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davehorner/video_save.py

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Created September 29, 2020 19:36
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  1. @imamdigmi imamdigmi revised this gist Apr 3, 2018. 1 changed file with 1 addition and 1 deletion.
    2 changes: 1 addition & 1 deletion video_save.py
    View file
    Original file line number Diff line number Diff line change
    @@ -18,7 +18,7 @@
    if tf.__version__ < '1.4.0':
    raise ImportError('Please upgrade your tensorflow installation to v1.4.* or later!')

    FILE_OUTPUT = '<OUTPUT_VIDEO_NAME>.avi'
    FILE_OUTPUT = '<PATH/TO/OUTPUT/VIDEO/NAME>.avi'

    # Checks and deletes the output file
    # You cant have a existing file or it will through an error
  2. @imamdigmi imamdigmi created this gist Apr 3, 2018.
    121 changes: 121 additions & 0 deletions video_save.py
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    Original file line number Diff line number Diff line change
    @@ -0,0 +1,121 @@
    """
    This notebook will demontrate a pre-trained model to recognition plate number in an image.
    Make sure to follow the [installation instructions](https://github.com/imamdigmi/plate-number-recognition#setup) before you start.
    """
    import numpy as np
    import os
    import six.moves.urllib as urllib
    import sys
    import tarfile
    import tensorflow as tf
    import zipfile
    from collections import defaultdict
    from io import StringIO
    from matplotlib import pyplot as plt
    from PIL import Image
    import cv2 as cv2

    if tf.__version__ < '1.4.0':
    raise ImportError('Please upgrade your tensorflow installation to v1.4.* or later!')

    FILE_OUTPUT = '<OUTPUT_VIDEO_NAME>.avi'

    # Checks and deletes the output file
    # You cant have a existing file or it will through an error
    if os.path.isfile(FILE_OUTPUT):
    os.remove(FILE_OUTPUT)

    # Playing video from file
    cap = cv2.VideoCapture('<PATH/TO/VIDEO/FILE.<mp4|avi|...>>')

    # Default resolutions of the frame are obtained.The default resolutions are system dependent.
    # We convert the resolutions from float to integer.
    frame_width = int(cap.get(3))
    frame_height = int(cap.get(4))

    # Define the codec and create VideoWriter object.The output is stored in 'output.avi' file.
    out = cv2.VideoWriter(FILE_OUTPUT, cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'),
    10, (frame_width, frame_height))

    sys.path.append("..")

    # Object detection imports
    # Here are the imports from the object detection module.
    from utils import label_map_util
    from utils import visualization_utils as vis_util

    # Model preparation
    MODEL_NAME = '<MODEL_NAME>'
    PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'
    PATH_TO_LABELS = os.path.join('data', '<LABEL_NAME>.pbtxt')
    NUM_CLASSES = 2

    # Load a (frozen) Tensorflow model into memory.
    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='')

    # 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)

    with detection_graph.as_default():
    with tf.Session(graph=detection_graph) as sess:
    # 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')

    while(cap.isOpened()):
    # Capture frame-by-frame
    ret, frame = cap.read()

    # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
    image_np_expanded = np.expand_dims(frame, axis=0)

    # Actual detection.
    (boxes, scores, classes, num) = sess.run(
    [detection_boxes, detection_scores, detection_classes, num_detections],
    feed_dict={image_tensor: image_np_expanded})

    # Visualization of the results of a detection.
    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)

    if ret == True:
    # Saves for video
    out.write(frame)

    # Display the resulting frame
    cv2.imshow('Charving Detection', frame)

    # Close window when "Q" button pressed
    if cv2.waitKey(1) & 0xFF == ord('q'):
    break
    else:
    break

    # When everything done, release the video capture and video write objects
    cap.release()
    out.release()

    # Closes all the frames
    cv2.destroyAllWindows()