def _read_annotations(self):
result = OrderedDict()
for annotation in read_lines(self.csv_data_file):
rows = annotation.split(' ')
img_file = rows[0]
if img_file not in result:
result[img_file] = []
for box in rows[1:]:
if len(box) < 5:
continue
x1, y1, x2, y2, class_name = box.split(',')
x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
class_name = self.class_names[int(class_name)]
result[img_file].append({'x1': x1, 'x2': x2, 'y1': y1, 'y2': y2, 'class': class_name})
print('read annotations done')
return result
def __init__(self, csv_data_file, csv_class_file, base_dir=None, **kwargs):
""" Initialize a CSV data generator.
Args
csv_data_file: Path to the CSV annotations file.
csv_class_file: Path to the CSV classes file.
base_dir: Directory w.r.t. where the files are to be searched (defaults to the directory containing the csv_data_file).
"""
self.class_names = []
self.image_names = []
self.image_data = {}
self.base_dir = base_dir
self.csv_data_file = csv_data_file
self.csv_class_file = csv_class_file
# Take base_dir from annotations file if not explicitly specified.
if self.base_dir is None:
self.base_dir = os.path.dirname(csv_data_file)
# parse the provided class file
try:
self.class_names = read_lines(csv_class_file)
self.classes = {}
for i, name in enumerate(self.class_names):
self.classes[name] = i
except ValueError as e:
raise_from(ValueError('invalid CSV class file: {}: {}'.format(csv_class_file, e)), None)
self.labels = {}
for key, value in self.classes.items():
self.labels[value] = key
# csv with img_path, x1, y1, x2, y2, class_name
try:
self.image_data = self._read_annotations()
except ValueError as e:
raise_from(ValueError('invalid CSV annotations file: {}: {}'.format(csv_data_file, e)), None)
self.image_names = list(self.image_data.keys())
super(TextGenerator, self).__init__(**kwargs)
import numpy as np
import tensorflow as tf
from notekeras.model.yolo3 import Dataset, YoloDataset
from notekeras.model.yolo3 import YoloBody
from notekeras.utils import read_lines
root = '/Users/liangtaoniu/workspace/MyDiary/notechats/notekeras/example/yolo'
classes = read_lines(root + "/data/classes/coco.names")
annotation_path = root + "/data/dataset/yymnist_train.txt"
tf.config.experimental_run_functions_eagerly(True)
def get_anchors():
# anchor = "10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326"
anchor = '1.25,1.625, 2.0,3.75, 4.125,2.875, 1.875,3.8125, 3.875,2.8125, 3.6875,7.4375, 3.625,2.8125, 4.875,6.1875, 11.65625,10.1875'
anchor = [float(x) for x in anchor.split(',')]
return np.array(anchor).reshape(-1, 2)
anchors = get_anchors()
yolo_body = YoloBody(anchors=anchors, num_classes=len(classes))
yolo_body.debug()
yolo_body.load_weights(
"/Users/liangtaoniu/workspace/MyDiary/tmp/models/yolo/configs/yolov3.h5",
freeze_body=3)
train_set2 = YoloDataset(annotation_path=annotation_path,
anchors=anchors,
classes=classes,
batch_size=4)
import hashlib
import numpy as np
from notekeras.model.yolo3 import YoloBody
from notekeras.utils import read_lines
from notemodel.database import set_weight_path
set_weight_path("/Users/liangtaoniu/workspace/MyDiary/src/tianchi/live/data/weights")
# import tensorflow as tf
# tf.config.experimental_run_functions_eagerly(True)
classes = read_lines("coco.names")
def get_md5(weight):
m = hashlib.md5()
m.update(weight)
return m.hexdigest()
def get_anchors():
anchors = "10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326"
# anchors = '1.25,1.625, 2.0,3.75, 4.125,2.875, 1.875,3.8125, 3.875,2.8125, 3.6875,7.4375, 3.625,2.8125, 4.875,6.1875, 11.65625,10.1875'
anchors = [float(x) for x in anchors.split(',')]
return np.array(anchors).reshape(-1, 2)
anchors = get_anchors()
yolo_body1 = YoloBody(anchors=anchors, num_classes=len(classes))