def process(input_video):
vcap = cv2.VideoCapture(input_video)
if vcap.isOpened():
# get vcap property
width = int(vcap.get(3))
height = int(vcap.get(4))
# it gives me 0.0 :/
fps = int(vcap.get(5))
detection_graph, category_index = backbone.set_model('peixe_v2_coco_t2')
#object_counting_api.object_counting(input_video, detection_graph, category_index, 0) # for counting all the objects, disabled color prediction
#object_counting_api.object_counting(input_video, detection_graph, category_index, 1) # for counting all the objects, enabled color prediction
targeted_objects = "peixe" # (for counting targeted objects) change it with your targeted objects
is_color_recognition_enabled = 0
object_counting_api.targeted_object_counting(
input_video, detection_graph, category_index,
is_color_recognition_enabled, targeted_objects, fps, width,
height) # targeted objects counting
def call_tensor():
import tensorflow as tf
from utils import backbone
from api import object_counting_api
import cv2
from carton.cart import Cart
# from carton.cart import
input_video = 1
detection_graph, category_index = backbone.set_model(
'faster_rcnn_inception_v2_coco_2018_01_28')
targeted_objects = "person" # (for counting targeted objects) change it with your targeted objects
fps = 24 # change it with your input video fps
width = 854 # change it with your input video width
height = 480 # change it with your input vide height
is_color_recognition_enabled = 0
object_counting_api.targeted_object_counting(
input_video, detection_graph, category_index,
is_color_recognition_enabled, targeted_objects, fps, width,
height) # targeted objects counting
# 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 = "New Office TOUR! Karlie Kloss.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_2017_11_17')
#object_counting_api.object_counting(input_video, detection_graph, category_index, 0) # for counting all the objects, disabled color prediction
#object_counting_api.object_counting(input_video, detection_graph, category_index, 1) # for counting all the objects, enabled color prediction
targeted_objects = "person" # (for counting targeted objects) change it with your targeted objects
fps = 24 # change it with your input video fps
width = 854 # change it with your input video width
height = 480 # change it with your input vide height
is_color_recognition_enabled = 0
object_counting_api.targeted_object_counting(
input_video, detection_graph, category_index, is_color_recognition_enabled,
targeted_objects, fps, width, height) # targeted objects counting
#object_counting_api.object_counting(input_video, detection_graph, category_index, is_color_recognition_enabled, fps, width, height) # counting all the objects
import tensorflow as tf
# Object detection imports
from utils import backbone
from api import object_counting_api
input_video = "./input_images_and_videos/demo (6).mp4"
detection_graph, category_index = backbone.set_model('custom_trained_inference_graph', 'label_map.pbtxt')
targeted_objects = "leaf, pill_pack, plastic"
is_color_recognition_enabled = 0
object_counting_api.targeted_object_counting(input_video, detection_graph, category_index, is_color_recognition_enabled, targeted_objects)
from utils import backbone
from api import object_counting_api
INPUT_STREAM = "./MVI_6835.mp4"
CPU_EXTENSION = "/opt/intel/openvino/deployment_tools/inference_engine/lib/intel64/libcpu_extension_sse4.so"
FILE_OUTPUT = "/content/output.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')
targeted_objects = "person, bicycle, bus, car, motorcycle, airplane, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, book, cell phone, laptop, wine glass, bottle, handbag, cat, dog, bird" # (for counting targeted objects) change it with your targeted objects
is_color_recognition_enabled = 0
object_counting_api.targeted_object_counting(
INPUT_STREAM, detection_graph, category_index,
is_color_recognition_enabled,
targeted_objects) # targeted objects counting
def get_args():
'''
Gets the arguments from the command line.
'''
parser = argparse.ArgumentParser("Run inference on an input video")
# -- Create the descriptions for the commands
m_desc = "The location of the model XML file"
i_desc = "The location of the input file"
d_desc = "The device name, if not 'CPU'"
### TODO: Add additional arguments and descriptions for:
### 1) Different confidence thresholds used to draw bounding boxes
### 2) The user choosing the color of the bounding boxes
