def __init__(self):
# detection_graph, self.category_index = backbone.set_model('ssd_mobilenet_v1_fpn_shared_box_predictor_640x640_coco14_sync_2018_07_03',
# 'mscoco_label_map.pbtxt')
# # detection_graph, self.category_index = backbone.set_model(
# # 'faster_rcnn_resnet50_coco_2018_01_28',
# # 'mscoco_label_map.pbtxt')
self.detection_graph, self.category_index = backbone.set_model(
"ssd_mobilenet_v1_coco_2018_01_28", "mscoco_label_map.pbtxt")
def __init__(self):
detection_graph, self.category_index = backbone.set_model(
"ssd_mobilenet_v1_coco_2018_01_28", "mscoco_label_map.pbtxt"
)
self.sess = tf.compat.v1.InteractiveSession(graph=detection_graph)
self.image_tensor = detection_graph.get_tensor_by_name("image_tensor:0")
self.detection_boxes = detection_graph.get_tensor_by_name("detection_boxes:0")
self.detection_scores = detection_graph.get_tensor_by_name("detection_scores:0")
self.detection_classes = detection_graph.get_tensor_by_name(
"detection_classes:0"
)
self.num_detections = detection_graph.get_tensor_by_name("num_detections:0")
def __init__(self):
# detection_graph, self.category_index = backbone.set_model('ssd_mobilenet_v1_fpn_shared_box_predictor_640x640_coco14_sync_2018_07_03',
# 'mscoco_label_map.pbtxt')
# # detection_graph, self.category_index = backbone.set_model(
# # 'faster_rcnn_resnet50_coco_2018_01_28',
# # 'mscoco_label_map.pbtxt')
detection_graph, self.category_index = backbone.set_model(
"ssd_mobilenet_v1_coco_2018_01_28", "mscoco_label_map.pbtxt")
self.sess = tf.compat.v1.InteractiveSession(graph=detection_graph)
self.image_tensor = detection_graph.get_tensor_by_name(
"image_tensor:0")
self.detection_boxes = detection_graph.get_tensor_by_name(
"detection_boxes:0")
self.detection_scores = detection_graph.get_tensor_by_name(
"detection_scores:0")
self.detection_classes = detection_graph.get_tensor_by_name(
"detection_classes:0")
self.num_detections = detection_graph.get_tensor_by_name(
"num_detections:0")
def objectGet(input_video, olc):
total_passed_object = 0
# input video
deviation = 3 # the constant that represents the object counting area
videoSource = openCV.VideoCapture(input_video)
width = int(videoSource.get(openCV.CAP_PROP_FRAME_WIDTH))
# print("Height: {0}\nWidth: {1}\nFPS: {2}")
detection_graph, category_index = backbone.set_model(
'ssd_mobilenet_v1_coco_2017_11_17')
# counting_mode = "..."
with detection_graph.as_default():
with tensor.Session(graph=detection_graph) as sess:
# Definite input and output Tensors for detection_graph
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
detection_boxes = detection_graph.get_tensor_by_name(
'detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
detection_scores = detection_graph.get_tensor_by_name(
'detection_scores:0')
detection_classes = detection_graph.get_tensor_by_name(
'detection_classes:0')
num_detections = detection_graph.get_tensor_by_name(
'num_detections:0')
# looping in all frames from camera or video stream
while (videoSource.isOpened()):
ret, frame = videoSource.read()
if not ret:
print("Running Finished")
databaseInsert(host, user, passwd, db, csv_file)
break
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(frame, axis=0)
# Actual detection.
boxes, scores, classes, num = sess.run(
[
detection_boxes, detection_scores, detection_classes,
num_detections
],
feed_dict={image_tensor: image_np_expanded})
# insert information text to video frame
# Visualization of the results of a detection.
counter, csv_line, counting_mode = drawing.visualizeBoxes(
videoSource.get(1),
frame,
2,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
y_reference=olc,
deviation=deviation,
use_normalized_coordinates=True,
line_thickness=4)
# insert information text to video frame if mode debug is on
print("---------------------------{0}".format(counter))
if debug_Mode == "on":
# when the object passed over line and counted, make the color of OLC line green
if counter == 1:
openCV.line(frame, (0, olc), (width, olc),
(0, 0xFF, 0), 5)
else:
openCV.line(frame, (0, olc), (width, olc),
(0, 0, 0xFF), 5)
total_passed_object = total_passed_object + counter
font = openCV.FONT_HERSHEY_SIMPLEX
openCV.putText(
frame,
'Total Objects: ' + str(total_passed_object),
(10, 35),
font,
0.8,
(0, 0x00, 0xFF),
2,
openCV.FONT_HERSHEY_SIMPLEX,
)
openCV.putText(
frame,
'OLC Line',
(545, olc - 10),
font,
0.6,
(0, 0, 0xFF),
2,
openCV.LINE_AA,
)
openCV.imshow('object counting', frame)
if openCV.waitKey(1) & 0xFF == ord('q'):
break
videoSource.release()
openCV.destroyAllWindows()
