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HumanDetector.py
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HumanDetector.py
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# coding: utf-8
# License: Apache License 2.0 (https://github.com/tensorflow/models/blob/master/LICENSE)
# source: https://github.com/tensorflow/models
########## CONFIGURATION ################
IP = "10.0.x.x"
username = "admin"
password = "password"
frameSkipped = 10 # Analyse a frame every X
frameScaling = 50 # Image resize ratio, in percentage
confidence = 0.6 # Consider object detected if confidence is more than this value
waitOnDetection = 30 # Seconds
#Active interval. NOTE: If start and stop are equal, means continuous operation
startTime = "21:00"
stopTime = "8:00"
###################################################
import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import cv2
import telegram_send
import time
import datetime
# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("..")
# ## Object detection imports
# Here are the imports from the object detection module.
from utils import label_map_util
from utils import visualization_utils as vis_util
# helper Code
ARBITRARY_DATE = datetime.datetime(1999, 9, 9)
def is_time_between(t, start, end):
if start == end:
return True
day_add = 1 if end < start else 0
end_add = 1 if day_add and end == datetime.time(0,0,0,0) else 0
test_add = 1 if day_add and t < start else 0
td_time_start = datetime.timedelta(hours=start.hour,
minutes=start.minute,
seconds=start.second,
microseconds=start.microsecond)
td_time_end = datetime.timedelta(days=day_add + end_add,
hours=end.hour,
minutes=end.minute,
seconds=end.second,
microseconds=end.microsecond)
td_testing = datetime.timedelta(days=test_add,
hours=t.hour,
minutes=t.minute,
seconds=t.second,
microseconds=t.microsecond)
start_date = ARBITRARY_DATE + td_time_start
end_date = ARBITRARY_DATE + td_time_end
testing_date = ARBITRARY_DATE + td_testing
return start_date <= testing_date and testing_date <= end_date
# # Model preparation
# What model to download.
MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017'
MODEL_FILE = MODEL_NAME + '.tar.gz'
DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'
# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'
# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')
NUM_CLASSES = 90
# ## Download Model
opener = urllib.request.URLopener()
opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)
tar_file = tarfile.open(MODEL_FILE)
for file in tar_file.getmembers():
file_name = os.path.basename(file.name)
if 'frozen_inference_graph.pb' in file_name:
tar_file.extract(file, os.getcwd())
# ## Load a (frozen) Tensorflow model into memory.
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.compat.v1.GraphDef()
with tf.io.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='')
# ## Loading label map
# Label maps map indices to category names, so that when our 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)
# Size, in inches, of the output images.
IMAGE_SIZE = (12, 8)
# RTSP URL construction (DAHUA CAMS)
streamURL = "rtsp://"+ username + ":" + password + "@" + IP + ":554/cam/realmonitor?channel=1&subtype=0"
# Open CAM stream
camStream = cv2.VideoCapture(streamURL)
if not camStream.isOpened():
print(str(datetime.datetime.now()) + " - " + IP + " - ERROR: Could not connect to CAM stream, aborting")
quit()
print(str(datetime.datetime.now()) + " - " + IP + " - Processing started")
with detection_graph.as_default():
with tf.compat.v1.Session(graph=detection_graph) as sess:
while True:
# Are we now in the defined time interval?
if (startTime != stopTime) and (not is_time_between(datetime.datetime.now().time(),
datetime.datetime.strptime(startTime, "%H:%M").time(),
datetime.datetime.strptime(stopTime, "%H:%M").time())):
# Not now, sleep a bit and check again
time.sleep(30)
# Good time, detect
else:
try:
# only process one in X frames
for i in range(1,frameSkipped):
# Capture frame
camStream.grab()
startCycle = int(round(time.time() * 1000))
# Get frame
success, frame = camStream.read()
# Check if we got a frame
if not success:
print(str(datetime.datetime.now()) + " - " + IP + " - ERROR: CAM did not return a valid frame, reconnecting")
raise
except:
print(str(datetime.datetime.now()) + " - " + IP + " - Error getting snapshot")
time.sleep(5)
# Empty framebuffer and reconnect
camStream.release()
camStream = cv2.VideoCapture(streamURL)
else:
# Scale frame if needed
if frameScaling < 100:
width = int(frame.shape[1] * frameScaling / 100)
height = int(frame.shape[0] * frameScaling / 100)
dim = (width, height)
# resize image
frame = cv2.resize(frame, dim, interpolation = cv2.INTER_AREA)
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
frame_expanded = np.expand_dims(frame, axis=0)
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.
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.
scores = detection_graph.get_tensor_by_name('detection_scores:0')
classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
# Actual detection.
(boxes, scores, classes, num_detections) = sess.run(
[boxes, scores, classes, num_detections],
feed_dict={image_tensor: frame_expanded})
# ONLY SHOW ONE CLASS (1 = people)
boxes = np.squeeze(boxes)
scores = np.squeeze(scores)
classes = np.squeeze(classes)
indices = ((classes == 1) & (scores > confidence)).nonzero()[0]
print("Frame processing Completed in " + str(int(round(time.time() * 1000)) - startCycle) + "ms")
# In case objects detected, send message
if len(indices) > 0:
print(str(datetime.datetime.now()) + " - " + IP + "- People detected: " + str(len(indices)))
boxes = boxes[indices]
scores = scores[indices]
classes = classes[indices]
# Visualization of the results of a detection
vis_util.visualize_boxes_and_labels_on_image_array(
frame,
boxes,
classes.astype(np.int32),
scores,
category_index,
use_normalized_coordinates=True,
line_thickness=3)
# Save image for telegram... TODO: is it necessary??
cv2.imwrite(IP + ".jpg", frame)
with open(IP + ".jpg", "rb") as f:
telegram_send.send(conf="/etc/telegram-send.conf", images=[f], captions=["ALERT: Intruder detected: " + str(len(indices))])
# Wait a bit, not to flood telegram
time.sleep(waitOnDetection)
# Empty framebuffer and reconnect
camStream.release()
camStream = cv2.VideoCapture(streamURL)