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guess.py
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guess.py
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#code: UTF-8
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
reload(sys)
#sys.setdefaultencoding('utf-8')
from datetime import datetime
import math
import time
from data import inputs
import numpy as np
import tensorflow as tf
from model import select_model, get_checkpoint
from utils import ImageCoder, make_batch
import os
import csv
import random
RESIZE_FINAL = 227
GENDER_LIST =['M','F']
AGE_LIST = ['(0, 2)','(4, 6)','(8, 12)','(15, 20)','(25, 32)','(38, 43)','(48, 53)','(60, 100)']
AGE_MODEL_PATH = '/Users/apple/Desktop/try/age_model'
GENDER_MODEL_PATH = '/Users/apple/Desktop/try/gender_model'
model_checkpoint_path = ''
tf.app.flags.DEFINE_string('model_dir', '',
'Model directory (where training data lives)')
tf.app.flags.DEFINE_string('class_type', 'age',
'Classification type (age|gender)')
tf.app.flags.DEFINE_string('device_id', '/cpu:0',
'What processing unit to execute inference on')
tf.app.flags.DEFINE_string('filename', '',
'File (Image) or File list (Text/No header TSV) to process')
tf.app.flags.DEFINE_string('target', '',
'CSV file containing the filename processed along with best guess and score')
tf.app.flags.DEFINE_string('checkpoint', 'checkpoint',
'Checkpoint basename')
tf.app.flags.DEFINE_string('model_type', 'default',
'Type of convnet')
tf.app.flags.DEFINE_string('requested_step', '', 'Within the model directory, a requested step to restore e.g., 9000')
tf.app.flags.DEFINE_boolean('single_look', False, 'single look at the image or multiple crops')
tf.app.flags.DEFINE_string('face_detection_model', '', 'Do frontal face detection with model specified')
FLAGS = tf.app.flags.FLAGS
def one_of(fname, types):
for ty in types:
if fname.endswith('.' + ty):
return True
return False
def resolve_file(fname):
if os.path.exists(fname): return fname
for suffix in ('.jpg', '.png', '.JPG', '.PNG', '.jpeg'):
cand = fname + suffix
if os.path.exists(cand):
return cand
return None
def classify(sess, label_list, softmax_output, coder, images, image_file):
print('Running file %s' % image_file)
image_batch = make_batch(image_file, coder, not FLAGS.single_look)
batch_results = sess.run(softmax_output, feed_dict={images:image_batch.eval()})
output = batch_results[0]
batch_sz = batch_results.shape[0]
for i in range(1, batch_sz):
output = output + batch_results[i]
output /= batch_sz
best = np.argmax(output)
best_choice = (label_list[best], output[best])
print('Guess @ 1 %s, prob = %.2f' % best_choice)
#calculate face score
score = scoreAge(output)
nlabels = len(label_list)
if nlabels > 2:
output[best] = 0
second_best = np.argmax(output)
print('Guess @ 2 %s, prob = %.2f' % (label_list[second_best], output[second_best]))
return label_list[best],score
def classifyGender(sess, label_list, softmax_output, coder, images, image_file):
print('Running file %s' % image_file)
image_batch = make_batch(image_file, coder, not FLAGS.single_look)
batch_results = sess.run(softmax_output, feed_dict={images:image_batch.eval()})
output = batch_results[0]
batch_sz = batch_results.shape[0]
for i in range(1, batch_sz):
output = output + batch_results[i]
output /= batch_sz
best = np.argmax(output)
best_choice = (label_list[best], output[best])
print('Guess @ 1 %s, prob = %.2f' % best_choice)
#calculate face score
#score = scoreAge(output)
nlabels = len(label_list)
if nlabels > 2:
output[best] = 0
second_best = np.argmax(output)
print('Guess @ 2 %s, prob = %.2f' % (label_list[second_best], output[second_best]))
return label_list[best]
def scoreAge(prop_list):
#['(0, 2)', '(4, 6)', '(8, 12)', '(15, 20)', '(25, 32)', '(38, 43)', '(48, 53)', '(60, 100)']
#[90, 80, 80, 70, 60, 50, 40, 20]
score_list = [100, 100, 95, 90, 80, 50, 40, 20]
random_score_list = []
finalScore = 0
for score in score_list:
randScore = score + random.random() * 10
random_score_list.append(randScore)
j = 0
while(j < len(random_score_list)):
prop = prop_list[j]
score = random_score_list[j]
finalScore = finalScore + score * prop
j = j + 1
return finalScore
def batchlist(srcfile):
with open(srcfile, 'r') as csvfile:
reader = csv.reader(csvfile)
if srcfile.endswith('.csv') or srcfile.endswith('.tsv'):
print('skipping header')
reader.next()
return [row[0] for row in reader]
# def detectface(filename):
# files = []
# #print('Using face detector %s' % FLAGS.face_detection_model)
# face_detect = FaceDetector('haarcascade_frontalface_default.xml')
# face_files, rectangles = face_detect.run(filename)
# files += face_files
# if (len(files)>0) :
# return 1
# else:
# return 0
def guessAge(image_file):
#import!!!Fix the bug http://stackoverflow.com/questions/33765336/remove-nodes-from-graph-or-reset-entire-default-graph
tf.reset_default_graph()
with tf.Session() as sess:
age_label_list = AGE_LIST
agelabels = len(age_label_list)
# print('Executing on %s' % FLAGS.device_id)
model_fn = select_model('inception')
images = tf.placeholder(tf.float32, [None, RESIZE_FINAL, RESIZE_FINAL, 3])
logits_age = model_fn(agelabels, images, 1, False)
init = tf.global_variables_initializer()
requested_step = FLAGS.requested_step if FLAGS.requested_step else None
checkpoint_path = '%s' % (AGE_MODEL_PATH)
# update in 0.11 version
model_checkpoint_path, global_step = get_checkpoint(checkpoint_path, requested_step, FLAGS.checkpoint)
#print 'model_checkpoint_path is', model_checkpoint_path
#print model_checkpoint_path
saver = tf.train.Saver()
if not saver.last_checkpoints :
saver.restore(sess, model_checkpoint_path)
softmax_output = tf.nn.softmax(logits_age)
coder = ImageCoder()
files = []
# detect age
best_choice = classify(sess, age_label_list, softmax_output, coder, images, image_file)
sess.close()
return best_choice
def guessGender(image_file):
tf.reset_default_graph()
with tf.Session() as sess:
#sess = tf.Session()
age_label_list = AGE_LIST
gender_label_list = GENDER_LIST
genderlabels = len(gender_label_list)
# print('Executing on %s' % FLAGS.device_id)
model_fn = select_model('')
with tf.device(FLAGS.device_id):
images = tf.placeholder(tf.float32, [None, RESIZE_FINAL, RESIZE_FINAL, 3])
logits_gender = model_fn(genderlabels, images, 1, False)
init = tf.global_variables_initializer()
requested_step = FLAGS.requested_step if FLAGS.requested_step else None
checkpoint_path = '%s' % (GENDER_MODEL_PATH)
model_checkpoint_path, global_step = get_checkpoint(checkpoint_path, requested_step, FLAGS.checkpoint)
saver = tf.train.Saver()
saver.restore(sess, model_checkpoint_path)
softmax_output = tf.nn.softmax(logits_gender)
coder = ImageCoder()
files = []
# detect gender
#try:
best_choice = classifyGender(sess, gender_label_list, softmax_output, coder, images, image_file)
return best_choice
#except Exception as e:
# print(e)
# print('Failed to run image %s ' % image_file)