def __init__(self, data_path=None, transform=None):
self.data_path = data_path
self.transform = transform
if data_path.endswith("pkl"):
data = pkl.load(open(data_path, "rb"))
self.img_paths = data["img_paths"]
self.labels = data["labels"]
else:
self.img_paths, _ = get_all_images(data_path)
self.labels = self.get_label()
self.num_classes = len(set(self.labels))
self.img_paths, self.labels = concur_shuffle(self.img_paths,
self.labels)
def __init__(self,
data_path=None,
transform=None,
batch_size=64,
p=0.6,
identity_size=3):
super(Dataset).__init__()
self.data_path = data_path
self.transform = transform
if data_path.endswith("pkl"):
data = pkl.load(open(data_path, "rb"))
img_paths = data["img_paths"]
labels = data["labels"]
else:
img_paths, _ = get_all_images(data_path)
labels = self.get_label(img_paths)
self.identity_label = IdentityLabel(img_paths, labels, batch_size, p,
identity_size)
self.num_classes = self.identity_label.num_classes
self.identity_label.concur_shuffle()
self.identity_label.get_label2img_paths()
self.identity_label.gen_batch_line()
def __init__(self, images_main_folder, json_file_path):
self.app_name = "GloryKeypointLabelingtool"
self.images_main_folder = images_main_folder
self.json_file_path = json_file_path
self.data = dataTree.data(json_file_path)
self.all_image_paths = utils.get_all_images(self.images_main_folder)
self.selected_image = None
self.selected_image_id = None
self.original_resolution = None
self.new_resolution = (1280, 720)
self.selected_original_image = None
self.screens = screens.screen()
self.current_index = 0
self.screen_name = ['help_screen']
self.selected_point = None
self.selected_button = None
self.selected_category = None
self.keypoint_visible = 2
self.added_keypoint_count = 0
self.selected_image_data = None
self.editable_point_radius = 3.0
if target_act == act_name:
person["act_id"] = act_id
person["act_name"] = act_name
def save_data(data, file_path):
with open(file_path, 'w') as outfile:
json.dump(data, outfile)
print("save {} success!".format(file_path))
def solve_one_image(image_path, act_name):
global url, act_cfg
json_path = image_path.replace(".jpg", ".json")
frame = cv2.imread(image_path)
data = post_image(url, frame)
add_act_id(data, act_name)
# print(json.dumps(data,indent=4))
save_data(data, json_path)
if __name__ == '__main__':
act_cfg = read_json("../config/action_space.json")
url = get_url_alphapose()
imgs = get_all_images("../imgs")
for img_path in imgs:
solve_one_image(img_path, "cross")
from functools import lru_cache
import random
from urllib.parse import urlparse
import itertools
import network
import utils
bugs = utils.get_bugs()
utils.prepare_images()
all_images = utils.get_all_images()[:3000] # 3000
image = utils.load_image(all_images[0])
input_shape = image.shape
BATCH_SIZE = 32
EPOCHS = 50
bugs_to_website = {}
for bug in bugs:
bugs_to_website[bug['id']] = urlparse(bug['url']).netloc
@lru_cache(maxsize=len(all_images))
def site_for_image(image):
bug = image[:image.index('_')]
return bugs_to_website[int(bug)]
def are_same_site(image1, image2):
return site_for_image(image1) == site_for_image(image2)
else:
img = sys.argv[1]
if len(sys.argv) == 3:
try:
THRESHOLD_SIMLARITY = float(sys.argv[2])
assert 0 < THRESHOLD_SIMLARITY < 1
except AssertionError:
print 'Similariy must be between 0 and 1.'
exit()
except ValueError:
path = sys.argv[2]
if len(sys.argv) == 4:
path = sys.argv[2]
THRESHOLD_SIMLARITY = sys.argv[3]
images = get_all_images(path)
similar_images = []
for image in images:
if calc_similar_by_path(img, image) > THRESHOLD_SIMLARITY:
similar_images.append(image)
if similar_images:
print 'The similar img(s) are below:'
for item in similar_images:
print item
print 'You can input "o" to open all the similar images, or imput any other key to quit'
command = getch()
if command == 'o':
for item in similar_images:
open(item)
else:
exit()