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GANs_inference.py
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GANs_inference.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Created on 2017 10.17
@author: liupeng
wechat: lp9628
blog: http://blog.csdn.net/u014365862/article/details/78422372
"""
import numpy as np
from scipy import misc
import tensorflow as tf
from threading import Lock
import os
import cv2
import sys
from lib.utils.GANs_utils import sample_noise
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import config
model_class = 1
dim = 64
num_gen = 128
def GPU_config(rate=0.99):
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
gpuConfig = tf.ConfigProto()
gpuConfig.allow_soft_placement = False
gpuConfig.gpu_options.allow_growth = True
gpuConfig.gpu_options.per_process_gpu_memory_fraction = rate
return gpuConfig
def prewhiten(self, img):
mean = np.mean(img)
std = np.std(img)
std_adj = np.maximum(std, 1.0/np.sqrt(img.size))
ret = np.multiply(np.subtract(img, mean), 1/std_adj)
return ret
def to_rgb(self,img):
w, h = img.shape
ret = np.empty((w, h, 3), dtype=np.uint8)
ret[:, :, 0] = ret[:, :, 1] = ret[:, :, 2] = img
return ret
def img_crop(img, box):
# y1, x1, y2, x2 = box[1]-20, box[0]-20, box[1]+box[3]+40, box[0]+box[2]+40
x1, y1, x2, y2 = int(box[0]), int(box[1]), int(box[2]), int(box[3])
img = img[y1:y2, x1:x2]
return img
def data_norm(img):
img = img / 255.0
img = img - 0.5
img = img * 2
return img
def dec_data_norm(img):
img = img / 2.
img = img + 0.5
img = img * 255.
return img
class LPAlg_unconditional(object):
# default model path of .pb
PB_PATH_1 = os.path.join(os.getcwd(), "model", "body_pose_model.pb")
PB_PATH = [PB_PATH_1]
CLASS_NUMBER = model_class
def __init__(self, pb_path_1=None, gpu_config=GPU_config()):
def get_path(path,default_path):
return (path, default_path)[path is None]
def load_graph(frozen_graph_filename):
# We load the protobuf file from the disk and parse it to retrieve the
# unserialized graph_def
with tf.gfile.GFile(frozen_graph_filename, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
# Then, we can use again a convenient built-in function to import a graph_def into the
# current default Graph
with tf.Graph().as_default() as graph:
tf.import_graph_def(
graph_def,
input_map=None,
return_elements=None,
name="prefix",
op_dict=None,
producer_op_list=None
)
return graph
# model
def sess_def(pb_path):
print (pb_path)
graph = load_graph(pb_path)
pred = graph.get_tensor_by_name('prefix/predictions:0')
batch_size = tf.placeholder(tf.float32, [None, 1])
label_indices = tf.placeholder(tf.float32, [None, 2])
x1 = graph.get_tensor_by_name('prefix/inputs_placeholder:0')
# x2 = graph.get_tensor_by_name('prefix/inputs_placeholder2:0')
# x3 = graph.get_tensor_by_name('prefix/inputs_placeholder3:0')
sess = tf.Session(graph=graph,config=gpu_config)
return [sess,x1,batch_size,label_indices,pred]
# multiple models
def multi_model_def(pb_path_1):
model_1 = sess_def(pb_path_1)
return [model_1]
path_1 = get_path(pb_path_1, LPAlg_unconditional.PB_PATH[0])
self._pb_path = [path_1]
self.model = multi_model_def(self._pb_path[0])
def _close(self):
self.model[0][0].close()
def _run(self, images_path=None):
idx = 0 # model index
# print (imgs)
sess = tf.Session()
generator_x = sess.run(sample_noise(num_gen, dim))
predict = self.model[idx][0].run(
self.model[idx][4],
feed_dict={self.model[idx][1]: generator_x
# self.model[idx][2]: imgs2,
# self.model[idx][3]: imgs3
}
)
print ('predict:', predict)
return predict.tolist()
class LPAlg_conditional(object):
# default model path of .pb
PB_PATH_1 = os.path.join(os.getcwd(), "model", "body_pose_model.pb")
PB_PATH = [PB_PATH_1]
CLASS_NUMBER = model_class
def __init__(self, pb_path_1=None, gpu_config=GPU_config()):
def get_path(path,default_path):
return (path, default_path)[path is None]
def load_graph(frozen_graph_filename):
# We load the protobuf file from the disk and parse it to retrieve the
# unserialized graph_def
with tf.gfile.GFile(frozen_graph_filename, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
# Then, we can use again a convenient built-in function to import a graph_def into the
# current default Graph
with tf.Graph().as_default() as graph:
tf.import_graph_def(
graph_def,
input_map=None,
return_elements=None,
name="prefix",
op_dict=None,
producer_op_list=None
)
return graph
# model
def sess_def(pb_path):
print (pb_path)
graph = load_graph(pb_path)
pred = graph.get_tensor_by_name('prefix/predictions:0')
batch_size = tf.placeholder(tf.float32, [None, 1])
label_indices = tf.placeholder(tf.float32, [None, 2])
x1 = graph.get_tensor_by_name('prefix/inputs_placeholder0:0')
x2 = graph.get_tensor_by_name('prefix/inputs_placeholder1:0')
# x2 = graph.get_tensor_by_name('prefix/inputs_placeholder2:0')
# x3 = graph.get_tensor_by_name('prefix/inputs_placeholder3:0')
sess = tf.Session(graph=graph,config=gpu_config)
return [sess,x1,x2,batch_size,label_indices,pred]
# multiple models
def multi_model_def(pb_path_1):
model_1 = sess_def(pb_path_1)
return [model_1]
path_1 = get_path(pb_path_1, LPAlg_conditional.PB_PATH[0])
self._pb_path = [path_1]
self.model = multi_model_def(self._pb_path[0])
def _close(self):
self.model[0][0].close()
def _run(self, images_path=None):
idx = 0 # model index
# print (imgs)
sess = tf.Session()
generator_x = sess.run(sample_noise(num_gen, dim))
num_classes = config.num_classes
def sample_label():
num = num_gen
label_vector = np.zeros((num , num_classes), dtype=np.float)
for i in range(0 , num):
label_vector[i , i%4] = 1.0
return label_vector
label = sample_label()
predict = self.model[idx][0].run(
self.model[idx][5],
feed_dict={self.model[idx][1]: generator_x,
self.model[idx][2]: label
# self.model[idx][3]: imgs3
}
)
print ('predict:', predict)
return predict.tolist()
def ProjectInterface(image_path_list, proxy=None):
images_path = image_path_list.keys()
predict = proxy._run(images_path)
return predict
def show_images(images):
# images = np.reshape(images, [images.shape[0], -1]) # images reshape to (batch_size, D)
# sqrtn = int(np.ceil(np.sqrt(images.shape[0])))
# sqrtimg = int(np.ceil(np.sqrt(images.shape[1])))
fig = plt.figure(figsize=(36, 36))
gs = gridspec.GridSpec(36, 36)
gs.update(wspace=0.05, hspace=0.05)
for i, img in enumerate(images):
img = np.asarray(img)
img = dec_data_norm(img)
cv2.imwrite('face0.jpg', img)
img = cv2.imread('face0.jpg')
img=cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
ax = plt.subplot(gs[i])
plt.axis('off')
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_aspect('equal')
# plt.imshow(img.reshape([sqrtimg,sqrtimg]))
plt.imshow(img.reshape([32,32,3]))
if __name__ == "__main__":
# python predict.py lp.jpg (带标签输出逻辑)
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('image', type=str, help='Assign the image path.', default="")
args = parser.parse_args()
arch_model = config.arch_model
if arch_model == "arch_dcgan_unconditional":
alg_core = LPAlg_unconditional(pb_path_1="model/body_pose_model.pb")
elif arch_model == "arch_dcgan_conditional":
alg_core = LPAlg_conditional(pb_path_1="model/body_pose_model.pb")
else:
print ('{} is error!', arch_model)
result_dict = ProjectInterface({args.image: args.image}, proxy=alg_core)
result_dict_img = ((np.asarray(result_dict) / 2. + 0.5) *255) #.reshape([32,32,3])
print(result_dict_img)
cv2.imwrite('face.jpg', result_dict_img[0])
result_dict = np.asarray(result_dict)
show_images(result_dict)
plt.show()