mirror of
https://github.com/blakeblackshear/frigate.git
synced 2025-10-27 10:52:11 +01:00
cleanup and update readme
This commit is contained in:
blakeblackshear
parent
4476bd8a13
commit
e0b9b616ce
@ -92,6 +92,10 @@ RUN tar xzf edgetpu_api.tar.gz \
|
||||
RUN (apt-get autoremove -y; \
|
||||
apt-get autoclean -y)
|
||||
|
||||
# symlink the model and labels
|
||||
RUN ln -s /python-tflite-source/edgetpu/test_data/mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite /frozen_inference_graph.pb
|
||||
RUN ln -s /python-tflite-source/edgetpu/test_data/coco_labels.txt /label_map.pbtext
|
||||
|
||||
# Set TF object detection available
|
||||
ENV PYTHONPATH "$PYTHONPATH:/usr/local/lib/python3.5/dist-packages/tensorflow/models/research:/usr/local/lib/python3.5/dist-packages/tensorflow/models/research/slim"
|
||||
RUN cd /usr/local/lib/python3.5/dist-packages/tensorflow/models/research && protoc object_detection/protos/*.proto --python_out=.
|
||||
@ -101,6 +105,3 @@ ADD frigate frigate/
|
||||
COPY detect_objects.py .
|
||||
|
||||
CMD ["python3", "-u", "detect_objects.py"]
|
||||
|
||||
# WORKDIR /python-tflite-source/edgetpu/
|
||||
# CMD ["python3", "-u", "demo/classify_image.py", "--model", "test_data/mobilenet_v2_1.0_224_inat_bird_quant_edgetpu.tflite", "--label", "test_data/inat_bird_labels.txt", "--image", "test_data/parrot.jpg"]
|
||||
114
README.md
114
README.md
@ -1,18 +1,18 @@
|
||||
# Frigate - Realtime Object Detection for RTSP Cameras
|
||||
**Note:** This version requires the use of a [Google Coral USB Accelerator](https://coral.withgoogle.com/products/accelerator/)
|
||||
|
||||
Uses OpenCV and Tensorflow to perform realtime object detection locally for RTSP cameras. Designed for integration with HomeAssistant or others via MQTT.
|
||||
|
||||
- Leverages multiprocessing and threads heavily with an emphasis on realtime over processing every frame
|
||||
- Allows you to define specific regions (squares) in the image to look for motion/objects
|
||||
- Motion detection runs in a separate process per region and signals to object detection to avoid wasting CPU cycles looking for objects when there is no motion
|
||||
- Object detection with Tensorflow runs in a separate process per region
|
||||
- Detected objects are placed on a shared mp.Queue and aggregated into a list of recently detected objects in a separate thread
|
||||
- A person score is calculated as the sum of all scores/5
|
||||
- Motion and object info is published over MQTT for integration into HomeAssistant or others
|
||||
- Allows you to define specific regions (squares) in the image to look for objects
|
||||
- No motion detection (for now)
|
||||
- Object detection with Tensorflow runs in a separate thread
|
||||
- Object info is published over MQTT for integration into HomeAssistant as a binary sensor
|
||||
- An endpoint is available to view an MJPEG stream for debugging
|
||||
|
||||
|
||||
|
||||
## Example video
|
||||
## Example video (from older version)
|
||||
You see multiple bounding boxes because it draws bounding boxes from all frames in the past 1 second where a person was detected. Not all of the bounding boxes were from the current frame.
|
||||
[](http://www.youtube.com/watch?v=nqHbCtyo4dY "Frigate")
|
||||
|
||||
@ -22,24 +22,16 @@ Build the container with
|
||||
docker build -t frigate .
|
||||
```
|
||||
|
||||
Download a model from the [zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md).
|
||||
|
||||
Download the cooresponding label map from [here](https://github.com/tensorflow/models/tree/master/research/object_detection/data).
|
||||
The `mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite` model is included and used by default. You can use your own model and labels by mounting files in the container at `/frozen_inference_graph.pb` and `/label_map.pbtext`. Models must be compatible with the Coral according to [this](https://coral.withgoogle.com/models/).
|
||||
|
||||
Run the container with
|
||||
```
|
||||
docker run --rm \
|
||||
-v <path_to_frozen_detection_graph.pb>:/frozen_inference_graph.pb:ro \
|
||||
-v <path_to_labelmap.pbtext>:/label_map.pbtext:ro \
|
||||
--privileged \
|
||||
-v /dev/bus/usb:/dev/bus/usb \
|
||||
-v <path_to_config_dir>:/config:ro \
|
||||
-p 5000:5000 \
|
||||
-e RTSP_URL='<rtsp_url>' \
|
||||
-e REGIONS='<box_size_1>,<x_offset_1>,<y_offset_1>,<min_person_size_1>,<min_motion_size_1>,<mask_file_1>:<box_size_2>,<x_offset_2>,<y_offset_2>,<min_person_size_2>,<min_motion_size_2>,<mask_file_2>' \
|
||||
-e MQTT_HOST='your.mqtthost.com' \
|
||||
-e MQTT_USER='username' \
|
||||
-e MQTT_PASS='password' \
|
||||
-e MQTT_TOPIC_PREFIX='cameras/1' \
|
||||
-e DEBUG='0' \
|
||||
-e RTSP_PASSWORD='password' \
|
||||
frigate:latest
|
||||
```
|
||||
|
||||
@ -48,107 +40,59 @@ Example docker-compose:
|
||||
frigate:
|
||||
container_name: frigate
|
||||
restart: unless-stopped
|
||||
privileged: true
|
||||
image: frigate:latest
|
||||
volumes:
|
||||
- <path_to_frozen_detection_graph.pb>:/frozen_inference_graph.pb:ro
|
||||
- <path_to_labelmap.pbtext>:/label_map.pbtext:ro
|
||||
- /dev/bus/usb:/dev/bus/usb
|
||||
- <path_to_config>:/config
|
||||
ports:
|
||||
- "127.0.0.1:5000:5000"
|
||||
- "5000:5000"
|
||||
environment:
|
||||
RTSP_URL: "<rtsp_url>"
|
||||
REGIONS: "<box_size_1>,<x_offset_1>,<y_offset_1>,<min_person_size_1>,<min_motion_size_1>,<mask_file_1>:<box_size_2>,<x_offset_2>,<y_offset_2>,<min_person_size_2>,<min_motion_size_2>,<mask_file_2>"
|
||||
MQTT_HOST: "your.mqtthost.com"
|
||||
MQTT_USER: "username" #optional
|
||||
MQTT_PASS: "password" #optional
|
||||
MQTT_TOPIC_PREFIX: "cameras/1"
|
||||
DEBUG: "0"
|
||||
RTSP_PASSWORD: "password"
|
||||
```
|
||||
|
||||
Here is an example `REGIONS` env variable:
|
||||
`350,0,300,5000,200,mask-0-300.bmp:400,350,250,2000,200,mask-350-250.bmp:400,750,250,2000,200,mask-750-250.bmp`
|
||||
A `config.yml` file must exist in the `config` directory. See example [here](config/config.yml).
|
||||
|
||||
First region broken down (all are required):
|
||||
- `350` - size of the square (350px by 350px)
|
||||
- `0` - x coordinate of upper left corner (top left of image is 0,0)
|
||||
- `300` - y coordinate of upper left corner (top left of image is 0,0)
|
||||
- `5000` - minimum person bounding box size (width*height for bounding box of identified person)
|
||||
- `200` - minimum number of changed pixels to trigger motion
|
||||
- `mask-0-300.bmp` - a bmp file with the masked regions as pure black, must be the same size as the region
|
||||
|
||||
Mask files go in the `/config` directory.
|
||||
|
||||
Access the mjpeg stream at http://localhost:5000
|
||||
Access the mjpeg stream at `http://localhost:5000/<camera_name>` and the best person snapshot at `http://localhost:5000/<camera_name>/best_person.jpg`
|
||||
|
||||
## Integration with HomeAssistant
|
||||
```
|
||||
camera:
|
||||
- name: Camera Last Person
|
||||
platform: generic
|
||||
still_image_url: http://<ip>:5000/best_person.jpg
|
||||
|
||||
binary_sensor:
|
||||
- name: Camera Motion
|
||||
platform: mqtt
|
||||
state_topic: "cameras/1/motion"
|
||||
device_class: motion
|
||||
availability_topic: "cameras/1/available"
|
||||
still_image_url: http://<ip>:5000/<camera_name>/best_person.jpg
|
||||
|
||||
sensor:
|
||||
- name: Camera Person Score
|
||||
- name: Camera Person
|
||||
platform: mqtt
|
||||
state_topic: "cameras/1/objects"
|
||||
state_topic: "frigate/<camera_name>/objects"
|
||||
value_template: '{{ value_json.person }}'
|
||||
unit_of_measurement: '%'
|
||||
availability_topic: "cameras/1/available"
|
||||
device_class: moving
|
||||
availability_topic: "frigate/available"
|
||||
```
|
||||
|
||||
## Tips
|
||||
- Lower the framerate of the RTSP feed on the camera to reduce the CPU usage for capturing the feed
|
||||
- Use SSDLite models to reduce CPU usage
|
||||
|
||||
## Future improvements
|
||||
- [x] Remove motion detection for now
|
||||
- [ ] Try running object detection in a thread rather than a process
|
||||
- [x] Try running object detection in a thread rather than a process
|
||||
- [x] Implement min person size again
|
||||
- [ ] Switch to a config file
|
||||
- [ ] Handle multiple cameras in the same container
|
||||
- [ ] Simplify motion detection (check entire image against mask)
|
||||
- [x] Switch to a config file
|
||||
- [x] Handle multiple cameras in the same container
|
||||
- [ ] Attempt to figure out coral symlinking
|
||||
- [ ] Add object list to config with min scores for mqtt
|
||||
- [ ] Move mjpeg encoding to a separate process
|
||||
- [ ] Simplify motion detection (check entire image against mask, resize instead of gaussian blur)
|
||||
- [ ] See if motion detection is even worth running
|
||||
- [ ] Scan for people across entire image rather than specfic regions
|
||||
- [ ] Dynamically resize detection area and follow people
|
||||
- [ ] Add ability to turn detection on and off via MQTT
|
||||
- [ ] MQTT motion occasionally gets stuck ON
|
||||
- [ ] Output movie clips of people for notifications, etc.
|
||||
- [ ] Integrate with homeassistant push camera
|
||||
- [ ] Merge bounding boxes that span multiple regions
|
||||
- [ ] Allow motion regions to be different than object detection regions
|
||||
- [ ] Implement mode to save labeled objects for training
|
||||
- [ ] Try and reduce CPU usage by simplifying the tensorflow model to just include the objects we care about
|
||||
- [ ] Look into GPU accelerated decoding of RTSP stream
|
||||
- [ ] Send video over a socket and use JSMPEG
|
||||
- [x] Look into neural compute stick
|
||||
|
||||
## Building Tensorflow from source for CPU optimizations
|
||||
https://www.tensorflow.org/install/source#docker_linux_builds
|
||||
used `tensorflow/tensorflow:1.12.0-devel-py3`
|
||||
|
||||
## Optimizing the graph (cant say I saw much difference in CPU usage)
|
||||
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/graph_transforms/README.md#optimizing-for-deployment
|
||||
```
|
||||
docker run -it -v ${PWD}:/lab -v ${PWD}/../back_camera_model/models/ssd_mobilenet_v2_coco_2018_03_29/frozen_inference_graph.pb:/frozen_inference_graph.pb:ro tensorflow/tensorflow:1.12.0-devel-py3 bash
|
||||
|
||||
bazel build tensorflow/tools/graph_transforms:transform_graph
|
||||
|
||||
bazel-bin/tensorflow/tools/graph_transforms/transform_graph \
|
||||
--in_graph=/frozen_inference_graph.pb \
|
||||
--out_graph=/lab/optimized_inception_graph.pb \
|
||||
--inputs='image_tensor' \
|
||||
--outputs='num_detections,detection_scores,detection_boxes,detection_classes' \
|
||||
--transforms='
|
||||
strip_unused_nodes(type=float, shape="1,300,300,3")
|
||||
remove_nodes(op=Identity, op=CheckNumerics)
|
||||
fold_constants(ignore_errors=true)
|
||||
fold_batch_norms
|
||||
fold_old_batch_norms'
|
||||
```
|
||||
@ -1,8 +1,8 @@
|
||||
web_port: 5000
|
||||
|
||||
mqtt:
|
||||
host: mqtt.blakeshome.com
|
||||
topic_prefix: cameras
|
||||
host: mqtt.server.com
|
||||
topic_prefix: frigate
|
||||
|
||||
cameras:
|
||||
back:
|
||||
@ -10,18 +10,40 @@ cameras:
|
||||
user: viewer
|
||||
host: 10.0.10.10
|
||||
port: 554
|
||||
# values that begin with a "$" will be replaced with environment variable
|
||||
password: $RTSP_PASSWORD
|
||||
path: /cam/realmonitor?channel=1&subtype=2
|
||||
regions:
|
||||
- size: 350
|
||||
x_offset: 0
|
||||
y_offset: 300
|
||||
min_person_size: 5000
|
||||
min_person_area: 5000
|
||||
- size: 400
|
||||
x_offset: 350
|
||||
y_offset: 250
|
||||
min_person_size: 2000
|
||||
min_person_area: 2000
|
||||
- size: 400
|
||||
x_offset: 750
|
||||
y_offset: 250
|
||||
min_person_size: 2000
|
||||
min_person_area: 2000
|
||||
back2:
|
||||
rtsp:
|
||||
user: viewer
|
||||
host: 10.0.10.10
|
||||
port: 554
|
||||
# values that begin with a "$" will be replaced with environment variable
|
||||
password: $RTSP_PASSWORD
|
||||
path: /cam/realmonitor?channel=1&subtype=2
|
||||
regions:
|
||||
- size: 350
|
||||
x_offset: 0
|
||||
y_offset: 300
|
||||
min_person_area: 5000
|
||||
- size: 400
|
||||
x_offset: 350
|
||||
y_offset: 250
|
||||
min_person_area: 2000
|
||||
- size: 400
|
||||
x_offset: 750
|
||||
y_offset: 250
|
||||
min_person_area: 2000
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 239 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 313 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 313 KiB |
@ -1,30 +1,15 @@
|
||||
import os
|
||||
import cv2
|
||||
import imutils
|
||||
import time
|
||||
import datetime
|
||||
import ctypes
|
||||
import logging
|
||||
import multiprocessing as mp
|
||||
import queue
|
||||
import threading
|
||||
import json
|
||||
import yaml
|
||||
from contextlib import closing
|
||||
import numpy as np
|
||||
from object_detection.utils import visualization_utils as vis_util
|
||||
from flask import Flask, Response, make_response, send_file
|
||||
from flask import Flask, Response, make_response
|
||||
import paho.mqtt.client as mqtt
|
||||
|
||||
from frigate.util import tonumpyarray
|
||||
from frigate.mqtt import MqttMotionPublisher, MqttObjectPublisher
|
||||
from frigate.objects import ObjectParser, ObjectCleaner, BestPersonFrame
|
||||
from frigate.motion import detect_motion
|
||||
from frigate.video import fetch_frames, FrameTracker, Camera
|
||||
from frigate.object_detection import FramePrepper, PreppedQueueProcessor
|
||||
from frigate.video import Camera
|
||||
from frigate.object_detection import PreppedQueueProcessor
|
||||
|
||||
with open('/config/config.yml') as f:
|
||||
# use safe_load instead load
|
||||
CONFIG = yaml.safe_load(f)
|
||||
|
||||
MQTT_HOST = CONFIG['mqtt']['host']
|
||||
@ -50,9 +35,9 @@ def main():
|
||||
client.connect(MQTT_HOST, MQTT_PORT, 60)
|
||||
client.loop_start()
|
||||
|
||||
# Queue for prepped frames
|
||||
# TODO: set length to 1.5x the number of total regions
|
||||
prepped_frame_queue = queue.Queue(6)
|
||||
# Queue for prepped frames, max size set to (number of cameras * 5)
|
||||
max_queue_size = len(CONFIG['cameras'].items())*5
|
||||
prepped_frame_queue = queue.Queue(max_queue_size)
|
||||
|
||||
cameras = {}
|
||||
for name, config in CONFIG['cameras'].items():
|
||||
|
||||
@ -1,116 +0,0 @@
|
||||
import datetime
|
||||
import numpy as np
|
||||
import cv2
|
||||
import imutils
|
||||
from . util import tonumpyarray
|
||||
|
||||
# do the actual motion detection
|
||||
def detect_motion(shared_arr, shared_frame_time, frame_lock, frame_ready, motion_detected, motion_changed,
|
||||
frame_shape, region_size, region_x_offset, region_y_offset, min_motion_area, mask, debug):
|
||||
# shape shared input array into frame for processing
|
||||
arr = tonumpyarray(shared_arr).reshape(frame_shape)
|
||||
|
||||
avg_frame = None
|
||||
avg_delta = None
|
||||
last_motion = -1
|
||||
frame_time = 0.0
|
||||
motion_frames = 0
|
||||
while True:
|
||||
now = datetime.datetime.now().timestamp()
|
||||
|
||||
# if it has been long enough since the last motion, clear the flag
|
||||
if last_motion > 0 and (now - last_motion) > 5:
|
||||
last_motion = -1
|
||||
if motion_detected.is_set():
|
||||
motion_detected.clear()
|
||||
with motion_changed:
|
||||
motion_changed.notify_all()
|
||||
|
||||
|
||||
with frame_ready:
|
||||
# if there isnt a frame ready for processing or it is old, wait for a signal
|
||||
if shared_frame_time.value == frame_time or (now - shared_frame_time.value) > 0.5:
|
||||
frame_ready.wait()
|
||||
|
||||
# lock and make a copy of the cropped frame
|
||||
with frame_lock:
|
||||
cropped_frame = arr[region_y_offset:region_y_offset+region_size, region_x_offset:region_x_offset+region_size].copy()
|
||||
frame_time = shared_frame_time.value
|
||||
|
||||
# convert to grayscale
|
||||
gray = cv2.cvtColor(cropped_frame, cv2.COLOR_BGR2GRAY)
|
||||
|
||||
# apply image mask to remove areas from motion detection
|
||||
gray[mask] = [255]
|
||||
|
||||
# apply gaussian blur
|
||||
gray = cv2.GaussianBlur(gray, (21, 21), 0)
|
||||
|
||||
if avg_frame is None:
|
||||
avg_frame = gray.copy().astype("float")
|
||||
continue
|
||||
|
||||
# look at the delta from the avg_frame
|
||||
frameDelta = cv2.absdiff(gray, cv2.convertScaleAbs(avg_frame))
|
||||
|
||||
if avg_delta is None:
|
||||
avg_delta = frameDelta.copy().astype("float")
|
||||
|
||||
# compute the average delta over the past few frames
|
||||
# the alpha value can be modified to configure how sensitive the motion detection is.
|
||||
# higher values mean the current frame impacts the delta a lot, and a single raindrop may
|
||||
# register as motion, too low and a fast moving person wont be detected as motion
|
||||
# this also assumes that a person is in the same location across more than a single frame
|
||||
cv2.accumulateWeighted(frameDelta, avg_delta, 0.2)
|
||||
|
||||
# compute the threshold image for the current frame
|
||||
current_thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]
|
||||
|
||||
# black out everything in the avg_delta where there isnt motion in the current frame
|
||||
avg_delta_image = cv2.convertScaleAbs(avg_delta)
|
||||
avg_delta_image[np.where(current_thresh==[0])] = [0]
|
||||
|
||||
# then look for deltas above the threshold, but only in areas where there is a delta
|
||||
# in the current frame. this prevents deltas from previous frames from being included
|
||||
thresh = cv2.threshold(avg_delta_image, 25, 255, cv2.THRESH_BINARY)[1]
|
||||
|
||||
# dilate the thresholded image to fill in holes, then find contours
|
||||
# on thresholded image
|
||||
thresh = cv2.dilate(thresh, None, iterations=2)
|
||||
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
|
||||
cnts = imutils.grab_contours(cnts)
|
||||
|
||||
motion_found = False
|
||||
|
||||
# loop over the contours
|
||||
for c in cnts:
|
||||
# if the contour is big enough, count it as motion
|
||||
contour_area = cv2.contourArea(c)
|
||||
if contour_area > min_motion_area:
|
||||
motion_found = True
|
||||
if debug:
|
||||
cv2.drawContours(cropped_frame, [c], -1, (0, 255, 0), 2)
|
||||
x, y, w, h = cv2.boundingRect(c)
|
||||
cv2.putText(cropped_frame, str(contour_area), (x, y),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 100, 0), 2)
|
||||
else:
|
||||
break
|
||||
|
||||
if motion_found:
|
||||
motion_frames += 1
|
||||
# if there have been enough consecutive motion frames, report motion
|
||||
if motion_frames >= 3:
|
||||
# only average in the current frame if the difference persists for at least 3 frames
|
||||
cv2.accumulateWeighted(gray, avg_frame, 0.01)
|
||||
motion_detected.set()
|
||||
with motion_changed:
|
||||
motion_changed.notify_all()
|
||||
last_motion = now
|
||||
else:
|
||||
# when no motion, just keep averaging the frames together
|
||||
cv2.accumulateWeighted(gray, avg_frame, 0.01)
|
||||
motion_frames = 0
|
||||
|
||||
if debug and motion_frames == 3:
|
||||
cv2.imwrite("/lab/debug/motion-{}-{}-{}.jpg".format(region_x_offset, region_y_offset, datetime.datetime.now().timestamp()), cropped_frame)
|
||||
cv2.imwrite("/lab/debug/avg_delta-{}-{}-{}.jpg".format(region_x_offset, region_y_offset, datetime.datetime.now().timestamp()), avg_delta_image)
|
||||
@ -1,29 +1,6 @@
|
||||
import json
|
||||
import threading
|
||||
|
||||
class MqttMotionPublisher(threading.Thread):
|
||||
def __init__(self, client, topic_prefix, motion_changed, motion_flags):
|
||||
threading.Thread.__init__(self)
|
||||
self.client = client
|
||||
self.topic_prefix = topic_prefix
|
||||
self.motion_changed = motion_changed
|
||||
self.motion_flags = motion_flags
|
||||
|
||||
def run(self):
|
||||
last_sent_motion = ""
|
||||
while True:
|
||||
with self.motion_changed:
|
||||
self.motion_changed.wait()
|
||||
|
||||
# send message for motion
|
||||
motion_status = 'OFF'
|
||||
if any(obj.is_set() for obj in self.motion_flags):
|
||||
motion_status = 'ON'
|
||||
|
||||
if last_sent_motion != motion_status:
|
||||
last_sent_motion = motion_status
|
||||
self.client.publish(self.topic_prefix+'/motion', motion_status, retain=False)
|
||||
|
||||
class MqttObjectPublisher(threading.Thread):
|
||||
def __init__(self, client, topic_prefix, objects_parsed, detected_objects):
|
||||
threading.Thread.__init__(self)
|
||||
|
||||
@ -36,13 +36,10 @@ class PreppedQueueProcessor(threading.Thread):
|
||||
# process queue...
|
||||
while True:
|
||||
frame = self.prepped_frame_queue.get()
|
||||
# print(self.prepped_frame_queue.qsize())
|
||||
|
||||
# Actual detection.
|
||||
objects = self.engine.DetectWithInputTensor(frame['frame'], threshold=0.5, top_k=3)
|
||||
# time.sleep(0.1)
|
||||
# objects = []
|
||||
# print(self.engine.get_inference_time())
|
||||
# put detected objects in the queue
|
||||
# parse and pass detected objects back to the camera
|
||||
parsed_objects = []
|
||||
for obj in objects:
|
||||
box = obj.bounding_box.flatten().tolist()
|
||||
@ -99,7 +96,6 @@ class FramePrepper(threading.Thread):
|
||||
# Expand dimensions since the model expects images to have shape: [1, 300, 300, 3]
|
||||
frame_expanded = np.expand_dims(cropped_frame_rgb, axis=0)
|
||||
|
||||
# print("Prepped frame at " + str(self.region_x_offset) + "," + str(self.region_y_offset))
|
||||
# add the frame to the queue
|
||||
if not self.prepped_frame_queue.full():
|
||||
self.prepped_frame_queue.put({
|
||||
|
||||
@ -3,18 +3,6 @@ import datetime
|
||||
import threading
|
||||
import cv2
|
||||
from object_detection.utils import visualization_utils as vis_util
|
||||
class ObjectParser(threading.Thread):
|
||||
def __init__(self, cameras, object_queue, detected_objects, regions):
|
||||
threading.Thread.__init__(self)
|
||||
self.cameras = cameras
|
||||
self.object_queue = object_queue
|
||||
self.regions = regions
|
||||
|
||||
def run(self):
|
||||
# frame_times = {}
|
||||
while True:
|
||||
obj = self.object_queue.get()
|
||||
self.cameras[obj['camera_name']].add_object(obj)
|
||||
|
||||
class ObjectCleaner(threading.Thread):
|
||||
def __init__(self, objects_parsed, detected_objects):
|
||||
@ -34,7 +22,6 @@ class ObjectCleaner(threading.Thread):
|
||||
# (newest objects are appended to the end)
|
||||
detected_objects = self._detected_objects.copy()
|
||||
|
||||
#print([round(now-obj['frame_time'],2) for obj in detected_objects])
|
||||
num_to_delete = 0
|
||||
for obj in detected_objects:
|
||||
if now-obj['frame_time']<2:
|
||||
@ -69,8 +56,6 @@ class BestPersonFrame(threading.Thread):
|
||||
# make a copy of detected objects
|
||||
detected_objects = self.detected_objects.copy()
|
||||
detected_people = [obj for obj in detected_objects if obj['name'] == 'person']
|
||||
# make a copy of the recent frames
|
||||
recent_frames = self.recent_frames.copy()
|
||||
|
||||
# get the highest scoring person
|
||||
new_best_person = max(detected_people, key=lambda x:x['score'], default=self.best_person)
|
||||
@ -89,7 +74,10 @@ class BestPersonFrame(threading.Thread):
|
||||
# or the current person is more than 1 minute old, use the new best person
|
||||
if new_best_person['score'] > self.best_person['score'] or (now - self.best_person['frame_time']) > 60:
|
||||
self.best_person = new_best_person
|
||||
|
||||
|
||||
# make a copy of the recent frames
|
||||
recent_frames = self.recent_frames.copy()
|
||||
|
||||
if not self.best_person is None and self.best_person['frame_time'] in recent_frames:
|
||||
best_frame = recent_frames[self.best_person['frame_time']]
|
||||
best_frame = cv2.cvtColor(best_frame, cv2.COLOR_BGR2RGB)
|
||||
|
||||
@ -8,11 +8,10 @@ import multiprocessing as mp
|
||||
from object_detection.utils import visualization_utils as vis_util
|
||||
from . util import tonumpyarray
|
||||
from . object_detection import FramePrepper
|
||||
from . objects import ObjectCleaner, ObjectParser, BestPersonFrame
|
||||
from . objects import ObjectCleaner, BestPersonFrame
|
||||
from . mqtt import MqttObjectPublisher
|
||||
|
||||
# fetch the frames as fast a possible, only decoding the frames when the
|
||||
# detection_process has consumed the current frame
|
||||
# fetch the frames as fast a possible and store current frame in a shared memory array
|
||||
def fetch_frames(shared_arr, shared_frame_time, frame_lock, frame_ready, frame_shape, rtsp_url):
|
||||
# convert shared memory array into numpy and shape into image array
|
||||
arr = tonumpyarray(shared_arr).reshape(frame_shape)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user