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94 lines
3.5 KiB
94 lines
3.5 KiB
5 years ago
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import numpy as np
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import os
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import six.moves.urllib as urllib
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import sys
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import tarfile
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import tensorflow as tf
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import zipfile
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from collections import defaultdict
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from io import StringIO
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from matplotlib import pyplot as plt
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from PIL import Image
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import cv2
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# cap = cv2.VideoCapture(0)
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cap = cv2.VideoCapture("/home/sipp11/Desktop/5min.mp4")
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sys.path.append("..")
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from utils import label_map_util
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from utils import visualization_utils as vis_util
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MODEL_NAME = "ssd_mobilenet_v1_coco_11_06_2017"
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MODEL_FILE = MODEL_NAME + ".tar.gz"
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DOWNLOAD_BASE = "http://download.tensorflow.org/models/object_detection/"
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# Path to frozen detection graph. This is the actual model that is used for the object detection.
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PATH_TO_CKPT = MODEL_NAME + "/frozen_inference_graph.pb"
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# List of the strings that is used to add correct label for each box.
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PATH_TO_LABELS = os.path.join("data", "mscoco_label_map.pbtxt")
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NUM_CLASSES = 90
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opener = urllib.request.URLopener()
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opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)
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tar_file = tarfile.open(MODEL_FILE)
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for file in tar_file.getmembers():
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file_name = os.path.basename(file.name)
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if "frozen_inference_graph.pb" in file_name:
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tar_file.extract(file, os.getcwd())
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detection_graph = tf.Graph()
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with detection_graph.as_default():
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od_graph_def = tf.compat.v1.GraphDef()
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with tf.io.gfile.GFile(PATH_TO_CKPT, "rb") as fid:
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serialized_graph = fid.read()
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od_graph_def.ParseFromString(serialized_graph)
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tf.import_graph_def(od_graph_def, name="")
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label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
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categories = label_map_util.convert_label_map_to_categories(
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label_map, max_num_classes=NUM_CLASSES, use_display_name=True
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)
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category_index = label_map_util.create_category_index(categories)
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with detection_graph.as_default():
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with tf.compat.v1.Session(graph=detection_graph) as sess:
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while True:
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ret, image_np = cap.read()
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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_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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boxes = detection_graph.get_tensor_by_name("detection_boxes:0")
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# Each score represent how level of confidence for each of the objects.
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# Score is shown on the result image, together with the class label.
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scores = detection_graph.get_tensor_by_name("detection_scores:0")
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classes = detection_graph.get_tensor_by_name("detection_classes:0")
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num_detections = detection_graph.get_tensor_by_name("num_detections:0")
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# Actual detection.
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(boxes, scores, classes, num_detections) = sess.run(
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[boxes, scores, classes, num_detections],
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feed_dict={image_tensor: image_np_expanded},
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)
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# Visualization of the results of a detection.
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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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np.squeeze(classes).astype(np.int32),
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np.squeeze(scores),
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category_index,
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use_normalized_coordinates=True,
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line_thickness=8,
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)
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cv2.imshow("object detection", cv2.resize(image_np, (800, 600)))
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if cv2.waitKey(25) & 0xFF == ord("q"):
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cv2.destroyAllWindows()
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break
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