def post(self, request, *args, **kwargs):
if request.is_ajax():
if request.POST.get('all') == 'true':
path = os.path.join(settings.BASE_DIR, settings.STUB, settings.SAMPLE_NAME)
with open(path, 'r') as f:
data = json.load(f)
else:
data = {'message': _('error!')}
if request.POST.get('csv', 'false') != 'true':
response = HttpResponse(json.dumps(data), content_type='application/json')
else:
response = HttpResponse(content_type='text/csv')
else:
response = super(WordleView, self).post(request, *args, **kwargs)
if request.POST.get('csv') == 'true':
response['Content-Disposition'] = 'attachment; filename="cloud.csv"'
helpers.json2csv(response, data['nodes'])
return response
def post(self, request, *args, **kwargs):
response = HttpResponse()
if request.is_ajax():
if request.POST.get("all") == "true":
path = os.path.join(settings.BASE_DIR, settings.STUB, settings.SAMPLE_NAME)
with open(path) as f:
data = json.load(f)
elif request.POST.get("verbatim") == "true":
code_id = request.POST.get("id")
data = self.get_verbatim(code_id)
else:
data = {"message": _("error")}
if request.POST.get("csv", "false") != "true":
response = HttpResponse(json.dumps(data), content_type="application/json")
else:
response["content_type"] = "text/csv"
if request.POST.get("csv") == "true":
response["Content-Disposition"] = 'attachment; filename="tree.csv"'
helpers.json2csv(response, data["nodes"])
return response
def post(self, request, *args, **kwargs):
response = HttpResponse()
if request.is_ajax():
if request.POST.get('all') == 'true':
path = os.path.join(settings.BASE_DIR, settings.STUB,
settings.SAMPLE_NAME)
with open(path) as f:
data = json.load(f)
elif request.POST.get('verbatim') == 'true':
code_id = request.POST.get('id')
data = self.get_verbatim(code_id)
else:
data = {'message': _('error')}
if request.POST.get('csv', 'false') != 'true':
response = HttpResponse(json.dumps(data),
content_type='application/json')
else:
response['content_type'] = 'text/csv'
if request.POST.get('csv') == 'true':
response['Content-Disposition'] = 'attachment; filename="tree.csv"'
helpers.json2csv(response, data['nodes'])
return response
def post(self, request, *args, **kwargs):
if request.is_ajax():
if request.POST.get("all") == "true":
# FIX: temporary data stub
path = os.path.join(settings.BASE_DIR, stub, sample_name)
with open(path, "r") as f:
data = json.load(f)
elif request.POST.get("verbatim") == "true":
code_id = request.POST.get("id")
data = self.get_verbatim(code_id)
else:
data = {"message": _("error!")}
if request.POST.get("csv", "false") != "true":
response = HttpResponse(json.dumps(data), content_type="application/json")
else:
response = HttpResponse(content_type="text/csv")
else:
response = super(CloudView, self).get(request, *args, **kwargs)
if request.POST.get("csv") == "true":
response["Content-Disposition"] = 'attachment; filename="cloud.csv"'
helpers.json2csv(response, data["nodes"])
return response
def post(self, request, *args, **kwargs):
if request.is_ajax():
if request.POST.get('all') == 'true':
# FIX: temporary data stub
path = os.path.join(settings.BASE_DIR, stub, sample_name)
with open(path, 'r') as f:
data = json.load(f)
elif request.POST.get('verbatim') == 'true':
code_id = request.POST.get('id')
data = self.get_verbatim(code_id)
else:
data = {'message': _('error!')}
if request.POST.get('csv', 'false') != 'true':
response = HttpResponse(json.dumps(data),
content_type='application/json')
else:
response = HttpResponse(content_type='text/csv')
else:
response = super(CloudView, self).get(request, *args, **kwargs)
if request.POST.get('csv') == 'true':
response[
'Content-Disposition'] = 'attachment; filename="cloud.csv"'
helpers.json2csv(response, data['nodes'])
return response
def object_detect(model_name,
url_file,
extension=EXTENSION,
downloaded=DOWNLOADED,
json_output_file=JSON_OUTPUT_FILE,
n_threads=N_THREADS,
visualize=VISUALIZE,
path_to_labels=PATH_TO_LABELS):
model_file = model_name + '.tar.gz'
path_to_ckpt = model_name + '/frozen_inference_graph.pb'
json_url_file = url_file + '.json'
csv_url_file = url_file + '.csv'
helpers.download_extract_model(model_file=model_file,
downloaded=downloaded)
if extension == '.json':
helpers.json2csv(json_name=json_url_file, csv_name=csv_url_file)
li = pd.read_csv(csv_url_file)
url_li = li['img_url'].tolist()
id_li = li['img_id'].tolist()
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(path_to_ckpt, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
with detection_graph.as_default():
im = helpers.url_image_reader(url_li)
queue = tf.PaddingFIFOQueue(capacity=500,
dtypes=tf.uint8,
shapes=[(None, None, None)])
enq_op = queue.enqueue(im)
inputs = queue.dequeue_many(BATCH_SIZE)
qr = tf.train.QueueRunner(queue, [enq_op] * n_threads)
with tf.Session(graph=detection_graph) as sess:
sess.run(tf.global_variables_initializer())
coord = tf.train.Coordinator()
enqueue_threads = qr.create_threads(sess=sess,
coord=coord,
start=True)
conversion_time = []
ix = 1
res = {}
category_index = helpers.load_category_index(path_to_labels)
t = time.time()
try:
while not coord.should_stop():
image = sess.run(
inputs) # Tensor of dimension (1, None, None, 3)
print('Processing Image', ix)
image_tensor = detection_graph.get_tensor_by_name(
'image_tensor:0')
boxes = detection_graph.get_tensor_by_name(
'detection_boxes:0')
scores = detection_graph.get_tensor_by_name(
'detection_scores:0')
classes = detection_graph.get_tensor_by_name(
'detection_classes:0')
num_detections = detection_graph.get_tensor_by_name(
'num_detections:0')
t2 = time.time()
(boxes, scores, classes, num_detections) = sess.run(
[boxes, scores, classes, num_detections],
feed_dict={image_tensor: image})
conversion_time.append(time.time() - t2)
print('Image', ix, 'Processing Time:',
conversion_time[ix - 1], 'sec')
res[id_li[ix - 1]] = {
'boxes': np.squeeze(boxes),
'scores': np.squeeze(scores),
'classes': np.squeeze(classes).astype(np.int32),
'num_detections': num_detections
}
ix += 1
except tf.errors.OutOfRangeError:
print('Total Image Processing Time:', sum(conversion_time),
'sec')
print('Total Time Consumed:', time.time() - t, 'sec')
finally:
coord.request_stop()
helpers.dict2json(res, json_output_file)
if visualize:
helpers.visualize(csv_url_file, res, category_index)
coord.join(enqueue_threads)
return res