60 lines
2.0 KiB
Python
60 lines
2.0 KiB
Python
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"""ssd_tf.py
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This module implements the TfSSD class.
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"""
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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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def _postprocess_tf(img, boxes, scores, classes, conf_th):
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"""Postprocess TensorFlow SSD output."""
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h, w, _ = img.shape
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out_boxes = boxes[0] * np.array([h, w, h, w])
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out_boxes = out_boxes.astype(np.int32)
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out_boxes = out_boxes[:, [1, 0, 3, 2]] # swap x's and y's
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out_confs = scores[0]
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out_clss = classes[0].astype(np.int32)
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# only return bboxes with confidence score above threshold
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mask = np.where(out_confs >= conf_th)
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return out_boxes[mask], out_confs[mask], out_clss[mask]
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class TfSSD(object):
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"""TfSSD class encapsulates things needed to run TensorFlow SSD."""
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def __init__(self, model, input_shape):
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self.model = model
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self.input_shape = input_shape
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# load detection graph
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ssd_graph = tf.Graph()
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with ssd_graph.as_default():
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graph_def = tf.GraphDef()
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with tf.gfile.GFile('ssd/%s.pb' % model, 'rb') as fid:
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serialized_graph = fid.read()
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graph_def.ParseFromString(serialized_graph)
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tf.import_graph_def(graph_def, name='')
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# define input/output tensors
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self.image_tensor = ssd_graph.get_tensor_by_name('image_tensor:0')
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self.det_boxes = ssd_graph.get_tensor_by_name('detection_boxes:0')
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self.det_scores = ssd_graph.get_tensor_by_name('detection_scores:0')
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self.det_classes = ssd_graph.get_tensor_by_name('detection_classes:0')
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# create the session for inferencing
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self.sess = tf.Session(graph=ssd_graph)
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def __del__(self):
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self.sess.close()
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def detect(self, img, conf_th):
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img_resized = _preprocess_tf(img, self.input_shape)
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boxes, scores, classes = self.sess.run(
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[self.det_boxes, self.det_scores, self.det_classes],
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feed_dict={self.image_tensor: np.expand_dims(img_resized, 0)})
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return _postprocess_tf(img, boxes, scores, classes, conf_th)
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