TF Inference Script

inference.py

scores = detection_graph.get_tensor_by_name('detection_scores:0')
classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
(boxes, scores, classes, num_detections) = sess.run([boxes, scores, classes, num_detections], feed_dict={image_tensor: image_np_expanded})

result = []
for i in range(len(classes[0])):
    if scores[0][i] >= 0.5:
        xmin, ymin, xmax, ymax = _normalize_box(boxes[0][i], width, height)
        label = CLASSES[classes[0][i]]
        result.append({'label':label, 'xmin': xmin, 'ymin':ymin, 'xmax':xmax, 'ymax':ymax})
output['annotations'][imagePath]  = result