def main(argv=None):
if FLAGS.tiny:
model = yolov3_tiny_3l.yolo_v3_tiny
elif FLAGS.dense:
model = dense_yolov3_v1.dense_yolo_v3
else:
model = yolo_v3.yolo_v3
classes = load_names(FLAGS.class_names)
# placeholder for detector inputs
inputs = tf.placeholder(tf.float32, [None, FLAGS.size, FLAGS.size, 3],
"inputs")
with tf.variable_scope('detector'):
detections = model(inputs, len(classes), data_format=FLAGS.data_format)
load_ops = load_weights(tf.global_variables(scope='detector'),
FLAGS.weights_file)
# Sets the output nodes in the current session
boxes = detections_boxes(detections)
with tf.Session() as sess:
sess.run(load_ops)
freeze_graph(sess, FLAGS.output_graph)
writer = tf.summary.FileWriter("logs/", sess.graph)
def predict(a, path_weights):
PATH = 'small_files/ASL'
names = utils.load_names(PATH)
# load yaml and create model
#yaml_file = open(path_weights+'/cnn_model.yaml', 'r')
#loaded_model_yaml = yaml_file.read()
#yaml_file.close()
#loaded_model = model_from_yaml(loaded_model_yaml)
# load weights into new model
#loaded_model.load_weights(path_weights+"/cnn_weights.h5")
#print("Loaded model from disk")
#a = a[:,:,0]
loaded_model = cnn_models.load_pre_tune_model(len(names), path_weights)
a = np.reshape(a, (1, a.shape[0], a.shape[1], 3))
y, names, X = utils.load_data("data/224/RGB/test")
#Convert class vectors to binary class matrices
y = keras_utils.to_categorical(np.ravel(y), len(names))
sgd = optimizers.SGD(lr=0.001, decay=1e-6, momentum=0.9, nesterov=True)
loaded_model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
X = np.reshape(X, (X.shape[0], X.shape[1], X.shape[2], 3))
#Normalize data
print "Normalize data.."
X = X.astype('float16')
X /= 255
#Evaluation of the model
print "Evaluating the model.."
scores = loaded_model.evaluate(X, y, verbose=1, batch_size=64)
print("Accuracy: %.2f%%" % (scores[1]*100))
#sgd = optimizers.SGD(lr=0.001, decay=1e-6, momentum=0.9, nesterov=True)
#loaded_model.compile(loss='sparse_categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
#print loaded_model.predict(a)
print loaded_model.predict_classes(a)
prediction = names[int(loaded_model.predict_classes(a))]
print prediction
return prediction
def main(argv=None):
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=FLAGS.gpu_memory_fraction)
config = tf.ConfigProto(
gpu_options=gpu_options,
log_device_placement=False,
# inter_op_parallelism_threads=0,
# intra_op_parallelism_threads=0,
# device_count={"CPU": 6}
)
cap = cv2.VideoCapture(FLAGS.video_path)
classes = utils.load_names(FLAGS.class_names)
frozenGraph = utils.load_graph(FLAGS.frozen_model)
boxes, inputs = utils.get_boxes_and_inputs_pb(frozenGraph)
with tf.Session(graph=frozenGraph, config=config) as sess:
while True:
ret, frame = cap.read()
if ret:
t1 = time.time()
frame1 = frame[:, :, ::-1] # from BGR to RGB
# frame1 = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
print('\'BGR2RGB\' time consumption:', time.time() - t1)
img_resized = utils.resize_cv2(
frame1, (FLAGS.size, FLAGS.size),
keep_aspect_ratio=FLAGS.keep_aspect_ratio)
img_resized = img_resized[np.newaxis, :]
t0 = time.time()
detected_boxes = sess.run(
boxes,
feed_dict={inputs: img_resized
}) # get the boxes whose confidence > 0.005
filtered_boxes = utils.non_max_suppression(
detected_boxes,
confidence_threshold=FLAGS.conf_threshold,
iou_threshold=FLAGS.iou_threshold)[
0] # boxes' filter by NMS
print('\'detection\' time consumption:', time.time() - t0)
utils.draw_boxes_cv2(filtered_boxes, frame, classes,
(FLAGS.size, FLAGS.size),
FLAGS.keep_aspect_ratio)
print('\n\n\n')
cv2.imshow('frame', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
break
cap.release()
cv2.destroyAllWindows()
def main(argv=None):
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=FLAGS.gpu_memory_fraction)
config = tf.ConfigProto(
gpu_options=gpu_options,
log_device_placement=False,
# inter_op_parallelism_threads=0,
# intra_op_parallelism_threads=0,
# device_count={"CPU": 6}
)
classes = utils.load_names(FLAGS.class_names)
input_size = (FLAGS.size, FLAGS.size)
img_pathes = [path for path in os.listdir(FLAGS.input_imgpath)
if path.endswith(('.jpg', '.png', '.bmp'))]
num_imgs = len(img_pathes)
batch_size = FLAGS.batch_size
img_list = []
img_batch_all = np.zeros((num_imgs, FLAGS.size, FLAGS.size, 3))
for k in range(num_imgs):
img_array = cv2.imread(os.path.join(FLAGS.input_imgpath, img_pathes[k]))
img_list.append(img_array)
img_batch_all[k] = utils.resize_cv2(img_array, input_size)[:, :, ::-1]
frozenGraph = utils.load_graph(FLAGS.frozen_model)
boxes, inputs = utils.get_boxes_and_inputs_pb(frozenGraph)
with tf.Session(graph=frozenGraph, config=config) as sess:
for i in range(0, num_imgs, batch_size):
if i < num_imgs - batch_size:
img_batch = img_batch_all[i:i + batch_size]
else:
img_batch = img_batch_all[i:]
detected_boxes = sess.run(boxes, feed_dict={inputs: img_batch})
filtered_boxes = utils.non_max_suppression(detected_boxes,
confidence_threshold=FLAGS.conf_threshold,
iou_threshold=FLAGS.iou_threshold)
for n, bboxes in enumerate(filtered_boxes):
img = img_list[i + n]
img_name = img_pathes[i + n]
utils.draw_boxes_cv2(bboxes, img, classes, input_size, keep_aspect_ratio=FLAGS.keep_aspect_ratio)
# cv2.imshow('image_{}'.format(img_name), img)
cv2.imwrite(os.path.join(FLAGS.output_imgpath, 'out_' + img_name), img)
print('{} has been processed !'.format(img_name))
print('#'*20)
def main(argv=None):
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=FLAGS.gpu_memory_fraction)
config = tf.ConfigProto(
gpu_options=gpu_options,
log_device_placement=False,
# inter_op_parallelism_threads=0,
# intra_op_parallelism_threads=0,
# device_count={"CPU": 6}
)
img = Image.open(FLAGS.input_img)
if FLAGS.keep_aspect_ratio:
img_resized = utils.letter_box_image(img, FLAGS.size, FLAGS.size, 128)
img_resized = img_resized.astype(np.float32)
else:
img_resized = img.resize((FLAGS.size, FLAGS.size), Image.BILINEAR)
img_resized = np.asarray(img_resized, dtype=np.float32)
classes = utils.load_names(FLAGS.class_names)
frozenGraph = utils.load_graph(FLAGS.frozen_model)
boxes, inputs = utils.get_boxes_and_inputs_pb(frozenGraph)
with tf.Session(graph=frozenGraph, config=config) as sess:
t0 = time.time()
detected_boxes = sess.run(boxes, feed_dict={inputs: [img_resized]})
print("Predictions found in {:.2f}s".format(time.time() - t0))
filtered_boxes = utils.non_max_suppression(
detected_boxes,
confidence_threshold=FLAGS.conf_threshold,
iou_threshold=FLAGS.iou_threshold)[0]
utils.draw_boxes(filtered_boxes, img, classes, (FLAGS.size, FLAGS.size),
FLAGS.keep_aspect_ratio)
img.save(FLAGS.output_img)
def main():
#load back args, checkpoint and labels
args = parse_args()
gpu = args.gpu
model = load_checkpoint(args.checkpoint)
cat_to_name = load_names(args.category_names)
img_path = args.filepath
#load probabilites calculates
probs, classes = predict(img_path, model, int(args.top_k), gpu)
labels = [cat_to_name[str(index)] for index in classes]
probability = probs
print('File: ' + img_path)
print(labels)
print(probability)
i = 0
while i < len(labels):
print("{} class returned at percentage {}".format(
labels[i], probability[i]))
#next iteration
i += 1
def main():
model = load_checkpoint(args.checkpoint)
category_names = load_names(args.category_names)
top_k = int(args.top_k)
devices = args.devices
if devices == 'gpu':
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
else:
device = torch.device('cpu')
img_path = args.img_input
top_prob, top_classes = predict(img_path, model, top_k)
max_index = top_classes[0]
names = [category_names[str(index)] for index in top_classes]
flower_name = category_names[str(max_index)]
i = 0
while i < len(names):
print("The probability for the category {} is {}".format(
names[i], top_prob[i]))
i += 1
print("The results indicate that this flower is is most likely to be {}".
format(flower_name))
print("Predition done!")
logger.info("Loaded configuration file {}".format(args.cfg))
logger.info("Running with config:\n{}".format(cfg))
cfg.MODEL.arch_encoder = cfg.MODEL.arch_encoder.lower()
cfg.MODEL.arch_decoder = cfg.MODEL.arch_decoder.lower()
# absolute paths of model weights
cfg.MODEL.weights_encoder = os.path.join(cfg.DIR,
'encoder_' + cfg.TEST.checkpoint)
cfg.MODEL.weights_decoder = os.path.join(cfg.DIR,
'decoder_' + cfg.TEST.checkpoint)
assert os.path.exists(cfg.MODEL.weights_encoder) and \
os.path.exists(cfg.MODEL.weights_decoder), "checkpoint does not exitst!"
# generate testing image list
if os.path.isdir(args.imgs):
imgs = find_recursive(args.imgs)
else:
imgs = [args.imgs]
assert len(imgs), "imgs should be a path to image (.jpg) or directory."
cfg.list_test = [{'fpath_img': x} for x in imgs]
if not os.path.isdir(cfg.TEST.result):
os.makedirs(cfg.TEST.result)
colors = load_colors(cfg.DATASET.colors_file)
names = load_names(cfg.DATASET.names_file)
main(cfg, args.gpu)
def main():
from matplotlib import pyplot as plt
parser = OptionParser()
parser.add_option('--model',
dest='model',
default='twitter.model',
help='model filename')
parser.add_option('--names',
dest='names',
default='data/names.json',
help='names filename')
options = parser.parse_args()[0]
names = load_names(options.names)
model = Doc2Vec.load(options.model)
print(len(list(model.docvecs)))
influencers = [str(uid) for uid in json.load(open('influencers.json'))]
influencers = [
uid for uid in influencers
if uid in model.docvecs and uid not in ignore_ids
]
print(len(influencers))
vectors = [model.docvecs[uid] for uid in influencers]
tsne = TSNE()
tsne.fit(vectors)
print(tsne.kl_divergence_)
positions = tsne.fit_transform(vectors)
vectors_writer = csv.writer(open('vectors.csv', 'w'))
positions_writer = csv.writer(open('positions.csv', 'w'))
positions_writer.writerow(['name', 'id', 'x', 'y'])
for uid, vector, position in zip(influencers, vectors, positions):
name = names.get(int(uid), '')
vectors_writer.writerow([name, uid] + list(vector))
positions_writer.writerow([name, uid] + list(position))
X = [[float(v) for v in row[2:]] for row in vectors]
print(X[0])
y = pdist(X)
Z = hierarchy.linkage(y)
print(hierarchy.fcluster(Z, 1))
plt.xlabel('sample index')
plt.ylabel('distance')
hierarchy.dendrogram(Z)
plt.savefig('hoge.pdf')
for k in range(2, 31):
kmeans = KMeans(n_clusters=k, n_init=1000, max_iter=1000).fit(X)
print('{}\t{}'.format(k, kmeans.inertia_))
k = 7
kmeans = KMeans(n_clusters=k, n_init=10000, max_iter=1000).fit(X)
writer = csv.writer(open('cluster.csv', 'w'))
writer.writerow(['name', 'id', 'label', 'x'] +
['y{}'.format(i) for i in range(k)])
for uid, l, row in zip(influencers, kmeans.labels_, positions):
name = names.get(int(uid), '')
x, y = row
cols = ['' for _ in range(k)]
cols[l] = y
writer.writerow([name, uid] + [l, x] + cols)
clusters = {uid: int(l) for uid, l in zip(influencers, kmeans.labels_)}
print(clusters)
json.dump(clusters, open('clusters.json', 'w'))
def main():
parser = OptionParser()
parser.add_option('-o', dest='filename', default='graph.gexf',
help='output filename')
parser.add_option('--names', dest='names', default='data/names.json',
help='names filename')
parser.add_option('--clusters', dest='clusters',
default='data/clusters.json',
help='clusters filename')
parser.add_option('--start', dest='start', default='20110301',
help='start date (default=20110301)')
parser.add_option('--stop', dest='stop', default='20130401',
help='stop date (default=20130401)')
parser.add_option('--sampling', dest='sampling', type='float', default=1.0,
help='tweet sampling rate (default=1.0)')
parser.add_option('--influencers', dest='n', type='int', default=None,
help='maximum number of influencers (default=inf)')
parser.add_option('--mincount', dest='mincount', type='int', default=1,
help='minimum number of retweet (default=1)')
parser.add_option('--mindegree', dest='mindegree', type='int', default=0,
help='minimum acceptable degree of nodes (default=0)')
parser.add_option('--group', dest='group', default='month',
help='month or week (default=month)')
options, args = parser.parse_args()
names = load_names(options.names)
clusters = load_clusters(options.clusters)
start_date = ymd_to_datetime(options.start)
stop_date = ymd_to_datetime(options.stop)
tweets = load_tweet(*args)
tweets = filter_by_datetime(tweets, start_date, stop_date)
random.seed(0)
tweets = sampling(tweets, options.sampling)
if options.group == 'month':
groups = [t.strftime('t%Y%m%d') for t
in months_between(start_date, stop_date)]
else:
groups = [t.strftime('t%Y%m%d') for t
in weeks_between(start_date, stop_date)]
graph = nx.DiGraph()
graph.graph['groups'] = groups
for ruid, tss in group_by_influencer(tweets, options.n, options.mincount):
for uid, ts in group_by_user(tss):
ts = list(ts)
labels = {d: False for d in groups}
for t in ts:
d = to_datetime(t['created_at']).strftime('t%Y%m%d')
labels[groups[bisect_right(groups, d) - 1]] = True
graph.add_edge(ruid, uid, weight=len(ts), **labels)
for node in graph.nodes():
if graph.degree(node, 'weight') < options.mindegree:
graph.remove_node(node)
continue
graph.node[node]['label'] = names.get(node, '')
graph.node[node]['cluster'] = clusters.get(node, -1)
if graph.out_degree(node) == 0:
cluster_count = {}
for ruid in graph.predecessors(node):
c = clusters.get(ruid, -1)
if c not in cluster_count:
cluster_count[c] = 0
cluster_count[c] += 1
graph.node[node]['cluster'] = max(cluster_count.items(),
key=lambda r: r[1])[0]
for d in groups:
graph.node[node][d] = False
for d in groups:
for u, v in graph.edges():
if graph[u][v][d]:
graph.node[u][d] = True
graph.node[v][d] = True
out_format = options.filename.split('.')[-1]
if out_format == 'gexf':
nx.write_gexf(graph, options.filename)
else:
obj = json_graph.node_link_data(graph)
json.dump(obj, open(options.filename, 'w'))
print('(|V|, |E|) = ({}, {})'.format(graph.number_of_nodes(),
graph.number_of_edges()))
