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87 lines
3.5 KiB
87 lines
3.5 KiB
import pyrealsense2 as rs
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import numpy as np
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import cv2
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import tensorflow as tf
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# Configure depth and color streams
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pipeline = rs.pipeline()
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config = rs.config()
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config.enable_stream(rs.stream.color, 1280, 720, rs.format.bgr8, 30)
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print("[INFO] Starting streaming...")
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pipeline.start(config)
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print("[INFO] Camera ready.")
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# download model from: https://github.com/opencv/opencv/wiki/TensorFlow-Object-Detection-API#run-network-in-opencv
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print("[INFO] Loading model...")
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PATH_TO_CKPT = "frozen_inference_graph.pb"
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# Load the Tensorflow model into memory.
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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.compat.v1.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.compat.v1.import_graph_def(od_graph_def, name='')
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sess = tf.compat.v1.Session(graph=detection_graph)
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# Input tensor is the image
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image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
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# Output tensors are the detection boxes, scores, and classes
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# Each box represents a part of the image where a particular object was detected
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detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
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# Each score represents level of confidence for each of the objects.
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# The score is shown on the result image, together with the class label.
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detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
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detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
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# Number of objects detected
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num_detections = detection_graph.get_tensor_by_name('num_detections:0')
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# code source of tensorflow model loading: https://www.geeksforgeeks.org/ml-training-image-classifier-using-tensorflow-object-detection-api/
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print("[INFO] Model loaded.")
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colors_hash = {}
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while True:
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frames = pipeline.wait_for_frames()
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color_frame = frames.get_color_frame()
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# Convert images to numpy arrays
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color_image = np.asanyarray(color_frame.get_data())
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scaled_size = (color_frame.width, color_frame.height)
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# expand image dimensions to have shape: [1, None, None, 3]
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# i.e. a single-column array, where each item in the column has the pixel RGB value
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image_expanded = np.expand_dims(color_image, axis=0)
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# Perform the actual detection by running the model with the image as input
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(boxes, scores, classes, num) = sess.run([detection_boxes, detection_scores, detection_classes, num_detections],
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feed_dict={image_tensor: image_expanded})
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boxes = np.squeeze(boxes)
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classes = np.squeeze(classes).astype(np.int32)
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scores = np.squeeze(scores)
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for idx in range(int(num)):
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class_ = classes[idx]
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score = scores[idx]
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box = boxes[idx]
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if class_ not in colors_hash:
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colors_hash[class_] = tuple(np.random.choice(range(256), size=3))
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if score > 0.6:
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left = int(box[1] * color_frame.width)
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top = int(box[0] * color_frame.height)
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right = int(box[3] * color_frame.width)
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bottom = int(box[2] * color_frame.height)
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p1 = (left, top)
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p2 = (right, bottom)
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# draw box
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r, g, b = colors_hash[class_]
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cv2.rectangle(color_image, p1, p2, (int(r), int(g), int(b)), 2, 1)
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cv2.namedWindow('RealSense', cv2.WINDOW_AUTOSIZE)
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cv2.imshow('RealSense', color_image)
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cv2.waitKey(1)
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print("[INFO] stop streaming ...")
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pipeline.stop()
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