def load_given_tokens(tokens=None, randomize=True):
"""
@param tokens: None for loading all instances
@type tokens: list(str)
@return: (token, data_stream)
"""
def get_data_stream(token):
"""
@return: [(img, (4 coordinates))]
"""
def process_line(line):
if line.find(",") >= 0:
return map(int, line.split(","))
else:
return map(int, line.split("\t"))
img_dir = get_images_directory(token)
for idx, line in enumerate(open(token)):
img = cv2.imread(os.path.join(img_dir, "%04d.jpg" % (idx + 1)))
x, y, w, h = process_line(line.strip())
coords = [(x, y), (x + w, y), (x + w, y + h), (x, y + h)]
coords = [(float(x) / img.shape[1], float(y) / img.shape[0])
for x, y in coords]
yield (img, coords)
if tokens is None:
tokens = get_tokens()
if randomize:
random.shuffle(tokens)
for token in tokens:
yield (token, get_data_stream(token))
def load_given_tokens(tokens=None, randomize=True):
"""
@param tokens: None for loading all instances
@type tokens: list(str)
@return: (token, data_stream)
"""
def get_data_stream(token):
"""
@return: [(img, (4 coordinates))]
"""
img_dir = get_images_directory(token)
for idx, line in enumerate(open(token)):
img = cv2.imread(os.path.join(img_dir, "%08d.jpg" % (idx + 1)))
arr = map(int, map(float, line.strip().split(",")))
coords = zip(arr[::2], arr[1::2])
coords = coords[1:] + coords[:1]
coords = [(float(x) / img.shape[1], float(y) / img.shape[0]) for x, y in coords]
yield (img, coords)
if tokens is None:
tokens = get_tokens()
if randomize:
random.shuffle(tokens)
for token in tokens:
yield (token, get_data_stream(token))
def load_given_tokens(tokens=None, randomize=True):
"""
@param tokens: None for loading all instances
@type tokens: list(str)
@return: (token, data_stream)
"""
def get_data_stream(token):
"""
@return: [(img, (4 coordinates))]
"""
img_dir = get_images_directory(token)
for f_idx, coords in load_annotations(token):
img = cv2.imread(os.path.join(img_dir, "%08d.jpg" % f_idx))
coords = [(float(x) / img.shape[1], float(y) / img.shape[0])
for x, y in coords]
yield (img, coords)
if tokens is None:
tokens = get_tokens()
if randomize:
random.shuffle(tokens)
for token in tokens:
yield (token, get_data_stream(token))
if not word == " ":
st.append(offset)
cutw.append((word, POS.index(flag)))
offset += len(word)
cut_result[f_no] = [st, cutw]
return cut_result
#导入数据
data = ace_data.load()
docs = data["docs"]
nes = data["nes"]
res = data["res"]
#导入词袋
tokens = token.get_tokens()
#文本分词
seg_docs = get_seg(docs)
#获取各种的类型对应表list
el = sorted(list(set([x[1] for f in nes.values() for x in f.values()])))
esl = sorted(list(set([x[-1] for f in nes.values() for x in f.values()])))
rl = sorted(list(set([x[1] for f in res.values() for x in f.values()])))
rsl = sorted(list(set([x[2] for f in res.values() for x in f.values()])))
#提取特征过程
w = 2
features = {}
lables = {}
for f_no in res:
