make motion detection less sensitive to rain

reduces the significance of fast moving objects and prioritizes objects that overlap in location across. multiple frames

README.md

@@ -44,7 +44,7 @@ Access the mjpeg stream at http://localhost:5000
 - [x] Add last will and availability for MQTT
 - [ ] Add ability to turn detection on and off via MQTT
 - [ ] Add a max size for motion and objects (height/width > 1.5, total area > 1500 and < 100,000)
-- [ ] Make motion less sensitive to rain
+- [x] Make motion less sensitive to rain
 - [x] Use Events or Conditions to signal between threads rather than polling a value
 - [ ] Implement a debug option to save images with detected objects
 - [ ] Only report if x% of the recent frames have a person to avoid single frame false positives (maybe take an average of the person scores in the past x frames?)
@@ -53,7 +53,7 @@ Access the mjpeg stream at http://localhost:5000
 - [ ] Merge bounding boxes that span multiple regions
 - [ ] Switch to a config file
 - [ ] Allow motion regions to be different than object detection regions
-- [ ] Add motion detection masking
+- [x] Add motion detection masking
 - [x] Change color of bounding box if motion detected
 - [x] Look for a subset of object types
 - [ ] Try and reduce CPU usage by simplifying the tensorflow model to just include the objects we care about

detect_objects.py

@@ -434,17 +434,11 @@ def detect_motion(shared_arr, shared_frame_time, frame_lock, frame_ready, motion
     arr = tonumpyarray(shared_arr).reshape(frame_shape)
 
     avg_frame = None
-    last_motion = -1
+    avg_delta = None
     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) > 2:
-            last_motion = -1
-            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
@@ -459,7 +453,7 @@ def detect_motion(shared_arr, shared_frame_time, frame_lock, frame_ready, motion
         # convert to grayscale
         gray = cv2.cvtColor(cropped_frame, cv2.COLOR_BGR2GRAY)
 
-        # apply image mask
+        # apply image mask to remove areas from motion detection
         gray[mask] = [255]
 
         # apply gaussian blur
@@ -470,15 +464,33 @@ def detect_motion(shared_arr, shared_frame_time, frame_lock, frame_ready, motion
             continue
         
         # look at the delta from the avg_frame
-        cv2.accumulateWeighted(gray, avg_frame, 0.01)
         frameDelta = cv2.absdiff(gray, cv2.convertScaleAbs(avg_frame))
-        thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]
+        
+        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
+        # put it over the edge, 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 = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
         cnts = imutils.grab_contours(cnts)
 
         # if there are no contours, there is no motion
@@ -506,15 +518,22 @@ def detect_motion(shared_arr, shared_frame_time, frame_lock, frame_ready, motion
             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
+            motion_detected.clear()
+            with motion_changed:
+                motion_changed.notify_all()
 
         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)
 
 if __name__ == '__main__':
     mp.freeze_support()