remote_file("images/cockatoo.mp4", test_dir)
F = imageio.formats["avbin"]
assert F.name == "AVBIN"
assert F.can_read(core.Request(fname1, "rI"))
assert not F.can_write(core.Request(fname1, "wI"))
assert not F.can_read(core.Request(fname1, "ri"))
assert not F.can_read(core.Request(fname1, "rv"))
# Test can read, cannot write
assert format.can_read(Request(lfp_file, "ri"))
# Test cannot read, cannot write
assert not format.can_read(Request(lfp_file, "rv"))
assert not format.can_read(Request(lfp_file, "rI"))
assert not format.can_read(Request(lfp_file, "rV"))
assert not format.can_read(Request(lfr_file, "ri"))
assert not format.can_read(Request(raw_f01_file, "ri"))
assert not format.can_read(Request(raw_illum_file, "ri"))
assert not format.can_read(Request(png_file, "ri"))
assert not format.can_write(Request(lfp_file, "wi"))
assert not format.can_write(Request(lfr_file, "wi"))
assert not format.can_write(Request(raw_f01_file, "wi"))
assert not format.can_write(Request(raw_illum_file, "wi"))
assert not format.can_write(Request(png_file, "wi"))