def show_all_images():
with open(json_path, 'r') as f:
data = json.loads(f.readline().strip())
details = Detail(ann_file, im_dir, pahse)
for im, val in data.items():
# input = raw_input('\nnext image? (y/n): ')
# if input in ['n', 'no']:
# while True:
# user_in = raw_input("Enter 'q' to quit: ")
# if user_in in ['q', 'quit']:
# return
while True:
input = raw_input(
'\nn (next image), c (close all figures), q (quit): ')
if input == 'c':
plt.close('all')
if input == 'n':
break
if input == 'q':
return
fig, ax = plt.subplots()
plt.ion()
plt.show()
im = str(im)
details.showImg(im, ax=ax, wait=True)
ax.set_title(im)
plot_points(val, fig, ax)
fig.canvas.mpl_connect("motion_notify_event", hover)
plt.draw()
plt.pause(0.001)
log_writer(fig, im)
def show_from_point_on(start_image, index_mode=False, params={}):
"""index_mode == True --> start_image is the index of the starting point by index
"""
json_path = params['json_path']
ann_file = params['ann_file']
im_dir = params['im_dir']
phase = params['phase']
log_table_path = params['log_table_path']
merge_level = params['merge_level']
with open(json_path, 'r') as f:
data = json.loads(f.readline().strip())
details = Detail(ann_file, im_dir, phase)
found_starting_point = False
for idx, (im, val) in enumerate(data.items()):
if not found_starting_point:
if index_mode:
if idx >= start_image:
found_starting_point = True
else:
continue
else:
if im == start_image:
found_starting_point = True
else:
continue
while True:
input = raw_input(
'\nn (next image), c (close all figures), q (quit): ')
if input == 'c':
plt.close('all')
if input == 'n':
break
if input == 'q':
return
fig, ax = plt.subplots()
plt.ion()
plt.show()
im = str(im)
details.showImg(im, ax=ax, wait=True)
ax.set_title(im)
plot_points(val, fig, ax, merge_level)
fig.canvas.mpl_connect("motion_notify_event", hover)
plt.draw()
plt.pause(0.001)
log_writer(fig, im, log_table_path)
def show_all_errors_for_class(cls, params):
json_path = params['json_path']
ann_file = params['ann_file']
im_dir = params['im_dir']
phase = params['phase']
log_table_path = params['log_table_path']
merge_level = params['merge_level']
print("showing errors for class {}".format(cls))
with open(json_path, 'r') as f:
data = json.loads(f.readline().strip())
details = Detail(ann_file, im_dir, phase)
for im, val in data.items():
# TODO: implement contains_error for json with just 0,1 predictions and annotations
# do it by using the pascal_gt.json for getting the ground truth class
# do same in visualize errors by class
if not contains_error(val, cls):
continue
while True:
input = raw_input(
'\nn (next image), c (close all figures), q (quit): ')
if input == 'c':
plt.close('all')
if input == 'n':
break
if input == 'q':
return
fig, ax = plt.subplots()
plt.ion()
plt.show()
im = str(im)
details.showImg(im, ax=ax, wait=True)
ax.set_title(im)
plot_points(val, fig, ax, merge_level)
fig.canvas.mpl_connect("motion_notify_event", hover)
plt.draw()
plt.pause(0.001)
log_writer(fig, im, log_table_path)
def show_1_image(im, params):
json_path = params['json_path']
ann_file = params['ann_file']
im_dir = params['im_dir']
phase = params['phase']
log_table_path = params['log_table_path']
merge_level = params['merge_level']
with open(json_path, 'r') as f:
data = json.loads(f.readline().strip())
details = Detail(ann_file, im_dir, phase)
while True:
try:
val = data[im]
fig, ax = plt.subplots()
plt.ion()
plt.show()
im = str(im)
details.showImg(im, ax=ax, wait=True)
ax.set_title(im)
plot_points(val, fig, ax, merge_level)
fig.canvas.mpl_connect("motion_notify_event", hover)
plt.draw()
plt.pause(0.001)
log_writer(fig, im, log_table_path)
except KeyError:
print("[-] Error: Invalid image name")
while True:
user_input = raw_input(
"n (another image), c (close all figures), q (quit): ")
if user_input == 'q':
return
if user_input == 'n':
im = raw_input("Enter image name: ")
break
if user_input == 'c':
plt.close('all')
def show_all_images_containing_given_class(cls, params):
json_path = params['json_path']
ann_file = params['ann_file']
im_dir = params['im_dir']
phase = params['phase']
log_table_path = params['log_table_path']
merge_level = params['merge_level']
print("showing images for class {}".format(cls))
with open(json_path, 'r') as f:
data = json.loads(f.readline().strip())
details = Detail(ann_file, im_dir, phase)
for im, val in data.items():
if not contains_class(val, cls):
continue
while True:
input = raw_input(
'\nn (next image), c (close all figures), q (quit): ')
if input == 'c':
plt.close('all')
if input == 'n':
break
if input == 'q':
return
fig, ax = plt.subplots()
plt.ion()
plt.show()
im = str(im)
details.showImg(im, ax=ax, wait=True)
ax.set_title(im)
plot_points(val, fig, ax, merge_level)
fig.canvas.mpl_connect("motion_notify_event", hover)
plt.draw()
plt.pause(0.001)
log_writer(fig, im, log_table_path)
# for cat in cats:
# print('\t{name} ({category_id}): \t{supercategory}'.format(**cat))
# print('%d images in the current phase (%s)' % (len(details.getImgs()), details.phase))
# imgs = details.getImgs(cats=['person', 'motorbike']);
# print('%d images contain both persons and motorbikes' % len(imgs))
_, axarr = plt.subplots(1,2)
# img = imgs[random.randrange(len(imgs))]
img = '2008_007573'
pt = [261, 119]
cat_idx = 8
details.showImg(img, ax=axarr[0], wait=True)
# cats = details.getCats(imgs=img)
# print("")
# print(['%s (%d) : %s' % (cat['name'], cat['category_id'], cat['supercategory']) for cat in cats])
print "****"
print "{} --> ".format(cat_idx) + CLASS_NAMES[cat_idx]
# mask
# mask = details.getMask(img, cat='person', instance='#0', show=False)
mask = details.getMask(img, show=False)
mycmap = get_rand_color_map()
mycmap.set_under(alpha=0.0)
nonzero = np.unique(mask[np.nonzero(mask)])
