How to count vehicles using opencv in python? - opencv

Question

I am working on a VCS (vehicle counting system) project. The scope of the project is to classify and count vehicles. I have built a custom model using Faster-RCNN in Tensorflow-object-detection-API This model only contains 7 classes such as car motorbike, bicycle and etc. The model works perfectly, But, the problem is "COUNTING". It is very hard to count vehicles in video frame. I did a pre-research on the internet. I tried a lot. but i could not find any useful information. There are some projects on github, they use tracking methods.
I want the following things. I want to draw an horizontal line in the frame. when the vehicle touch it, the counting should take place. How to do it. I don't know the algorithm behind it. I heard that centroid tracking would help me.
My question is, i want to count vehicles when it touch the horizontal line. I have linked a sample image bellow.
Sample_Image
import os
import cv2
import numpy as np
import tensorflow as tf
import sys
# This is needed since the notebook is stored in 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 = 'inference_graph'
VIDEO_NAME = 'Video_105.mp4'
# 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,'training','labelmap.pbtxt')
# Path to video
PATH_TO_VIDEO = os.path.join(CWD_PATH,VIDEO_NAME)
# Number of classes the object detector can identify
NUM_CLASSES = 7
# Load the label map.
# Label maps map indices to category names, so that when our convolution
# network predicts `5`, we know that this corresponds to `king`.
# 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')
# Open video file
video = cv2.VideoCapture(PATH_TO_VIDEO)
while(video.isOpened()):
# 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
ret, frame = video.read()
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=0.90)
# All the results have been drawn on the frame, so it's time to display it.
final_score = np.squeeze(scores)
count = 0
cv2.line(frame, (1144, 568), (1723,664), (0,0,255), 2) #Line
for i in range(100):
if scores is None or final_score[i] > 0.90:
min_score_thresh = 0.90
bboxes = boxes[scores > min_score_thresh]
im_width = video.get(cv2.CAP_PROP_FRAME_WIDTH)
im_height = video.get(cv2.CAP_PROP_FRAME_HEIGHT)
final_box = []
for box in bboxes:
ymin, xmin, ymax, xmax = box
print("Ymin:{}:Xmin:{}:Ymax:{}Xmax{}".format(ymin*im_width,xmin*im_width,ymax*im_width,xmax*im_width))
final_box.append([xmin * im_width, xmax * im_width, ymin * im_height, ymax * im_height])
#print(final_box)
cv2.imshow('Object detector', frame)
# Press 'q' to quit
if cv2.waitKey(1) == ord('q'):
break
# Clean up
video.release()
cv2.destroyAllWindows()

Answer 1

# import the necessary packages
from imutils.video import VideoStream
from imutils.video import FPS
import argparse
import imutils
import time
import cv2
tracker = cv2.TrackerCSRT_create()
vs = cv2.VideoCapture("Video.mp4")
initBB = None
detec = []
def pega_centro(x, y, w, h):
x1 = int(w / 2)
y1 = int(h / 2)
cx = x + x1
cy = y + y1
return cx,cy
roi = 480
counter = 0
offset = 6
# loop over frames from the video stream
while vs.isOpened():
ret,frame = vs.read()
cv2.line(frame, (769 , roi), (1298 , roi), (255,0,0), 3)
# check to see if we are currently tracking an object
if initBB is not None:
# grab the new bounding box coordinates of the object
(success, box) = tracker.update(frame)
# check to see if the tracking was a success
if success:
(x, y, w, h) = [int(v) for v in box]
cv2.rectangle(frame, (x, y), (x + w, y + h),
(0, 255, 0), 2)
cX = int((x + x+w) / 2.0)
cY = int((y + y+h) / 2.0)
cv2.circle(frame, (cX, cY), 3, (0, 0, 255), -1)
c=pega_centro(x, y, w, h)
detec.append(c)
for (x,y) in detec:
if y<(roi+offset) and y>(roi-offset):
counter+=1
print(counter)
cv2.line(frame, (769 , roi), (1298 , roi), (0,0,255), 3)
detec.remove((x,y))
# show the output frame
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
if key == ord("s"):
# select the bounding box of the object we want to track (make
# sure you press ENTER or SPACE after selecting the ROI)
initBB = cv2.selectROI("Frame", frame, fromCenter=False,
showCrosshair=True)
# start OpenCV object tracker using the supplied bounding box
# coordinates, then start the FPS throughput estimator as well
tracker.init(frame, initBB)
fps = FPS().start()
# if the `q` key was pressed, break from the loop
elif key == ord("q"):
break
else:
vs.release()
cv2.destroyAllWindows()