Nsryjdtyk

My code can detect an object now, which is human and other objects. But the issue that i faced now is i want to count the moving human entering a classroom. I would like to count in and out so that i can know the number of people in a room. How can do this? something like this https://www.youtube.com/watch?v=aEcBnD80nL

How can i make that detected object in rectangle to count in and out when they pass through the red and blue line?

import os

import cv2

import numpy as np

from picamera.array import PiRGBArray

from picamera import PiCamera

import tensorflow as tf

import argparse

import sys



# Set up camera constants

IM_WIDTH = 1280

IM_HEIGHT = 720

#IM_WIDTH = 640    Use smaller resolution for

#IM_HEIGHT = 480   slightly faster framerate



# Select camera type (if user enters --usbcam when calling this script,

# a USB webcam will be used)

camera_type = 'picamera'

parser = argparse.ArgumentParser()

parser.add_argument('--usbcam', help='Use a USB webcam instead of picamera',

                action='store_true')

args = parser.parse_args()

if args.usbcam:

camera_type = 'usb'



# This is needed since the working directory is the object_detection folder.

sys.path.append('..')



# Import utilites

from utils import label_map_util

from utils import visualization_utils as vis_util



# Name of the directory containing the object detection module we're using

MODEL_NAME = 'ssdlite_mobilenet_v2_coco_2018_05_09'



# Grab path to current working directory

CWD_PATH = os.getcwd()



# Path to frozen detection graph .pb file, which contains the model that is used

# for object detection.

PATH_TO_CKPT = os.path.join(CWD_PATH,MODEL_NAME,'frozen_inference_graph.pb')



# Path to label map file

PATH_TO_LABELS = os.path.join(CWD_PATH,'data','mscoco_label_map.pbtxt')



# Number of classes the object detector can identify

NUM_CLASSES = 90



## Load the label map.

# Label maps map indices to category names, so that when the convolution

# network predicts `5`, we know that this corresponds to `airplane`.

# Here we use internal utility functions, but anything that returns a

# dictionary mapping integers to appropriate string labels would be fine

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)



# Load the Tensorflow model into memory. 

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='')



sess = tf.Session(graph=detection_graph)





# Define input and output tensors (i.e. data) for the object detection 

classifier



# Input tensor is the image

image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')



# Output tensors are the detection boxes, scores, and classes

# 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 represents level of confidence for each of the objects.

# The 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')



# Number of objects detected

num_detections = detection_graph.get_tensor_by_name('num_detections:0')



# Initialize frame rate calculation

frame_rate_calc = 1

freq = cv2.getTickFrequency()

font = cv2.FONT_HERSHEY_SIMPLEX



# Initialize camera and perform object detection.

# The camera has to be set up and used differently depending on if it's a

# Picamera or USB webcam.



# I know this is ugly, but I basically copy+pasted the code for the object

# detection loop twice, and made one work for Picamera and the other work

# for USB.



### Picamera ###

if camera_type == 'picamera':

# Initialize Picamera and grab reference to the raw capture

camera = PiCamera()

camera.resolution = (IM_WIDTH,IM_HEIGHT)

camera.framerate = 10

rawCapture = PiRGBArray(camera, size=(IM_WIDTH,IM_HEIGHT))

rawCapture.truncate(0)



for frame1 in camera.capture_continuous(rawCapture, format="bgr",use_video_port=True):



    t1 = cv2.getTickCount()



    # Acquire frame and expand frame dimensions to have shape: [1, None, None, 3]

    # i.e. a single-column array, where each item in the column has the pixel RGB value

    frame = frame1.array

    frame.setflags(write=1)

    frame_expanded = np.expand_dims(frame, axis=0)



    # Perform the actual detection by running the model with the image as input

    (boxes, scores, classes, num) = sess.run(

        [detection_boxes, detection_scores, detection_classes, num_detections],

        feed_dict={image_tensor: frame_expanded})



    # Draw the results of the detection (aka 'visulaize the results')

    vis_util.visualize_boxes_and_labels_on_image_array(

        frame,

        np.squeeze(boxes),

        np.squeeze(classes).astype(np.int32),

        np.squeeze(scores),

        category_index,

        use_normalized_coordinates=True,

        line_thickness=8,

        min_score_thresh=0.40)



    # Blue line

    cv2.line(frame, (IM_WIDTH // 2, 0), (IM_WIDTH // 2 , IM_WIDTH), (250, 0, 1), 2)

    # Red line

    cv2.line(frame, (IM_WIDTH // 2 - 50, 0), (IM_WIDTH // 2 - 50, IM_WIDTH), (0, 0, 255), 2)



    # FPS Text

    cv2.putText(frame,"FPS: {0:.2f}".format(frame_rate_calc),(30,50),font,1,(255,255,0),2,cv2.LINE_AA)



    # All the results have been drawn on the frame, so it's time to display it.

    cv2.imshow('Object detector', frame)



    t2 = cv2.getTickCount()

    time1 = (t2-t1)/freq

    frame_rate_calc = 1/time1



    # Press 'q' to quit

    if cv2.waitKey(1) == ord('q'):

        break



    rawCapture.truncate(0)



camera.close()