def process_facerecog(): input_video = 0 # 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_2017_11_17') targeted_objects = "person" fps = 24 # change it with your input video fps width = 640 # change it with your input video width height = 480 # change it with your input vide height is_color_recognition_enabled = 0 # set it to 1 for enabling the color prediction for the detected objects roi = 350 # roi line position deviation = 3 # the constant that represents the object counting area object_counting_api.cumulative_object_counting_y_axis(input_video, detection_graph, category_index, is_color_recognition_enabled, targeted_objects,fps, width, height, roi, deviation) # counting all the objects
#---------------------------------------------- #--- 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/vehicle_survaillance.mp4" # 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 = 1 # set it to 1 for enabling the color prediction for the detected objects roi = 185 # roi line position deviation = 2 # the constant that represents the object counting area custom_object_name = 'Vehicle' # set it to your custom object name object_counting_api.cumulative_object_counting_y_axis(input_video, detection_graph, category_index, is_color_recognition_enabled, roi, deviation, custom_object_name) # counting all the objects
# Imports import tensorflow as tf # Object detection imports from utils import backbone from api import object_counting_api # if tf.__version__ < '1.4.0': # raise ImportError('Please upgrade your tensorflow installation to v1.4.* or later!') input_video = "demovideo.mp4" # 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('fcc') #fps = 24 # change it with your input video fps #width = 640 # change it with your input video width #height = 352 # change it with your input vide height fps = 30 # change it with your input video fps width = 670 # change it with your input video width height = 360 # change it with your input vide height is_color_recognition_enabled = 0 # set it to 1 for enabling the color prediction for the detected objects #roi = 200 # roi line position roi = 180 deviation = 3 # the constant that represents the object counting area object_counting_api.cumulative_object_counting_y_axis( input_video, detection_graph, category_index, is_color_recognition_enabled, fps, width, height, roi, deviation) # counting all the objects
import tensorflow as tf from utils import backbone from api import object_counting_api input_video = "./input_images_and_videos/object_test.mp4" detection_graph, category_index = backbone.set_model( 'ssd_mobilenet_v1_coco_2018_01_28', 'mscoco_label_map.pbtxt') is_color_recognition_enabled = 0 roi = 185 deviation = 2 object_counting_api.cumulative_object_counting_y_axis( input_video, detection_graph, category_index, is_color_recognition_enabled, roi, deviation) #object_counting_api.object_counting_webcam(detection_graph, category_index, is_color_recognition_enabled)