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https://github.com/blakeblackshear/frigate.git
synced 2024-11-26 02:40:44 -06:00
wip: just detect objects in a specific area
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blakeblackshear
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7e3d2f6611
commit
98ce5a4a59
@ -29,9 +29,9 @@ categories = label_map_util.convert_label_map_to_categories(label_map, max_num_c
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use_display_name=True)
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category_index = label_map_util.create_category_index(categories)
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def detect_objects(image_np, sess, detection_graph):
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def detect_objects(cropped_frame, full_frame, sess, detection_graph):
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# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
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image_np_expanded = np.expand_dims(image_np, axis=0)
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image_np_expanded = np.expand_dims(cropped_frame, axis=0)
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image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
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# Each box represents a part of the image where a particular object was detected.
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@ -52,22 +52,41 @@ def detect_objects(image_np, sess, detection_graph):
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objects = []
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for index, value in enumerate(classes[0]):
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object_dict = {}
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if scores[0, index] > 0.5:
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if scores[0, index] > 0.1:
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object_dict[(category_index.get(value)).get('name').encode('utf8')] = \
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scores[0, index]
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objects.append(object_dict)
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squeezed_boxes = np.squeeze(boxes)
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squeezed_scores = np.squeeze(scores)
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if(len(objects)>0):
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# reposition bounding box based on full frame
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for i, box in enumerate(squeezed_boxes):
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if squeezed_scores[i] > .1:
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ymin = ((box[0] * 300) + 200)/1080 # ymin
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xmin = ((box[1] * 300) + 1300)/1920 # xmin
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xmax = ((box[2] * 300) + 200)/1080 # ymax
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ymax = ((box[3] * 300) + 1300)/1920 # xmax
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print("ymin", box[0] * 300, ymin)
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print("xmin", box[1] * 300, xmin)
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print("ymax", box[2] * 300, ymax)
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print("xmax", box[3] * 300, xmax)
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# draw boxes for detected objects on image
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vis_util.visualize_boxes_and_labels_on_image_array(
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image_np,
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np.squeeze(boxes),
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cropped_frame,
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squeezed_boxes,
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np.squeeze(classes).astype(np.int32),
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np.squeeze(scores),
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squeezed_scores,
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category_index,
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use_normalized_coordinates=True,
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line_thickness=4)
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line_thickness=4,
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min_score_thresh=.1)
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# cv2.rectangle(full_frame, (800, 100), (1250, 550), (255,0,0), 2)
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return objects, image_np
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return objects, cropped_frame
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def main():
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# capture a single frame and check the frame shape so the correct array
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@ -88,18 +107,21 @@ def main():
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shared_frame_time = mp.Value('d', 0.0)
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# compute the flattened array length from the array shape
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flat_array_length = frame_shape[0] * frame_shape[1] * frame_shape[2]
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# create shared array for passing the image data from capture to detect_objects
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# create shared array for storing the full frame image data
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shared_arr = mp.Array(ctypes.c_uint16, flat_array_length)
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# create shared array for storing the cropped frame image data
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# TODO: make dynamic
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shared_cropped_arr = mp.Array(ctypes.c_uint16, 300*300*3)
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# create shared array for passing the image data from detect_objects to flask
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shared_output_arr = mp.Array(ctypes.c_uint16, flat_array_length)
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shared_output_arr = mp.Array(ctypes.c_uint16, 300*300*3)#flat_array_length)
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# create a numpy array with the image shape from the shared memory array
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# this is used by flask to output an mjpeg stream
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frame_output_arr = tonumpyarray(shared_output_arr).reshape(frame_shape)
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frame_output_arr = tonumpyarray(shared_output_arr).reshape(300,300,3)
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capture_process = mp.Process(target=fetch_frames, args=(shared_arr, shared_frame_time, frame_shape))
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capture_process = mp.Process(target=fetch_frames, args=(shared_arr, shared_cropped_arr, shared_frame_time, frame_shape))
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capture_process.daemon = True
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detection_process = mp.Process(target=process_frames, args=(shared_arr, shared_output_arr, shared_frame_time, frame_shape))
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detection_process = mp.Process(target=process_frames, args=(shared_arr, shared_cropped_arr, shared_output_arr, shared_frame_time, frame_shape))
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detection_process.daemon = True
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capture_process.start()
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@ -119,9 +141,9 @@ def main():
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# max out at 5 FPS
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time.sleep(0.2)
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# convert back to BGR
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frame_bgr = cv2.cvtColor(frame_output_arr, cv2.COLOR_RGB2BGR)
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# frame_bgr = cv2.cvtColor(frame_output_arr, cv2.COLOR_RGB2BGR)
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# encode the image into a jpg
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ret, jpg = cv2.imencode('.jpg', frame_bgr)
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ret, jpg = cv2.imencode('.jpg', frame_output_arr)
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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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@ -136,9 +158,10 @@ def tonumpyarray(mp_arr):
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# fetch the frames as fast a possible, only decoding the frames when the
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# detection_process has consumed the current frame
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def fetch_frames(shared_arr, shared_frame_time, frame_shape):
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def fetch_frames(shared_arr, shared_cropped_arr, shared_frame_time, frame_shape):
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# convert shared memory array into numpy and shape into image array
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arr = tonumpyarray(shared_arr).reshape(frame_shape)
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cropped_frame = tonumpyarray(shared_cropped_arr).reshape(300,300,3)
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# start the video capture
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video = cv2.VideoCapture(RTSP_URL)
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@ -158,6 +181,12 @@ def fetch_frames(shared_arr, shared_frame_time, frame_shape):
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ret, frame = video.retrieve()
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if ret:
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# copy the frame into the numpy array
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# Position 1
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# cropped_frame[:] = frame[270:720, 550:1000]
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# Position 2
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# frame_cropped = frame[270:720, 100:550]
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# Car
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cropped_frame[:] = frame[200:500, 1300:1600]
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arr[:] = frame
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# signal to the detection_process by setting the shared_frame_time
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shared_frame_time.value = frame_time.timestamp()
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@ -165,11 +194,12 @@ def fetch_frames(shared_arr, shared_frame_time, frame_shape):
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video.release()
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# do the actual object detection
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def process_frames(shared_arr, shared_output_arr, shared_frame_time, frame_shape):
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def process_frames(shared_arr, shared_cropped_arr, shared_output_arr, shared_frame_time, frame_shape):
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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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shared_cropped_frame = tonumpyarray(shared_cropped_arr).reshape(300,300,3)
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# shape shared output array into frame so it can be copied into
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output_arr = tonumpyarray(shared_output_arr).reshape(frame_shape)
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output_arr = tonumpyarray(shared_output_arr).reshape(300,300,3)
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# Load a (frozen) Tensorflow model into memory before the processing loop
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detection_graph = tf.Graph()
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@ -211,14 +241,15 @@ def process_frames(shared_arr, shared_output_arr, shared_frame_time, frame_shape
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# make a copy of the frame
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frame = arr.copy()
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cropped_frame = shared_cropped_frame.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 RGB
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frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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cropped_frame_rgb = cv2.cvtColor(cropped_frame, cv2.COLOR_BGR2RGB)
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# do the object detection
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objects, frame_overlay = detect_objects(frame_rgb, sess, detection_graph)
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objects, frame_overlay = detect_objects(cropped_frame_rgb, frame, sess, detection_graph)
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# copy the output frame with the bounding boxes to the output array
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output_arr[:] = frame_overlay
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if(len(objects) > 0):
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