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dynamic number of processes based on selected regions

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blakeblackshear 2019-02-04 07:07:13 -06:00
parent 623a3044fb
commit 0359e2d2a1
1 changed files with 43 additions and 28 deletions
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71
detect_objects.py
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@ -24,9 +24,8 @@ PATH_TO_LABELS = '/label_map.pbtext'
# TODO: make dynamic?
NUM_CLASSES = 90
REGION_SIZE = 300
REGION_X_OFFSET = 1250
REGION_Y_OFFSET = 180
#REGIONS = "600,0,380:600,600,380:600,1200,380"
REGIONS = os.getenv('REGIONS')
DETECTED_OBJECTS = []
@ -98,6 +97,15 @@ class ObjectParser(threading.Thread):
time.sleep(0.01)
def main():
# Parse selected regions
regions = []
for region_string in REGIONS.split(':'):
region_parts = region_string.split(',')
regions.append({
'size': int(region_parts[0]),
'x_offset': int(region_parts[1]),
'y_offset': int(region_parts[2])
})
# capture a single frame and check the frame shape so the correct array
# size can be allocated in memory
video = cv2.VideoCapture(RTSP_URL)
@ -109,42 +117,45 @@ def main():
exit(1)
video.release()
# create shared value for storing the time the frame was captured
# note: this must be a double even though the value you are storing
# is a float. otherwise it stops updating the value in shared
# memory. probably something to do with the size of the memory block
shared_frame_time = mp.Value('d', 0.0)
shared_frame_time2 = mp.Value('d', 0.0)
shared_memory_objects = []
for region in regions:
shared_memory_objects.append({
# create shared value for storing the time the frame was captured
# note: this must be a double even though the value you are storing
# is a float. otherwise it stops updating the value in shared
# memory. probably something to do with the size of the memory block
'frame_time': mp.Value('d', 0.0),
# create shared array for storing 10 detected objects
'output_array': mp.Array(ctypes.c_double, 6*10)
})
# compute the flattened array length from the array shape
flat_array_length = frame_shape[0] * frame_shape[1] * frame_shape[2]
# create shared array for storing the full frame image data
shared_arr = mp.Array(ctypes.c_uint16, flat_array_length)
# shape current frame so it can be treated as an image
frame_arr = tonumpyarray(shared_arr).reshape(frame_shape)
# create shared array for storing 10 detected objects
shared_output_arr = mp.Array(ctypes.c_double, 6*10)
shared_output_arr2 = mp.Array(ctypes.c_double, 6*10)
capture_process = mp.Process(target=fetch_frames, args=(shared_arr, [shared_frame_time, shared_frame_time2], frame_shape))
capture_process = mp.Process(target=fetch_frames, args=(shared_arr, [obj['frame_time'] for obj in shared_memory_objects], frame_shape))
capture_process.daemon = True
detection_process = mp.Process(target=process_frames, args=(shared_arr, shared_output_arr,
shared_frame_time, frame_shape, REGION_SIZE, REGION_X_OFFSET, REGION_Y_OFFSET))
detection_process.daemon = True
detection_processes = []
for index, region in enumerate(regions):
detection_process = mp.Process(target=process_frames, args=(shared_arr,
shared_memory_objects[index]['output_array'],
shared_memory_objects[index]['frame_time'], frame_shape,
region['size'], region['x_offset'], region['y_offset']))
detection_process.daemon = True
detection_processes.append(detection_process)
detection_process2 = mp.Process(target=process_frames, args=(shared_arr, shared_output_arr2,
shared_frame_time2, frame_shape, 1080, 0, 0))
detection_process.daemon = True
object_parser = ObjectParser([shared_output_arr, shared_output_arr2])
object_parser = ObjectParser([obj['output_array'] for obj in shared_memory_objects])
object_parser.start()
capture_process.start()
print("capture_process pid ", capture_process.pid)
detection_process.start()
print("detection_process pid ", detection_process.pid)
detection_process2.start()
print("detection_process pid ", detection_process2.pid)
for detection_process in detection_processes:
detection_process.start()
print("detection_process pid ", detection_process.pid)
app = Flask(__name__)
@ -175,7 +186,11 @@ def main():
thickness=2,
display_str_list=["{}: {}%".format(obj['name'],int(obj['score']*100))],
use_normalized_coordinates=False)
cv2.rectangle(frame, (REGION_X_OFFSET, REGION_Y_OFFSET), (REGION_X_OFFSET+REGION_SIZE, REGION_Y_OFFSET+REGION_SIZE), (255,255,255), 2)
for region in regions:
cv2.rectangle(frame, (region['x_offset'], region['y_offset']),
(region['x_offset']+region['size'], region['y_offset']+region['size']),
(255,255,255), 2)
# convert back to BGR
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
# encode the image into a jpg
@ -186,8 +201,8 @@ def main():
app.run(host='0.0.0.0', debug=False)
capture_process.join()
detection_process.join()
detection_process2.join()
for detection_process in detection_processes:
detection_process.join()
object_parser.join()
# convert shared memory array into numpy array