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Use cv2 to draw object boxes and labels instead of tensorflow object_detection.utils

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This facilitates removing tensorflow models and protobuf-python from the
docker image greatly reducing image build time and image size by ~1.3GB
This commit is contained in:
lkorth 2019-06-22 15:03:11 -04:00
parent 7f565333d9
commit 8ffdcc95c6
4 changed files with 65 additions and 77 deletions
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19
Dockerfile
View File

@ -50,21 +50,6 @@ RUN pip install -U pip \
paho-mqtt \
PyYAML
# Install tensorflow models object detection
RUN GIT_SSL_NO_VERIFY=true git clone -q https://github.com/tensorflow/models /usr/local/lib/python3.5/dist-packages/tensorflow/models
RUN wget -q -P /usr/local/src/ --no-check-certificate https://github.com/google/protobuf/releases/download/v3.5.1/protobuf-python-3.5.1.tar.gz
# Download & build protobuf-python
RUN cd /usr/local/src/ \
&& tar xf protobuf-python-3.5.1.tar.gz \
&& rm protobuf-python-3.5.1.tar.gz \
&& cd /usr/local/src/protobuf-3.5.1/ \
&& ./configure \
&& make \
&& make install \
&& ldconfig \
&& rm -rf /usr/local/src/protobuf-3.5.1/
# Download & build OpenCV
RUN wget -q -P /usr/local/src/ --no-check-certificate https://github.com/opencv/opencv/archive/4.0.1.zip
RUN cd /usr/local/src/ \
@ -97,10 +82,6 @@ RUN ln -s /python-tflite-source/edgetpu/test_data/coco_labels.txt /coco_labels.t
RUN (apt-get autoremove -y; \
apt-get autoclean -y)
# 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=.
WORKDIR /opt/frigate/
ADD frigate frigate/
COPY detect_objects.py .

35
frigate/objects.py
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@ -2,7 +2,7 @@ import time
import datetime
import threading
import cv2
from object_detection.utils import visualization_utils as vis_util
from . util import drawobjectbox
class ObjectCleaner(threading.Thread):
def __init__(self, objects_parsed, detected_objects):
@ -70,27 +70,24 @@ class BestPersonFrame(threading.Thread):
# if there is already a best_person
else:
now = datetime.datetime.now().timestamp()
# if the new best person is a higher score than the current best person
# if the new best person is a higher score than the current best person
# 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)
# draw the bounding box on the frame
vis_util.draw_bounding_box_on_image_array(best_frame,
self.best_person['ymin'],
self.best_person['xmin'],
self.best_person['ymax'],
self.best_person['xmax'],
color='red',
thickness=2,
display_str_list=["{}: {}%".format(self.best_person['name'],int(self.best_person['score']*100))],
use_normalized_coordinates=False)
# convert back to BGR
self.best_frame = cv2.cvtColor(best_frame, cv2.COLOR_RGB2BGR)
if not self.best_person is None and self.best_person['frame_time'] in recent_frames:
self.best_frame = recent_frames[self.best_person['frame_time']]
label = "{}: {}%".format(self.best_person['name'], int(self.best_person['score'] * 100))
bounding_box = (
self.best_person['xmin'],
self.best_person['ymin'],
self.best_person['xmax'],
self.best_person['ymax']
)
# draw the bounding box on the frame
drawobjectbox(self.best_frame, label, bounding_box)

22
frigate/util.py
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@ -1,5 +1,25 @@
import numpy as np
import cv2
LABEL_FONT = cv2.FONT_HERSHEY_PLAIN
FONT_SCALE = 0.8
# convert shared memory array into numpy array
def tonumpyarray(mp_arr):
return np.frombuffer(mp_arr.get_obj(), dtype=np.uint8)
return np.frombuffer(mp_arr.get_obj(), dtype=np.uint8)
# draw a box with text in the upper left on the image
def drawobjectbox(image, text, rect):
x1, y1, x2, y2 = rect
# draw the red bounding box
cv2.rectangle(image, (x1, y1), (x2, y2), (0, 0, 255), 2)
# get the size of the text
(text_width, text_height) = cv2.getTextSize(text, LABEL_FONT, FONT_SCALE, 1)[0]
# draw the text background with padding
cv2.rectangle(image, (x1, y1), (x1 + text_width + 8, y1 - text_height - 8), (0, 0, 255), cv2.FILLED)
# draw the text
cv2.putText(image, text, (x1 + 4, y1 - 4), LABEL_FONT, FONT_SCALE, (0, 0, 0), lineType=cv2.LINE_AA)

66
frigate/video.py
View File

@ -6,8 +6,8 @@ import threading
import ctypes
import multiprocessing as mp
import numpy as np
from object_detection.utils import visualization_utils as vis_util
from . util import tonumpyarray
from . util import drawobjectbox
from . object_detection import FramePrepper
from . objects import ObjectCleaner, BestPersonFrame
from . mqtt import MqttObjectPublisher
@ -54,10 +54,10 @@ def fetch_frames(shared_arr, shared_frame_time, frame_lock, frame_ready, frame_s
else:
print("Unable to grab a frame")
bad_frame_counter += 1
if bad_frame_counter > 100:
video.release()
video.release()
# Stores 2 seconds worth of frames when motion is detected so they can be used for other threads
@ -79,12 +79,12 @@ class FrameTracker(threading.Thread):
# if there isnt a frame ready for processing or it is old, wait for a signal
if self.frame_time.value == frame_time or (now - self.frame_time.value) > 0.5:
self.frame_ready.wait()
# lock and make a copy of the frame
with self.frame_lock:
with self.frame_lock:
frame = self.shared_frame.copy()
frame_time = self.frame_time.value
# add the frame to recent frames
self.recent_frames[frame_time] = frame
@ -106,7 +106,7 @@ def get_frame_shape(rtsp_url):
def get_rtsp_url(rtsp_config):
if (rtsp_config['password'].startswith('$')):
rtsp_config['password'] = os.getenv(rtsp_config['password'][1:])
return 'rtsp://{}:{}@{}:{}{}'.format(rtsp_config['user'],
return 'rtsp://{}:{}@{}:{}{}'.format(rtsp_config['user'],
rtsp_config['password'], rtsp_config['host'], rtsp_config['port'],
rtsp_config['path'])
@ -174,9 +174,9 @@ class Camera:
region['size'], region['x_offset'], region['y_offset'], region['threshold'],
prepped_frame_queue
))
# start a thread to store recent motion frames for processing
self.frame_tracker = FrameTracker(self.shared_frame_np, self.shared_frame_time,
self.frame_tracker = FrameTracker(self.shared_frame_np, self.shared_frame_time,
self.frame_ready, self.frame_lock, self.recent_frames)
self.frame_tracker.start()
@ -209,28 +209,28 @@ class Camera:
self.capture_process.terminate()
del self.capture_process
self.capture_process = None
# create the process to capture frames from the RTSP stream and store in a shared array
print("Creating a new capture process...")
self.capture_process = mp.Process(target=fetch_frames, args=(self.shared_frame_array,
self.capture_process = mp.Process(target=fetch_frames, args=(self.shared_frame_array,
self.shared_frame_time, self.frame_lock, self.frame_ready, self.frame_shape, self.rtsp_url))
self.capture_process.daemon = True
print("Starting a new capture process...")
self.capture_process.start()
def start(self):
self.start_or_restart_capture()
# start the object detection prep threads
for detection_prep_thread in self.detection_prep_threads:
detection_prep_thread.start()
self.watchdog.start()
def join(self):
self.capture_process.join()
def get_capture_pid(self):
return self.capture_process.pid
def add_objects(self, objects):
if len(objects) == 0:
return
@ -246,15 +246,15 @@ class Camera:
obj['ymin'] >= r['y_offset'] and
obj['xmax'] <= r['x_offset']+r['size'] and
obj['ymax'] <= r['y_offset']+r['size']
):
):
region = r
break
# if the min person area is larger than the
# detected person, don't add it to detected objects
if region and region['min_person_area'] > person_area:
continue
# compute the coordinates of the person and make sure
# the location isnt outide the bounds of the image (can happen from rounding)
y_location = min(int(obj['ymax']), len(self.mask)-1)
@ -271,7 +271,7 @@ class Camera:
def get_best_person(self):
return self.best_person_frame.best_frame
def get_current_frame_with_objects(self):
# make a copy of the current detected objects
detected_objects = self.detected_objects.copy()
@ -279,31 +279,21 @@ class Camera:
with self.frame_lock:
frame = self.shared_frame_np.copy()
# convert to RGB for drawing
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# draw the bounding boxes on the screen
for obj in detected_objects:
vis_util.draw_bounding_box_on_image_array(frame,
obj['ymin'],
label = "{}: {}%".format(obj['name'], int(obj['score'] * 100))
bounding_box = (
obj['xmin'],
obj['ymax'],
obj['ymin'],
obj['xmax'],
color='red',
thickness=2,
display_str_list=["{}: {}%".format(obj['name'],int(obj['score']*100))],
use_normalized_coordinates=False)
obj['ymax']
)
drawobjectbox(frame, label, bounding_box)
for region in self.regions:
color = (255,255,255)
cv2.rectangle(frame, (region['x_offset'], region['y_offset']),
(region['x_offset']+region['size'], region['y_offset']+region['size']),
cv2.rectangle(frame, (region['x_offset'], region['y_offset']),
(region['x_offset']+region['size'], region['y_offset']+region['size']),
color, 2)
# convert back to BGR
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
return frame