def getNumberOfPerson(img,place):
input_video = img
detection_graph, category_index = backbone.set_model('ssd_mobilenet_v2_coco_2018_03_29', 'mscoco_label_map.pbtxt')
is_color_recognition_enabled = 0
result = object_counting_api.single_image_object_counting(input_video, detection_graph, category_index, is_color_recognition_enabled) # targeted objects counting
cv2.destroyAllWindows()
info_dict={}
try:
idx = result.find('person')
info = result[idx:idx+11]
#print(info)
#print(int(info[-1]))
complexity = cal_complexity(int(info[-1]))
info_dict = {}
info_dict['person'] = int(info[-1])
info_dict['place']= place
info_dict['complexity'] = complexity
info_dict['time']=datetime.datetime.now().strftime('%Y-%m-%d-%H-%M')
except:
info_dict['person']= None
info_dict['person'] = None
info_dict['place']= None
info_dict['complexity'] = None
info_dict['time']=None
return info_dict
def count(image="ParkingLot.jpg"):
input_video = image
detection_graph, category_index = backbone.set_model(
'ssd_mobilenet_v1_coco_2018_01_28', 'mscoco_label_map.pbtxt')
is_color_recognition_enabled = 0
result = object_counting_api.single_image_object_counting(
input_video, detection_graph, category_index,
is_color_recognition_enabled) # targeted objects counting
print(result)
return result
def count(image):
input_image = image
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)
fps = 30 # change it with your input video fps
width = 626 # change it with your input video width
height = 360 # change it with your input vide height
is_color_recognition_enabled = 0
result = object_counting_api.single_image_object_counting(input_image, detection_graph, category_index, is_color_recognition_enabled, fps, width, height) # targeted objects counting
return result
#---------------------------------------------- #--- Author : Ahmet Ozlu #--- Mail : [email protected] #--- Date : 27th January 2018 #---------------------------------------------- # Imports import tensorflow as tf # Object detection imports from utils import backbone from api import object_counting_api input_video = "./input_images_and_videos/sample_input_image.jpg" # By default I use an "SSD with Mobilenet" model here. See the detection model zoo (https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md) for a list of other models that can be run out-of-the-box with varying speeds and accuracies. detection_graph, category_index = backbone.set_model( 'ssd_mobilenet_v1_coco_2018_01_28', 'mscoco_label_map.pbtxt') is_color_recognition_enabled = False # set it to true for enabling the color prediction for the detected objects result = object_counting_api.single_image_object_counting( input_video, detection_graph, category_index, is_color_recognition_enabled) # targeted objects counting print(result)