def save_vid_worker(img_files, view_i, save_dir, tag, img_size, fps):
# port_data soted in view order
create_dir_if_not_exists(save_dir)
output_vid_name = "{}_view{}.mov".format(tag, view_i)
frame_count = len(img_files)
out_shape_cv = img_size[:2]
# out_shape_cv = np_shape_to_cv(img_size[:2])
output_vid_name = os.path.join(save_dir, output_vid_name)
with vid_writer(output_vid_name, fps, out_shape_cv, frame_count) as vid:
for im_f in img_files:
img = cv2.imread(im_f)
vid.write(img)
def save_img_worker(ports, save_dir, tag, port_data_queue,
result_file_name_que):
# port_data soted in view order
create_dir_if_not_exists(save_dir)
while True:
port_data = port_data_queue.get()
files_names = {}
for view_i, p in enumerate(ports):
f_cnt = port_data[p]["num"]
frame = port_data[p]["frame"]
filenamne = "frame{}_{}_view{}.png".format(f_cnt, tag, view_i)
filenamne = os.path.join(save_dir, filenamne)
files_names[p] = filenamne
cv2.imwrite(filenamne, frame)
result_file_name_que.put(files_names)
def set_log_file(log_file, mpi_only_log_rank=None):
"""
mpi_only_rank(int or NOne), mpi rank only to log to the file
"""
global _file_handler
# create file handler which logs even debug messages
# always overte file unless multi mpi log
mode = "a" if mpi_only_log_rank is None else "w"
mpi_info = "only mpi rank {}".format(
mpi_only_log_rank) if mpi_only_log_rank is not None and rank is not None else ""
if rank is None or mpi_only_log_rank is None or rank == mpi_only_log_rank:
if _file_handler is not None:
log.removeHandler(_file_handler)
create_dir_if_not_exists(log_file)
_file_handler = logging.FileHandler(log_file, mode=mode)
formatter = logging.Formatter(_format, datefmt=datefmt)
_file_handler.setFormatter(formatter)
log.addHandler(_file_handler)
start_dt = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
log.info("Start logging {}, saved to file: {} {}".format(start_dt, log_file, mpi_info))
def plot_embedding(X,
labels_str,
title,
imgs=None,
save_dir=None,
frame_lable=None,
max_frame=None,
vid_lable=None):
# http://scikit-learn.org/stable/auto_examples/manifold/plot_lle_digits.html
x_min, x_max = np.min(X, 0), np.max(X, 0)
X = (X - x_min) / (x_max - x_min)
if imgs is not None:
fig = plt.figure(figsize=(20, 20))
ax = plt.subplot(221)
else:
fig = plt.figure()
ax = fig.gca()
# labels blow plt
n_classes, y, colors, legend_elements = plt_labeled_data(ax, X, labels_str)
plt.title(title)
if imgs is not None:
# plt again but with image overlay
ax = plt.subplot(222)
ax.set_title("image overlay")
ax.scatter(X[:, 0], X[:, 1], color=colors)
if hasattr(offsetbox, "AnnotationBbox"):
# only print thumbnails with matplotlib > 1.0
shown_images = np.array([[1.0, 1.0]]) # just something big
for i in range(X.shape[0]):
dist = np.sum((X[i] - shown_images)**2, 1)
if np.min(dist) < 5e-3:
# don't show points that are too close
continue
shown_images = np.r_[shown_images, [X[i]]]
imagebox = offsetbox.AnnotationBbox(offsetbox.OffsetImage(
imgs[i], cmap=plt.cm.gray_r, zoom=0.75),
X[i],
pad=0.0)
ax.add_artist(imagebox)
# plt legend same as befor
plt_labels_blow(ax, list(legend_elements.values()))
if frame_lable is not None:
# plt the frames classe
# show color for ever 50 frame in legend
ax = plt.subplot(223)
plt_labeled_data(
ax,
X,
frame_lable,
label_filter_legend=lambda l: l % 50 == 0,
plt_cm=plt.cm.Spectral,
index_color_factor=max_frame,
)
ax.set_title("frames as label (color range normalized for every vid)")
if vid_lable is not None:
# plt the view pair as classe
ax = plt.subplot(224)
plt_labeled_data(ax, X, vid_lable, label_filter_legend=lambda x: False)
ax.set_title("view pair as label")
if save_dir is not None:
create_dir_if_not_exists(save_dir)
save_dir = os.path.expanduser(save_dir)
title = os.path.join(save_dir, title)
fig.savefig(title + ".pdf", bbox_inches="tight")
log.info("save TSNE plt to: {}".format(title))
plt.close("all")