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https://github.com/blakeblackshear/frigate.git
synced 2024-11-21 19:07:46 +01:00
basic motion detection working
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blakeblackshear
parent
d8f27658f8
commit
5d894f006a
@ -1,5 +1,6 @@
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import os
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import cv2
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import imutils
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import time
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import datetime
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import ctypes
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@ -24,8 +25,8 @@ PATH_TO_LABELS = '/label_map.pbtext'
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# TODO: make dynamic?
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NUM_CLASSES = 90
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#REGIONS = "600,0,380:600,600,380:600,1200,380"
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REGIONS = os.getenv('REGIONS')
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REGIONS = "300,0,0:300,300,0:300,600,0"
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#REGIONS = os.getenv('REGIONS')
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DETECTED_OBJECTS = []
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@ -152,62 +153,77 @@ def main():
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detection_process.daemon = True
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detection_processes.append(detection_process)
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motion_processes = []
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for index, region in enumerate(regions):
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motion_process = mp.Process(target=detect_motion, args=(shared_arr,
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shared_memory_objects[index]['frame_time'],
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shared_memory_objects[index]['motion_detected'],
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frame_shape,
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region['size'], region['x_offset'], region['y_offset']))
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motion_process.daemon = True
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motion_processes.append(motion_process)
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object_parser = ObjectParser([obj['output_array'] for obj in shared_memory_objects])
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object_parser.start()
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# object_parser.start()
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capture_process.start()
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print("capture_process pid ", capture_process.pid)
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for detection_process in detection_processes:
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detection_process.start()
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print("detection_process pid ", detection_process.pid)
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# for detection_process in detection_processes:
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# detection_process.start()
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# print("detection_process pid ", detection_process.pid)
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for motion_process in motion_processes:
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motion_process.start()
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print("motion_process pid ", motion_process.pid)
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app = Flask(__name__)
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# app = Flask(__name__)
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@app.route('/')
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def index():
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# return a multipart response
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return Response(imagestream(),
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mimetype='multipart/x-mixed-replace; boundary=frame')
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def imagestream():
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global DETECTED_OBJECTS
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while True:
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# max out at 5 FPS
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time.sleep(0.2)
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# make a copy of the current detected objects
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detected_objects = DETECTED_OBJECTS.copy()
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# make a copy of the current frame
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frame = frame_arr.copy()
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# convert to RGB for drawing
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frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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# draw the bounding boxes on the screen
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for obj in DETECTED_OBJECTS:
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vis_util.draw_bounding_box_on_image_array(frame,
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obj['ymin'],
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obj['xmin'],
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obj['ymax'],
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obj['xmax'],
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color='red',
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thickness=2,
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display_str_list=["{}: {}%".format(obj['name'],int(obj['score']*100))],
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use_normalized_coordinates=False)
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# @app.route('/')
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# def index():
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# # return a multipart response
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# return Response(imagestream(),
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# mimetype='multipart/x-mixed-replace; boundary=frame')
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# def imagestream():
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# global DETECTED_OBJECTS
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# while True:
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# # max out at 5 FPS
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# time.sleep(0.2)
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# # make a copy of the current detected objects
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# detected_objects = DETECTED_OBJECTS.copy()
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# # make a copy of the current frame
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# frame = frame_arr.copy()
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# # convert to RGB for drawing
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# frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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# # draw the bounding boxes on the screen
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# for obj in DETECTED_OBJECTS:
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# vis_util.draw_bounding_box_on_image_array(frame,
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# obj['ymin'],
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# obj['xmin'],
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# obj['ymax'],
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# obj['xmax'],
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# color='red',
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# thickness=2,
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# display_str_list=["{}: {}%".format(obj['name'],int(obj['score']*100))],
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# use_normalized_coordinates=False)
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for region in regions:
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cv2.rectangle(frame, (region['x_offset'], region['y_offset']),
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(region['x_offset']+region['size'], region['y_offset']+region['size']),
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(255,255,255), 2)
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# convert back to BGR
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frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
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# encode the image into a jpg
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ret, jpg = cv2.imencode('.jpg', frame)
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yield (b'--frame\r\n'
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b'Content-Type: image/jpeg\r\n\r\n' + jpg.tobytes() + b'\r\n\r\n')
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# for region in regions:
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# cv2.rectangle(frame, (region['x_offset'], region['y_offset']),
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# (region['x_offset']+region['size'], region['y_offset']+region['size']),
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# (255,255,255), 2)
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# # convert back to BGR
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# frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
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# # encode the image into a jpg
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# ret, jpg = cv2.imencode('.jpg', frame)
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# yield (b'--frame\r\n'
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# b'Content-Type: image/jpeg\r\n\r\n' + jpg.tobytes() + b'\r\n\r\n')
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app.run(host='0.0.0.0', debug=False)
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# app.run(host='0.0.0.0', debug=False)
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capture_process.join()
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for detection_process in detection_processes:
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detection_process.join()
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object_parser.join()
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# for detection_process in detection_processes:
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# detection_process.join()
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for motion_process in motion_processes:
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motion_process.join()
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# object_parser.join()
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# convert shared memory array into numpy array
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def tonumpyarray(mp_arr):
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@ -307,6 +323,91 @@ def process_frames(shared_arr, shared_output_arr, shared_frame_time, shared_moti
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# copy the detected objects to the output array, filling the array when needed
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shared_output_arr[:] = objects + [0.0] * (60-len(objects))
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# do the actual object detection
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def detect_motion(shared_arr, shared_frame_time, shared_motion, frame_shape, region_size, region_x_offset, region_y_offset):
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# shape shared input array into frame for processing
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arr = tonumpyarray(shared_arr).reshape(frame_shape)
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no_frames_available = -1
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avg_frame = None
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last_motion = -1
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while True:
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now = datetime.datetime.now().timestamp()
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# if it has been 30 seconds since the last motion, clear the flag
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if last_motion > 0 and (now - last_motion) > 30:
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last_motion = -1
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shared_motion.value = 0
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print("motion cleared")
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# if there isnt a frame ready for processing
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if shared_frame_time.value == 0.0:
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# save the first time there were no frames available
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if no_frames_available == -1:
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no_frames_available = now
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# if there havent been any frames available in 30 seconds,
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# sleep to avoid using so much cpu if the camera feed is down
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if no_frames_available > 0 and (now - no_frames_available) > 30:
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time.sleep(1)
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print("sleeping because no frames have been available in a while")
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else:
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# rest a little bit to avoid maxing out the CPU
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time.sleep(0.01)
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continue
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# we got a valid frame, so reset the timer
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no_frames_available = -1
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# if the frame is more than 0.5 second old, discard it
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if (now - shared_frame_time.value) > 0.5:
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# signal that we need a new frame
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shared_frame_time.value = 0.0
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# rest a little bit to avoid maxing out the CPU
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time.sleep(0.01)
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continue
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# make a copy of the cropped frame
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cropped_frame = arr[region_y_offset:region_y_offset+region_size, region_x_offset:region_x_offset+region_size].copy()
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frame_time = shared_frame_time.value
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# signal that the frame has been used so a new one will be ready
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shared_frame_time.value = 0.0
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# convert to grayscale
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gray = cv2.cvtColor(cropped_frame, cv2.COLOR_BGR2GRAY)
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# convert to uint8
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gray = (gray/256).astype('uint8')
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# apply gaussian blur
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gray = cv2.GaussianBlur(gray, (21, 21), 0)
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if avg_frame is None:
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avg_frame = gray.copy().astype("float")
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continue
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# look at the delta from the avg_frame
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cv2.accumulateWeighted(gray, avg_frame, 0.5)
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frameDelta = cv2.absdiff(gray, cv2.convertScaleAbs(avg_frame))
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thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]
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# dilate the thresholded image to fill in holes, then find contours
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# on thresholded image
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thresh = cv2.dilate(thresh, None, iterations=2)
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cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
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cv2.CHAIN_APPROX_SIMPLE)
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cnts = imutils.grab_contours(cnts)
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# loop over the contours
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for c in cnts:
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# if the contour is too small, ignore it
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if cv2.contourArea(c) < 50:
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continue
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print("motion_detected")
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last_motion = now
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shared_motion.value = 1
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# compute the bounding box for the contour, draw it on the frame,
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# and update the text
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(x, y, w, h) = cv2.boundingRect(c)
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cv2.rectangle(cropped_frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
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cv2.imwrite("motion%d.png" % frame_time, cropped_frame)
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if __name__ == '__main__':
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mp.freeze_support()
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main()
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