如何在Tensorflow中检测目标在图像中的位置

# Run the model
    out = sess.run([sess.graph.get_tensor_by_name('num_detections:0'),
                    sess.graph.get_tensor_by_name('detection_scores:0'),
                    sess.graph.get_tensor_by_name('detection_boxes:0'),
                    sess.graph.get_tensor_by_name('detection_classes:0')],
                   feed_dict={'image_tensor:0': inp.reshape(1, inp.shape[0], inp.shape[1], 3)})

     # Visualize detected bounding boxes.
    num_detections = int(out[0][0])
    for i in range(num_detections):
        classId = int(out[3][0][i])
        score = float(out[1][0][i])
        bbox = [float(v) for v in out[2][0][i]]

Get the bounding box coordinates in the TensorFlow object detection API tutorial

在此之后:

out = vis_util.visualize_boxes_and_labels_on_image_array(
image,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=1,
min_score_thresh=0.80)

职位是:

im_height, im_width = image.shape[:2]

position = boxes[0][0]

(xmin, xmax, ymin, ymax) = (position[1]*im_width, position[3]*im_width, position[0]*im_height, position[2]*im_height)

roi = image2[int(ymin):int(ymax), int(xmin):int(xmax)]

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