blakeblackshear 6 anni fa
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      .dockerignore
  2. 86 0
      Dockerfile
  3. 661 0
      LICENSE
  4. 35 0
      README.md
  5. 224 0
      detect_objects.py

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.dockerignore

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+README.md

+ 86 - 0
Dockerfile

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+FROM ubuntu:16.04
+
+# Install system packages
+RUN apt-get -qq update && apt-get -qq install --no-install-recommends -y python3 \ 
+ python3-dev \
+ python-pil \
+ python-lxml \
+ python-tk \
+ build-essential \
+ cmake \ 
+ git \ 
+ libgtk2.0-dev \ 
+ pkg-config \ 
+ libavcodec-dev \ 
+ libavformat-dev \ 
+ libswscale-dev \ 
+ libtbb2 \
+ libtbb-dev \ 
+ libjpeg-dev \
+ libpng-dev \
+ libtiff-dev \
+ libjasper-dev \
+ libdc1394-22-dev \
+ x11-apps \
+ wget \
+ vim \
+ ffmpeg \
+ unzip \
+ && rm -rf /var/lib/apt/lists/* 
+
+# Install core packages 
+RUN wget -q -O /tmp/get-pip.py --no-check-certificate https://bootstrap.pypa.io/get-pip.py && python3 /tmp/get-pip.py
+RUN  pip install -U pip \
+ numpy \
+ matplotlib \
+ notebook \
+ jupyter \
+ pandas \
+ moviepy \
+ tensorflow \
+ keras \
+ autovizwidget \
+ Flask
+
+# Install tensorflow models object detection
+RUN GIT_SSL_NO_VERIFY=true git clone -q https://github.com/tensorflow/models /usr/local/lib/python3.5/dist-packages/tensorflow/models
+RUN wget -q -P /usr/local/src/ --no-check-certificate https://github.com/google/protobuf/releases/download/v3.5.1/protobuf-python-3.5.1.tar.gz
+
+# Download & build protobuf-python
+RUN cd /usr/local/src/ \
+ && tar xf protobuf-python-3.5.1.tar.gz \
+ && rm protobuf-python-3.5.1.tar.gz \
+ && cd /usr/local/src/protobuf-3.5.1/ \
+ && ./configure \
+ && make \
+ && make install \
+ && ldconfig \
+ && rm -rf /usr/local/src/protobuf-3.5.1/
+
+# Add dataframe display widget
+RUN jupyter nbextension enable --py --sys-prefix widgetsnbextension
+
+# Download & build OpenCV
+RUN wget -q -P /usr/local/src/ --no-check-certificate https://github.com/opencv/opencv/archive/3.4.1.zip
+RUN cd /usr/local/src/ \
+ && unzip 3.4.1.zip \
+ && rm 3.4.1.zip \
+ && cd /usr/local/src/opencv-3.4.1/ \
+ && mkdir build \
+ && cd /usr/local/src/opencv-3.4.1/build \ 
+ && cmake -D CMAKE_INSTALL_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local/ .. \
+ && make -j4 \
+ && make install \
+ && rm -rf /usr/local/src/opencv-3.4.1
+
+# Minimize image size 
+RUN (apt-get autoremove -y; \
+     apt-get autoclean -y)
+
+# Set TF object detection available
+ENV PYTHONPATH "$PYTHONPATH:/usr/local/lib/python3.5/dist-packages/tensorflow/models/research:/usr/local/lib/python3.5/dist-packages/tensorflow/models/research/slim"
+RUN cd /usr/local/lib/python3.5/dist-packages/tensorflow/models/research && protoc object_detection/protos/*.proto --python_out=.
+
+COPY detect_objects.py .
+
+CMD ["python3", "-u", "detect_objects.py"]

+ 661 - 0
LICENSE

@@ -0,0 +1,661 @@
+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
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+ 35 - 0
README.md

@@ -0,0 +1,35 @@
+# Realtime Object Detection for RTSP Cameras
+- Prioritizes realtime processing over frames per second. Dropping frames is fine.
+- OpenCV runs in a separate process so it can grab frames as quickly as possible to ensure there aren't old frames in the buffer
+- Object detection with Tensorflow runs in a separate process and ignores frames that are more than 0.5 seconds old
+- Uses shared memory arrays for handing frames between processes
+- Provides a url for viewing the video feed at a hard coded ~5FPS as an mjpeg stream
+- Frames are only encoded into mjpeg stream when it is being viewed
+
+## Getting Started
+Build the container with
+```
+docker build -t realtime-od .
+```
+
+Download a model from the [zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md).
+
+Download the cooresponding label map from [here](https://github.com/tensorflow/models/tree/master/research/object_detection/data).
+
+Run the container with
+```
+docker run -it --rm \
+-v <path_to_frozen_detection_graph.pb>:/frozen_inference_graph.pb:ro \
+-v <path_to_labelmap.pbtext>:/label_map.pbtext:ro \
+-p 5000:5000 \
+-e RTSP_URL='<rtsp_url>' \
+realtime-od:latest
+```
+
+Access the mjpeg stream at http://localhost:5000
+
+## Future improvements
+- MQTT messages when detected objects change
+- Dynamic changes to processing speed, ie. only process 1FPS unless motion detected
+- Break incoming frame into multiple smaller images and run detection in parallel for lower latency (rather than input a lower resolution)
+- Parallel processing to increase FPS

+ 224 - 0
detect_objects.py

@@ -0,0 +1,224 @@
+import os
+import cv2
+import time
+import datetime
+import ctypes
+import logging
+import multiprocessing as mp
+from contextlib import closing
+import numpy as np
+import tensorflow as tf
+from object_detection.utils import label_map_util
+from object_detection.utils import visualization_utils as vis_util
+from flask import Flask, Response, make_response
+
+RTSP_URL = os.getenv('RTSP_URL')
+
+# Path to frozen detection graph. This is the actual model that is used for the object detection.
+PATH_TO_CKPT = '/frozen_inference_graph.pb'
+
+# List of the strings that is used to add correct label for each box.
+PATH_TO_LABELS = '/label_map.pbtext'
+
+# TODO: make dynamic?
+NUM_CLASSES = 90
+
+# Loading label map
+label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
+categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES,
+                                                            use_display_name=True)
+category_index = label_map_util.create_category_index(categories)
+
+def detect_objects(image_np, sess, detection_graph):
+    # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
+    image_np_expanded = np.expand_dims(image_np, axis=0)
+    image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
+
+    # Each box represents a part of the image where a particular object was detected.
+    boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
+
+    # Each score represent how level of confidence for each of the objects.
+    # Score is shown on the result image, together with the class label.
+    scores = detection_graph.get_tensor_by_name('detection_scores:0')
+    classes = detection_graph.get_tensor_by_name('detection_classes:0')
+    num_detections = detection_graph.get_tensor_by_name('num_detections:0')
+
+    # Actual detection.
+    (boxes, scores, classes, num_detections) = sess.run(
+        [boxes, scores, classes, num_detections],
+        feed_dict={image_tensor: image_np_expanded})
+
+    # build an array of detected objects
+    objects = []
+    for index, value in enumerate(classes[0]):
+        object_dict = {}
+        if scores[0, index] > 0.5:
+            object_dict[(category_index.get(value)).get('name').encode('utf8')] = \
+                                scores[0, index]
+            objects.append(object_dict)
+
+    # draw boxes for detected objects on image
+    vis_util.visualize_boxes_and_labels_on_image_array(
+        image_np,
+        np.squeeze(boxes),
+        np.squeeze(classes).astype(np.int32),
+        np.squeeze(scores),
+        category_index,
+        use_normalized_coordinates=True,
+        line_thickness=4)
+
+    return objects, image_np
+
+def main():
+    # capture a single frame and check the frame shape so the correct array
+    # size can be allocated in memory
+    video = cv2.VideoCapture(RTSP_URL)
+    ret, frame = video.read()
+    if ret:
+        frame_shape = frame.shape
+    else:
+        print("Unable to capture video stream")
+        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)
+    # 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 passing the image data from capture to detect_objects
+    shared_arr = mp.Array(ctypes.c_uint16, flat_array_length)
+    # create shared array for passing the image data from detect_objects to flask
+    shared_output_arr = mp.Array(ctypes.c_uint16, flat_array_length)
+    # create a numpy array with the image shape from the shared memory array
+    # this is used by flask to output an mjpeg stream
+    frame_output_arr = tonumpyarray(shared_output_arr).reshape(frame_shape)
+
+    capture_process = mp.Process(target=fetch_frames, args=(shared_arr, shared_frame_time, frame_shape))
+    capture_process.daemon = True
+
+    detection_process = mp.Process(target=process_frames, args=(shared_arr, shared_output_arr, shared_frame_time, frame_shape))
+    detection_process.daemon = True
+
+    capture_process.start()
+    print("capture_process pid ", capture_process.pid)
+    detection_process.start()
+    print("detection_process pid ", detection_process.pid)
+
+    app = Flask(__name__)
+
+    @app.route('/')
+    def index():
+        # return a multipart response
+        return Response(imagestream(),
+                        mimetype='multipart/x-mixed-replace; boundary=frame')
+    def imagestream():
+        while True:
+            # max out at 5 FPS
+            time.sleep(0.2)
+            # convert back to BGR
+            frame_bgr = cv2.cvtColor(frame_output_arr, cv2.COLOR_RGB2BGR)
+            # encode the image into a jpg
+            ret, jpg = cv2.imencode('.jpg', frame_bgr)
+            yield (b'--frame\r\n'
+                b'Content-Type: image/jpeg\r\n\r\n' + jpg.tobytes() + b'\r\n\r\n')
+
+    app.run(host='0.0.0.0', debug=False)
+
+    capture_process.join()
+    detection_process.join()
+
+# convert shared memory array into numpy array
+def tonumpyarray(mp_arr):
+    return np.frombuffer(mp_arr.get_obj(), dtype=np.uint16)
+
+# fetch the frames as fast a possible, only decoding the frames when the
+# detection_process has consumed the current frame
+def fetch_frames(shared_arr, shared_frame_time, frame_shape):
+    # convert shared memory array into numpy and shape into image array
+    arr = tonumpyarray(shared_arr).reshape(frame_shape)
+
+    # start the video capture
+    video = cv2.VideoCapture(RTSP_URL)
+    # keep the buffer small so we minimize old data
+    video.set(cv2.CAP_PROP_BUFFERSIZE,1)
+
+    while True:
+        # grab the frame, but dont decode it yet
+        ret = video.grab()
+        # snapshot the time the frame was grabbed
+        frame_time = datetime.datetime.now()
+        if ret:
+            # if the detection_process is ready for the next frame decode it
+            # otherwise skip this frame and move onto the next one
+            if shared_frame_time.value == 0.0:
+                # go ahead and decode the current frame
+                ret, frame = video.retrieve()
+                if ret:
+                    # copy the frame into the numpy array
+                    arr[:] = frame
+                    # signal to the detection_process by setting the shared_frame_time
+                    shared_frame_time.value = frame_time.timestamp()
+    
+    video.release()
+
+# do the actual object detection
+def process_frames(shared_arr, shared_output_arr, shared_frame_time, frame_shape):
+    # shape shared input array into frame for processing
+    arr = tonumpyarray(shared_arr).reshape(frame_shape)
+    # shape shared output array into frame so it can be copied into
+    output_arr = tonumpyarray(shared_output_arr).reshape(frame_shape)
+
+    # Load a (frozen) Tensorflow model into memory before the processing loop
+    detection_graph = tf.Graph()
+    with detection_graph.as_default():
+        od_graph_def = tf.GraphDef()
+        with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
+            serialized_graph = fid.read()
+            od_graph_def.ParseFromString(serialized_graph)
+            tf.import_graph_def(od_graph_def, name='')
+        sess = tf.Session(graph=detection_graph)
+
+    no_frames_available = -1
+    while True:
+        # if there isnt a frame ready for processing
+        if shared_frame_time.value == 0.0:
+            # save the first time there were no frames available
+            if no_frames_available == -1:
+                no_frames_available = datetime.datetime.now().timestamp()
+            # if there havent been any frames available in 30 seconds, 
+            # sleep to avoid using so much cpu if the camera feed is down
+            if no_frames_available > 0 and (datetime.datetime.now().timestamp() - no_frames_available) > 30:
+                time.sleep(1)
+                print("sleeping because no frames have been available in a while")
+            continue
+        
+        # we got a valid frame, so reset the timer
+        no_frames_available = -1
+
+        # if the frame is more than 0.5 second old, discard it
+        if (datetime.datetime.now().timestamp() - shared_frame_time.value) > 0.5:
+            # signal that we need a new frame
+            shared_frame_time.value = 0.0
+            continue
+        
+        # make a copy of the frame
+        frame = arr.copy()
+        frame_time = shared_frame_time.value
+        # signal that the frame has been used so a new one will be ready
+        shared_frame_time.value = 0.0
+
+        # convert to RGB
+        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        # do the object detection
+        objects, frame_overlay = detect_objects(frame_rgb, sess, detection_graph)
+        # copy the output frame with the bounding boxes to the output array
+        output_arr[:] = frame_overlay
+        if(len(objects) > 0):
+            print(objects)
+
+if __name__ == '__main__':
+    mp.freeze_support()
+    main()