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evaluate_single.py
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evaluate_single.py
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from tinder import Tinder
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import logging
import time
import json
import requests
import datetime
from random import random
import person_detector
import tensorflow as tf
import numpy as np
class Classifier():
def __init__(self, graph, labels):
self._graph = self.load_graph(graph)
self._labels = self.load_labels(labels)
self._input_operation = self._graph.get_operation_by_name("import/Placeholder")
self._output_operation = self._graph.get_operation_by_name("import/final_result")
self._session = tf.Session(graph=self._graph)
def classify(self, file_name):
t = self.read_tensor_from_image_file(file_name)
# Open up a new tensorflow session and run it on the input
results = self._session.run(self._output_operation.outputs[0], {self._input_operation.outputs[0]: t})
results = np.squeeze(results)
# Sort the output predictions by prediction accuracy
top_k = results.argsort()[-5:][::-1]
result = {}
for i in top_k:
result[self._labels[i]] = results[i]
# Return sorted result tuples
return result
def close(self):
self._session.close()
@staticmethod
def load_graph(model_file):
graph = tf.Graph()
graph_def = tf.GraphDef()
with open(model_file, "rb") as f:
graph_def.ParseFromString(f.read())
with graph.as_default():
tf.import_graph_def(graph_def)
return graph
@staticmethod
def load_labels(label_file):
label = []
proto_as_ascii_lines = tf.gfile.GFile(label_file).readlines()
for l in proto_as_ascii_lines:
label.append(l.rstrip())
return label
@staticmethod
def read_tensor_from_image_file(file_name,
input_height=299,
input_width=299,
input_mean=0,
input_std=255):
input_name = "file_reader"
file_reader = tf.read_file(file_name, input_name)
image_reader = tf.image.decode_jpeg(
file_reader, channels=3, name="jpeg_reader")
float_caster = tf.cast(image_reader, tf.float32)
dims_expander = tf.expand_dims(float_caster, 0)
resized = tf.image.resize_bilinear(dims_expander, [input_height, input_width])
normalized = tf.divide(tf.subtract(resized, [input_mean]), [input_std])
sess = tf.Session()
result = sess.run(normalized)
return result
def predict_likeliness(classifier, sess, default_graph, filename):
img = person_detector.get_person(filename, sess, default_graph)
img = img.convert('L')
img.save(filename, "jpeg")
certainty = classifier.classify(filename)
pos = certainty["positive"]
return pos
detection_graph = person_detector.open_graph()
with detection_graph.as_default():
logging.DEBUG
with tf.Session() as sess:
classifier = Classifier(graph="tf/training_output/retrained_graph.pb",
labels="tf/training_output/retrained_labels.txt")
graph = tf.get_default_graph()
score = predict_likeliness(classifier, sess, graph, 'tmp.jpg')
print(f"SCORE: {score}")