Initial commit

.gitignore

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+*.tar.gz
+*/

webcamdemo.py

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+#!/usr/bin/env python
+
+"""webcam_demo:  A live webcam stream with overlayed information."""
+
+import sys
+import cv2
+import numpy as np
+import tensorflow as tf
+import collections
+
+import os
+import six.moves.urllib as urllib
+import tarfile
+import time
+
+from threading import Thread
+from queue import Queue
+
+from object_detection.utils import label_map_util
+from object_detection.utils import visualization_utils as vis_util
+
+# What model to download.
+#MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017'
+MODEL_NAME = 'ssd_inception_v2_coco_11_06_2017'
+#MODEL_NAME = 'faster_rcnn_inception_resnet_v2_atrous_coco_11_06_2017'
+MODEL_FILE = MODEL_NAME + '.tar.gz'
+DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'
+
+# Path to frozen detection graph. This is the actual model that is used for the object detection.
+PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'
+
+# List of the strings that is used to add correct label for each box.
+PATH_TO_LABELS = os.path.join('/home/brust/repos/models/research/object_detection', 'data', 'mscoco_label_map.pbtxt')
+
+NUM_CLASSES = 90
+
+
+class FileVideoStream:
+    def __init__(self, device=0, queue_size=2):
+        self.stream = cv2.VideoCapture(device)
+        ret, _ = self.stream.read()
+        if not ret:
+            print('Error: read() returned false. IsOpened: %r' % (self.stream.isOpened()))
+
+        self.stopped = not ret
+        self.Q = Queue(maxsize=queue_size)
+
+    def start(self):
+        t = Thread(target=self.update, args=())
+        t.daemon = True
+        t.start()
+        self.running_thread = t
+        return self
+
+    def update(self):
+        while True:
+            if self.stopped:
+                return
+            else:
+                ret, frame = self.stream.read()
+                if not ret:
+                    self.stop()
+                    return
+
+                if not self.Q.full():
+                    for i in range(1):
+                        self.Q.put(frame)
+
+                else:
+                    self.Q.get()
+                    self.Q.put(frame)
+                    time.sleep(0)
+
+    def read(self):
+        return self.Q.get()
+
+    def more(self):
+        return self.Q.qsize() > 0
+
+    def stop(self):
+        self.stopped = True
+
+    def close(self):
+        self.stop()
+        self.running_thread.join()
+        self.stream.release()
+
+def main(cam_id):
+    time.time()
+    frames = 0
+    if not os.path.exists(MODEL_FILE):
+      print('Downloading model...')
+      opener = urllib.request.URLopener()
+      opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)
+    tar_file = tarfile.open(MODEL_FILE)
+    for file in tar_file.getmembers():
+        file_name = os.path.basename(file.name)
+        if 'frozen_inference_graph.pb' in file_name:
+            tar_file.extract(file, os.getcwd())
+
+    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='')
+
+    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 load_image_into_numpy_array(image):
+        if isinstance(image.size, collections.Sequence):
+            (im_width, im_height) = image.size
+            return np.array(image.getdata(), dtype=np.uint8).reshape((im_height, im_width, 3))
+        else:
+            im_height = image.shape[0]
+            im_width = image.shape[1]
+            return np.array(image).reshape((im_height, im_width, 3))
+
+    # For the sake of simplicity we will use only 2 images:
+    # image1.jpg
+    # image2.jpg
+    # If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS.
+    PATH_TO_TEST_IMAGES_DIR = 'test_images'
+    TEST_IMAGE_PATHS = [os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 3)]
+
+    # Size, in inches, of the output images.
+    IMAGE_SIZE = (12, 8)
+
+
+    with detection_graph.as_default():
+        with tf.Session(graph=detection_graph) as sess:
+
+            cap = FileVideoStream(cam_id).start()
+
+            if cap.stopped:
+                print('Error: Stream is not available')
+                quit(-1)
+
+            # Definite input and output Tensors for detection_graph
+            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.
+            detection_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.
+            detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
+            detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
+            num_detections = detection_graph.get_tensor_by_name('num_detections:0')
+
+            first = time.time()
+            last = first
+
+            print("Running...")
+            while True:
+                now = time.time()
+                diff = now - last
+                last = now
+                fps_string = "FPS: %02.1f" % (1.0 / diff)
+
+                frame = cap.read()
+
+                rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+                small = cv2.resize(rgb, (128, 128))
+                # pilim = Image.fromarray(rgb)
+                # pilim_small = pilim.resize((128, 128), resample=Image.LANCZOS)
+
+                # image_np = load_image_into_numpy_array(pilim)
+                image_np_small = load_image_into_numpy_array(rgb)
+                # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
+                image_np_small_expanded = np.expand_dims(image_np_small, axis=0)
+                # Actual detection.
+                if True:
+                    (boxes, scores, classes, num) = sess.run(
+                        [detection_boxes, detection_scores, detection_classes, num_detections],
+                        feed_dict={image_tensor: image_np_small_expanded})
+
+                    vis_util.visualize_boxes_and_labels_on_image_array(
+                        frame,
+                        np.squeeze(boxes),
+                        np.squeeze(classes).astype(np.int32),
+                        np.squeeze(scores),
+                        category_index,
+                        use_normalized_coordinates=True,
+                        line_thickness=8)
+
+                #bgr = cv2.cvtColor(rgb, cv2.COLOR_RGB2BGR)
+                cv2.putText(frame, fps_string, (0, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.5, 255)
+                cv2.putText(frame, "Elapsed time: %02.1fs" % (now - first), (0, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.5, 255)
+                cv2.imshow('Webcam Demo', frame)
+                frames += 1
+
+                if (cv2.waitKey(1) & 0xFF == ord('q')):  #  or (now - first) >= 20.0:
+                    print("Benchmark done. FPS avg: %02.1f" % (float(frames) / (now - first)))
+                    print("Time per frame: %.1f ms" % (1000.0 * (float(now - first) / float(frames))))
+                    print("Elapsed time: %02.1fs" % (now - first))
+                    break
+
+        cap.close()
+        cv2.destroyAllWindows()
+
+if __name__ == "__main__":
+    cam_id = int(sys.argv[1]) if len(sys.argv) > 1 else 0
+    main(cam_id)