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vehicle_detection_main.py
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#!/usr/bin/python
# -*- coding: utf-8 -*-
# ----------------------------------------------
# --- Author : Ahmet Ozlu
# --- Mail : ahmetozlu93@gmail.com
# --- Date : 27th January 2018
# ----------------------------------------------
# Imports
import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
import cv2
import numpy as np
import csv
import time
from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image
# Object detection imports
from utils import label_map_util
from utils import visualization_utils as vis_util
# Detection
def object_detection_function(input_video, detection_graph, category_index, width, roi):
# initialize .csv
with open('traffic_measurement.csv', 'w') as f:
writer = csv.writer(f)
csv_line = \
'Vehicle Type/Size, Vehicle Color, Vehicle Movement Direction, Vehicle Speed (km/h)'
writer.writerows([csv_line.split(',')])
total_passed_vehicle = 0
speed = 'waiting...'
direction = 'waiting...'
size = 'waiting...'
color = 'waiting...'
#input video
cap = cv2.VideoCapture(input_video)
with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:
# Definite input and output Tensors for detection_graph
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# 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 represent how level of confidence for each of the objects.
# 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')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
# for all the frames that are extracted from input video
while cap.isOpened():
(ret, frame) = cap.read()
if not ret:
print ('end of the video file...')
break
input_frame = frame
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(input_frame, axis=0)
# Actual detection.
(boxes, scores, classes, num) = \
sess.run([detection_boxes, detection_scores,
detection_classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
# Visualization of the results of a detection.
(counter, csv_line) = \
vis_util.visualize_boxes_and_labels_on_image_array(
cap.get(1),
input_frame,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=4,
)
total_passed_vehicle = total_passed_vehicle + counter
# insert information text to video frame
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(
input_frame,
'Detected Vehicles: ' + str(total_passed_vehicle),
(10, 35),
font,
0.8,
(0, 0xFF, 0xFF),
2,
cv2.FONT_HERSHEY_SIMPLEX,
)
# when the vehicle passed over line and counted, make the color of ROI line green
if counter == 1:
cv2.line(input_frame, (0, roi), (width, roi), (0, 0xFF, 0), 5)
else:
cv2.line(input_frame, (0, roi), (width, roi), (0, 0, 0xFF), 5)
# insert information text to video frame
cv2.rectangle(input_frame, (10, 275), (230, 337), (180, 132, 109), -1)
cv2.putText(
input_frame,
'ROI Line',
(545, 190),
font,
0.6,
(0, 0, 0xFF),
2,
cv2.LINE_AA,
)
cv2.putText(
input_frame,
'LAST PASSED VEHICLE INFO',
(11, 290),
font,
0.5,
(0xFF, 0xFF, 0xFF),
1,
cv2.FONT_HERSHEY_SIMPLEX,
)
cv2.putText(
input_frame,
'-Movement Direction: ' + direction,
(14, 302),
font,
0.4,
(0xFF, 0xFF, 0xFF),
1,
cv2.FONT_HERSHEY_COMPLEX_SMALL,
)
cv2.putText(
input_frame,
'-Speed(km/h): ' + speed,
(14, 312),
font,
0.4,
(0xFF, 0xFF, 0xFF),
1,
cv2.FONT_HERSHEY_COMPLEX_SMALL,
)
cv2.putText(
input_frame,
'-Color: ' + color,
(14, 322),
font,
0.4,
(0xFF, 0xFF, 0xFF),
1,
cv2.FONT_HERSHEY_COMPLEX_SMALL,
)
cv2.putText(
input_frame,
'-Vehicle Size/Type: ' + size,
(14, 332),
font,
0.4,
(0xFF, 0xFF, 0xFF),
1,
cv2.FONT_HERSHEY_COMPLEX_SMALL,
)
cv2.imshow('vehicle detection', input_frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if csv_line != 'not_available':
with open('traffic_measurement.csv', 'a') as f:
writer = csv.writer(f)
(size, color, direction, speed) = \
csv_line.split(',')
writer.writerows([csv_line.split(',')])
cap.release()
cv2.destroyAllWindows()
object_detection_function()