Mirrors/blakeblackshear.frigate
1
0
Fork 0
mirror of https://github.com/blakeblackshear/frigate.git synced 2024-11-21 19:07:46 +01:00
Code Issues Projects Releases Wiki Activity

rework object detection to watch the motion flag

Browse Source
This commit is contained in:
blakeblackshear 2019-02-09 09:15:55 -06:00
parent 5d894f006a
commit 53c9a7368d
1 changed files with 59 additions and 58 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

117
detect_objects.py
View File

@ -25,7 +25,7 @@ PATH_TO_LABELS = '/label_map.pbtext'
# TODO: make dynamic? # TODO: make dynamic?
NUM_CLASSES = 90 NUM_CLASSES = 90
REGIONS = "300,0,0:300,300,0:300,600,0" REGIONS = "350,0,300:400,350,250:400,750,250"
#REGIONS = os.getenv('REGIONS') #REGIONS = os.getenv('REGIONS')
DETECTED_OBJECTS = [] DETECTED_OBJECTS = []
@ -123,8 +123,11 @@ def main():
shared_memory_objects.append({ shared_memory_objects.append({
# create shared value for storing the time the frame was captured # create shared value for storing the time the frame was captured
'frame_time': mp.Value('d', 0.0), 'frame_time': mp.Value('d', 0.0),
# shared value for signaling to the capture process that we are ready for the next frame
# (1 for ready 0 for not ready)
'ready_for_frame': mp.Value('i', 1),
# shared value for motion detection signal (1 for motion 0 for no motion) # shared value for motion detection signal (1 for motion 0 for no motion)
'motion_detected': mp.Value('i', 1), 'motion_detected': mp.Value('i', 0),
# create shared array for storing 10 detected objects # create shared array for storing 10 detected objects
# note: this must be a double even though the value you are storing # note: this must be a double even though the value you are storing
# is a float. otherwise it stops updating the value in shared # is a float. otherwise it stops updating the value in shared
@ -164,66 +167,66 @@ def main():
motion_processes.append(motion_process) motion_processes.append(motion_process)
object_parser = ObjectParser([obj['output_array'] for obj in shared_memory_objects]) object_parser = ObjectParser([obj['output_array'] for obj in shared_memory_objects])
# object_parser.start() object_parser.start()
capture_process.start() capture_process.start()
print("capture_process pid ", capture_process.pid) print("capture_process pid ", capture_process.pid)
# for detection_process in detection_processes: for detection_process in detection_processes:
# detection_process.start() detection_process.start()
# print("detection_process pid ", detection_process.pid) print("detection_process pid ", detection_process.pid)
for motion_process in motion_processes: for motion_process in motion_processes:
motion_process.start() motion_process.start()
print("motion_process pid ", motion_process.pid) print("motion_process pid ", motion_process.pid)
# app = Flask(__name__) app = Flask(__name__)
# @app.route('/') @app.route('/')
# def index(): def index():
# # return a multipart response # return a multipart response
# return Response(imagestream(), return Response(imagestream(),
# mimetype='multipart/x-mixed-replace; boundary=frame') mimetype='multipart/x-mixed-replace; boundary=frame')
# def imagestream(): def imagestream():
# global DETECTED_OBJECTS global DETECTED_OBJECTS
# while True: while True:
# # max out at 5 FPS # max out at 5 FPS
# time.sleep(0.2) time.sleep(0.2)
# # make a copy of the current detected objects # make a copy of the current detected objects
# detected_objects = DETECTED_OBJECTS.copy() detected_objects = DETECTED_OBJECTS.copy()
# # make a copy of the current frame # make a copy of the current frame
# frame = frame_arr.copy() frame = frame_arr.copy()
# # convert to RGB for drawing # convert to RGB for drawing
# frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# # draw the bounding boxes on the screen # draw the bounding boxes on the screen
# for obj in DETECTED_OBJECTS: for obj in DETECTED_OBJECTS:
# vis_util.draw_bounding_box_on_image_array(frame, vis_util.draw_bounding_box_on_image_array(frame,
# obj['ymin'], obj['ymin'],
# obj['xmin'], obj['xmin'],
# obj['ymax'], obj['ymax'],
# obj['xmax'], obj['xmax'],
# color='red', color='red',
# thickness=2, thickness=2,
# display_str_list=["{}: {}%".format(obj['name'],int(obj['score']*100))], display_str_list=["{}: {}%".format(obj['name'],int(obj['score']*100))],
# use_normalized_coordinates=False) use_normalized_coordinates=False)
# for region in regions: for region in regions:
# cv2.rectangle(frame, (region['x_offset'], region['y_offset']), cv2.rectangle(frame, (region['x_offset'], region['y_offset']),
# (region['x_offset']+region['size'], region['y_offset']+region['size']), (region['x_offset']+region['size'], region['y_offset']+region['size']),
# (255,255,255), 2) (255,255,255), 2)
# # convert back to BGR # convert back to BGR
# frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR) frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
# # encode the image into a jpg # encode the image into a jpg
# ret, jpg = cv2.imencode('.jpg', frame) ret, jpg = cv2.imencode('.jpg', frame)
# yield (b'--frame\r\n' yield (b'--frame\r\n'
# b'Content-Type: image/jpeg\r\n\r\n' + jpg.tobytes() + b'\r\n\r\n') b'Content-Type: image/jpeg\r\n\r\n' + jpg.tobytes() + b'\r\n\r\n')
# app.run(host='0.0.0.0', debug=False) app.run(host='0.0.0.0', debug=False)
capture_process.join() capture_process.join()
# for detection_process in detection_processes: for detection_process in detection_processes:
# detection_process.join() detection_process.join()
for motion_process in motion_processes: for motion_process in motion_processes:
motion_process.join() motion_process.join()
# object_parser.join() object_parser.join()
# convert shared memory array into numpy array # convert shared memory array into numpy array
def tonumpyarray(mp_arr): def tonumpyarray(mp_arr):
@ -278,20 +281,22 @@ def process_frames(shared_arr, shared_output_arr, shared_frame_time, shared_moti
sess = tf.Session(graph=detection_graph) sess = tf.Session(graph=detection_graph)
no_frames_available = -1 no_frames_available = -1
frame_time = 0.0
while True: while True:
now = datetime.datetime.now().timestamp()
# if there is no motion detected # if there is no motion detected
if shared_motion.value == 0: if shared_motion.value == 0:
time.sleep(0.01) time.sleep(0.01)
continue continue
# if there isnt a frame ready for processing # if there isnt a new frame ready for processing
if shared_frame_time.value == 0.0: if shared_frame_time.value == frame_time:
# save the first time there were no frames available # save the first time there were no frames available
if no_frames_available == -1: if no_frames_available == -1:
no_frames_available = datetime.datetime.now().timestamp() no_frames_available = now
# if there havent been any frames available in 30 seconds, # if there havent been any frames available in 30 seconds,
# sleep to avoid using so much cpu if the camera feed is down # sleep to avoid using so much cpu if the camera feed is down
if no_frames_available > 0 and (datetime.datetime.now().timestamp() - no_frames_available) > 30: if no_frames_available > 0 and (now - no_frames_available) > 30:
time.sleep(1) time.sleep(1)
print("sleeping because no frames have been available in a while") print("sleeping because no frames have been available in a while")
else: else:
@ -302,10 +307,8 @@ def process_frames(shared_arr, shared_output_arr, shared_frame_time, shared_moti
# we got a valid frame, so reset the timer # we got a valid frame, so reset the timer
no_frames_available = -1 no_frames_available = -1
# if the frame is more than 0.5 second old, discard it # if the frame is more than 0.5 second old, ignore it
if (datetime.datetime.now().timestamp() - shared_frame_time.value) > 0.5: if (now - shared_frame_time.value) > 0.5:
# signal that we need a new frame
shared_frame_time.value = 0.0
# rest a little bit to avoid maxing out the CPU # rest a little bit to avoid maxing out the CPU
time.sleep(0.01) time.sleep(0.01)
continue continue
@ -313,8 +316,6 @@ def process_frames(shared_arr, shared_output_arr, shared_frame_time, shared_moti
# make a copy of the cropped frame # make a copy of the cropped frame
cropped_frame = arr[region_y_offset:region_y_offset+region_size, region_x_offset:region_x_offset+region_size].copy() 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 frame_time = shared_frame_time.value
# signal that the frame has been used so a new one will be ready
shared_frame_time.value = 0.0
# convert to RGB # convert to RGB
cropped_frame_rgb = cv2.cvtColor(cropped_frame, cv2.COLOR_BGR2RGB) cropped_frame_rgb = cv2.cvtColor(cropped_frame, cv2.COLOR_BGR2RGB)