refactor some classes into new files

frigate/edgetpu.py

@@ -0,0 +1,124 @@
+import multiprocessing as mp
+import numpy as np
+import SharedArray as sa
+import tflite_runtime.interpreter as tflite
+from tflite_runtime.interpreter import load_delegate
+
+def load_labels(path, encoding='utf-8'):
+  """Loads labels from file (with or without index numbers).
+  Args:
+    path: path to label file.
+    encoding: label file encoding.
+  Returns:
+    Dictionary mapping indices to labels.
+  """
+  with open(path, 'r', encoding=encoding) as f:
+    lines = f.readlines()
+    if not lines:
+        return {}
+
+    if lines[0].split(' ', maxsplit=1)[0].isdigit():
+        pairs = [line.split(' ', maxsplit=1) for line in lines]
+        return {int(index): label.strip() for index, label in pairs}
+    else:
+        return {index: line.strip() for index, line in enumerate(lines)}
+
+class ObjectDetector():
+    def __init__(self, model_file):
+        edge_tpu_delegate = None
+        try:
+            edge_tpu_delegate = load_delegate('libedgetpu.so.1.0')
+        except ValueError:
+            print("No EdgeTPU detected. Falling back to CPU.")
+        
+        if edge_tpu_delegate is None:
+            self.interpreter = tflite.Interpreter(
+                model_path=model_file)
+        else:
+            self.interpreter = tflite.Interpreter(
+                model_path=model_file,
+                experimental_delegates=[edge_tpu_delegate])
+        
+        self.interpreter.allocate_tensors()
+
+        self.tensor_input_details = self.interpreter.get_input_details()
+        self.tensor_output_details = self.interpreter.get_output_details()
+    
+    def detect_raw(self, tensor_input):
+        self.interpreter.set_tensor(self.tensor_input_details[0]['index'], tensor_input)
+        self.interpreter.invoke()
+        boxes = np.squeeze(self.interpreter.get_tensor(self.tensor_output_details[0]['index']))
+        label_codes = np.squeeze(self.interpreter.get_tensor(self.tensor_output_details[1]['index']))
+        scores = np.squeeze(self.interpreter.get_tensor(self.tensor_output_details[2]['index']))
+
+        detections = np.zeros((20,6), np.float32)
+        for i, score in enumerate(scores):
+            detections[i] = [label_codes[i], score, boxes[i][0], boxes[i][1], boxes[i][2], boxes[i][3]]
+        
+        return detections
+
+class EdgeTPUProcess():
+    def __init__(self, model):
+        try:
+            sa.delete("frame")
+        except:
+            pass
+        try:
+            sa.delete("detections")
+        except:
+            pass
+
+        self.input_frame = sa.create("frame", shape=(1,300,300,3), dtype=np.uint8)
+        self.detections = sa.create("detections", shape=(20,6), dtype=np.float32)
+
+        self.detect_lock = mp.Lock()
+        self.detect_ready = mp.Event()
+        self.frame_ready = mp.Event()
+
+        def run_detector(model, detect_ready, frame_ready):
+            object_detector = ObjectDetector(model)
+            input_frame = sa.attach("frame")
+            detections = sa.attach("detections")
+
+            while True:
+                # wait until a frame is ready
+                frame_ready.wait()
+                # signal that the process is busy
+                frame_ready.clear()
+                detections[:] = object_detector.detect_raw(input_frame)
+                # signal that the process is ready to detect
+                detect_ready.set()
+
+        self.detect_process = mp.Process(target=run_detector, args=(model, self.detect_ready, self.frame_ready))
+        self.detect_process.daemon = True
+        self.detect_process.start()
+
+class RemoteObjectDetector():
+    def __init__(self, labels, detect_lock, detect_ready, frame_ready):
+        self.labels = load_labels(labels)
+
+        self.input_frame = sa.attach("frame")
+        self.detections = sa.attach("detections")
+
+        self.detect_lock = detect_lock
+        self.detect_ready = detect_ready
+        self.frame_ready = frame_ready
+    
+    def detect(self, tensor_input, threshold=.4):
+        detections = []
+        with self.detect_lock:
+            self.input_frame[:] = tensor_input
+            # unset detections and signal that a frame is ready
+            self.detect_ready.clear()
+            self.frame_ready.set()
+            # wait until the detection process is finished,
+            self.detect_ready.wait()
+            for d in self.detections:
+                if d[1] < threshold:
+                    break
+                detections.append((
+                    self.labels[int(d[0])],
+                    float(d[1]),
+                    (d[2], d[3], d[4], d[5])
+                ))
+        return detections

frigate/motion.py

@@ -0,0 +1,76 @@
+import cv2
+import imutils
+import numpy as np
+
+class MotionDetector():
+    # TODO: add motion masking
+    def __init__(self, frame_shape, resize_factor=4):
+        self.resize_factor = resize_factor
+        self.motion_frame_size = (int(frame_shape[0]/resize_factor), int(frame_shape[1]/resize_factor))
+        self.avg_frame = np.zeros(self.motion_frame_size, np.float)
+        self.avg_delta = np.zeros(self.motion_frame_size, np.float)
+        self.motion_frame_count = 0
+        self.frame_counter = 0
+
+    def detect(self, frame):
+        motion_boxes = []
+
+        # resize frame
+        resized_frame = cv2.resize(frame, dsize=(self.motion_frame_size[1], self.motion_frame_size[0]), interpolation=cv2.INTER_LINEAR)
+
+        # convert to grayscale
+        gray = cv2.cvtColor(resized_frame, cv2.COLOR_BGR2GRAY)
+
+        # it takes ~30 frames to establish a baseline
+        # dont bother looking for motion
+        if self.frame_counter < 30:
+            self.frame_counter += 1
+        else:
+            # compare to average
+            frameDelta = cv2.absdiff(gray, cv2.convertScaleAbs(self.avg_frame))
+
+            # 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
+            # register as motion, 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, self.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(self.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, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+            cnts = imutils.grab_contours(cnts)
+
+            # loop over the contours
+            for c in cnts:
+                # if the contour is big enough, count it as motion
+                contour_area = cv2.contourArea(c)
+                if contour_area > 100:
+                    # cv2.drawContours(resized_frame, [c], -1, (255,255,255), 2)
+                    x, y, w, h = cv2.boundingRect(c)
+                    motion_boxes.append((x*self.resize_factor, y*self.resize_factor, (x+w)*self.resize_factor, (y+h)*self.resize_factor))
+        
+        if len(motion_boxes) > 0:
+            self.motion_frame_count += 1
+            # TODO: this really depends on FPS
+            if self.motion_frame_count >= 10:
+                # only average in the current frame if the difference persists for at least 3 frames
+                cv2.accumulateWeighted(gray, self.avg_frame, 0.2)
+        else:
+            # when no motion, just keep averaging the frames together
+            cv2.accumulateWeighted(gray, self.avg_frame, 0.2)
+            self.motion_frame_count = 0
+
+        return motion_boxes

start_no_thread.py

@@ -14,25 +14,8 @@ import SharedArray as sa
 from scipy.spatial import distance as dist
 import tflite_runtime.interpreter as tflite
 from tflite_runtime.interpreter import load_delegate
-
-def load_labels(path, encoding='utf-8'):
-  """Loads labels from file (with or without index numbers).
-  Args:
-    path: path to label file.
-    encoding: label file encoding.
-  Returns:
-    Dictionary mapping indices to labels.
-  """
-  with open(path, 'r', encoding=encoding) as f:
-    lines = f.readlines()
-    if not lines:
-      return {}
-
-    if lines[0].split(' ', maxsplit=1)[0].isdigit():
-      pairs = [line.split(' ', maxsplit=1) for line in lines]
-      return {int(index): label.strip() for index, label in pairs}
-    else:
-      return {index: line.strip() for index, line in enumerate(lines)}
+from frigate.edgetpu import ObjectDetector, EdgeTPUProcess, RemoteObjectDetector, load_labels
+from frigate.motion import MotionDetector
 
 def draw_box_with_label(frame, x_min, y_min, x_max, y_max, label, info, thickness=2, color=None, position='ul'):
     if color is None:
@@ -152,193 +135,6 @@ def create_tensor_input(frame, region):
     # Expand dimensions since the model expects images to have shape: [1, 300, 300, 3]
     return np.expand_dims(cropped_frame, axis=0)
 
-
-class MotionDetector():
-    # TODO: add motion masking
-    def __init__(self, frame_shape, resize_factor=4):
-        self.resize_factor = resize_factor
-        self.motion_frame_size = (int(frame_shape[0]/resize_factor), int(frame_shape[1]/resize_factor))
-        self.avg_frame = np.zeros(self.motion_frame_size, np.float)
-        self.avg_delta = np.zeros(self.motion_frame_size, np.float)
-        self.motion_frame_count = 0
-        self.frame_counter = 0
-
-    def detect(self, frame):
-        motion_boxes = []
-
-        # resize frame
-        resized_frame = cv2.resize(frame, dsize=(self.motion_frame_size[1], self.motion_frame_size[0]), interpolation=cv2.INTER_LINEAR)
-
-        # convert to grayscale
-        gray = cv2.cvtColor(resized_frame, cv2.COLOR_BGR2GRAY)
-
-        # it takes ~30 frames to establish a baseline
-        # dont bother looking for motion
-        if self.frame_counter < 30:
-            self.frame_counter += 1
-        else:
-            # compare to average
-            frameDelta = cv2.absdiff(gray, cv2.convertScaleAbs(self.avg_frame))
-
-            # 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
-            # register as motion, 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, self.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(self.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, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-            cnts = imutils.grab_contours(cnts)
-
-            # loop over the contours
-            for c in cnts:
-                # if the contour is big enough, count it as motion
-                contour_area = cv2.contourArea(c)
-                if contour_area > 100:
-                    # cv2.drawContours(resized_frame, [c], -1, (255,255,255), 2)
-                    x, y, w, h = cv2.boundingRect(c)
-                    motion_boxes.append((x*self.resize_factor, y*self.resize_factor, (x+w)*self.resize_factor, (y+h)*self.resize_factor))
-        
-        if len(motion_boxes) > 0:
-            self.motion_frame_count += 1
-            # TODO: this really depends on FPS
-            if self.motion_frame_count >= 10:
-                # only average in the current frame if the difference persists for at least 3 frames
-                cv2.accumulateWeighted(gray, self.avg_frame, 0.2)
-        else:
-            # when no motion, just keep averaging the frames together
-            cv2.accumulateWeighted(gray, self.avg_frame, 0.2)
-            self.motion_frame_count = 0
-
-        return motion_boxes
-
-class ObjectDetector():
-    def __init__(self, model_file, label_file):
-        self.labels = load_labels(label_file)
-        edge_tpu_delegate = None
-        try:
-            edge_tpu_delegate = load_delegate('libedgetpu.so.1.0')
-        except ValueError:
-            print("No EdgeTPU detected. Falling back to CPU.")
-        
-        if edge_tpu_delegate is None:
-            self.interpreter = tflite.Interpreter(
-                model_path=model_file)
-        else:
-            self.interpreter = tflite.Interpreter(
-                model_path=model_file,
-                experimental_delegates=[edge_tpu_delegate])
-        
-        self.interpreter.allocate_tensors()
-
-        self.tensor_input_details = self.interpreter.get_input_details()
-        self.tensor_output_details = self.interpreter.get_output_details()
-    
-    def detect_raw(self, tensor_input):
-        self.interpreter.set_tensor(self.tensor_input_details[0]['index'], tensor_input)
-        self.interpreter.invoke()
-        boxes = np.squeeze(self.interpreter.get_tensor(self.tensor_output_details[0]['index']))
-        label_codes = np.squeeze(self.interpreter.get_tensor(self.tensor_output_details[1]['index']))
-        scores = np.squeeze(self.interpreter.get_tensor(self.tensor_output_details[2]['index']))
-
-        detections = np.zeros((20,6), np.float32)
-        for i, score in enumerate(scores):
-            detections[i] = [label_codes[i], score, boxes[i][0], boxes[i][1], boxes[i][2], boxes[i][3]]
-        
-        return detections
-
-    def detect(self, tensor_input, threshold=.4):
-        self.interpreter.set_tensor(self.tensor_input_details[0]['index'], tensor_input)
-        self.interpreter.invoke()
-        boxes = np.squeeze(self.interpreter.get_tensor(self.tensor_output_details[0]['index']))
-        label_codes = np.squeeze(self.interpreter.get_tensor(self.tensor_output_details[1]['index']))
-        scores = np.squeeze(self.interpreter.get_tensor(self.tensor_output_details[2]['index']))
-
-        detections = []
-        for i, score in enumerate(scores):
-            label = self.labels[int(label_codes[i])]
-            
-            if score < threshold:
-                break
-
-            detections.append((
-                label,
-                float(score),
-                boxes[i]
-            ))
-        
-        return detections
-
-class RemoteObjectDetector():
-    def __init__(self, model, labels):
-        self.labels = load_labels(labels)
-        try:
-            sa.delete("frame")
-        except:
-            pass
-        try:
-            sa.delete("detections")
-        except:
-            pass
-
-        self.input_frame = sa.create("frame", shape=(1,300,300,3), dtype=np.uint8)
-        self.detections = sa.create("detections", shape=(20,6), dtype=np.float32)
-
-        self.detect_lock = mp.Lock()
-        self.detect_ready = mp.Event()
-        self.frame_ready = mp.Event()
-
-        def run_detector(model, labels, detect_ready, frame_ready):
-            object_detector = ObjectDetector(model, labels)
-            input_frame = sa.attach("frame")
-            detections = sa.attach("detections")
-
-            while True:
-                # wait until a frame is ready
-                frame_ready.wait()
-                # signal that the process is busy
-                frame_ready.clear()
-                detections[:] = object_detector.detect_raw(input_frame)
-                # signal that the process is ready to detect
-                detect_ready.set()
-
-        self.detect_process = mp.Process(target=run_detector, args=(model, labels, self.detect_ready, self.frame_ready))
-        self.detect_process.daemon = True
-        self.detect_process.start()
-    
-    def detect(self, tensor_input, threshold=.4):
-        detections = []
-        with self.detect_lock:
-            self.input_frame[:] = tensor_input
-            # unset detections and signal that a frame is ready
-            self.detect_ready.clear()
-            self.frame_ready.set()
-            # wait until the detection process is finished,
-            self.detect_ready.wait()
-            for d in self.detections:
-                if d[1] < threshold:
-                    break
-                detections.append((
-                    self.labels[int(d[0])],
-                    float(d[1]),
-                    (d[2], d[3], d[4], d[5])
-                ))
-        return detections
-
 class ObjectTracker():
     def __init__(self, max_disappeared):
         self.tracked_objects = {}
@@ -521,7 +317,7 @@ def main():
 
         start_frame = datetime.datetime.now().timestamp()
         frame_detections = 0
-        frame_bytes = ffmpeg_process.stdout.read(frame_size)#f.read(frame_size)
+        frame_bytes = ffmpeg_process.stdout.read(frame_size)
         if not frame_bytes:
             break
         frame_time = datetime.datetime.now().timestamp()