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# coding=utf-8
# date:
import tensorflow as tf
import numpy as np
import cv2
import os
if tf.__version__ >= '2.0':
print("tf version >= 2.0")
tf = tf.compat.v1
tf.disable_eager_execution()
class human_segmenter(object):
def __init__(self, model_path,is_encrypted_model=False):
super(human_segmenter, self).__init__()
f = tf.gfile.FastGFile(model_path, 'rb')
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
persisted_graph = tf.import_graph_def(graph_def, name='')
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.3 # 占用GPU 30%的显存
self.sess = tf.InteractiveSession(graph=persisted_graph, config=config)
# self.image_node = self.sess.graph.get_tensor_by_name("input_image:0")
# # self.output_node = self.sess.graph.get_tensor_by_name("output_png:0")
# # check if the nodename in model
# if "output_png:0" in self.sess.graph_def.node:
# self.output_node = self.sess.graph.get_tensor_by_name("output_png:0")
# else:
# self.output_node = self.sess.graph.get_tensor_by_name("output_alpha:0")
# if "if_person:0" in self.sess.graph_def.node:
# self.logits_node = self.sess.graph.get_tensor_by_name("if_person:0")
print("human_segmenter init done")
def image_preprocess(self, img):
if len(img.shape) == 2:
img = np.dstack((img, img, img))
elif img.shape[2] == 4:
img = img[:, :, :3]
img = img[:, :, ::-1]
img = img.astype(np.float32)
return img
def run(self, img):
image_feed = self.image_preprocess(img)
output_img_value, logits_value = self.sess.run([self.sess.graph.get_tensor_by_name("output_png:0"), self.sess.graph.get_tensor_by_name("if_person:0")],
feed_dict={self.sess.graph.get_tensor_by_name("input_image:0"): image_feed})
# mask = output_img_value[:,:,-1]
output_img_value = cv2.cvtColor(output_img_value, cv2.COLOR_RGBA2BGRA)
return output_img_value
def run_head(self, img):
image_feed = self.image_preprocess(img)
# image_feed = image_feed/255.0
output_alpha = self.sess.run(self.sess.graph.get_tensor_by_name('output_alpha:0'),
feed_dict={'input_image:0': image_feed})
return output_alpha
def get_human_bbox(self, mask):
'''
:param mask:
:return: [x,y,w,h]
'''
print('dtype:{}, max:{},shape:{}'.format(mask.dtype, np.max(mask), mask.shape))
ret, thresh = cv2.threshold(mask,127,255,0)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
if len(contours) == 0:
return None
contoursArea = [cv2.contourArea(c) for c in contours]
max_area_index = contoursArea.index(max(contoursArea))
bbox = cv2.boundingRect(contours[max_area_index])
return bbox
def release(self):
self.sess.close()
class head_segmenter(object):
def __init__(self, model_path, is_encrypted_model=False):
super(head_segmenter, self).__init__()
f = tf.gfile.FastGFile(model_path, 'rb')
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
persisted_graph = tf.import_graph_def(graph_def, name='')
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.3 # 占用GPU 30%的显存
self.sess = tf.InteractiveSession(graph=persisted_graph, config=config)
print("human_segmenter init done")
def image_preprocess(self, img):
if len(img.shape) == 2:
img = np.dstack((img, img, img))
elif img.shape[2] == 4:
img = img[:, :, :3]
img = img[:, :, ::-1]
img = img.astype(np.float32)
return img
def run_head(self, img):
image_feed = self.image_preprocess(img)
# image_feed = image_feed/255.0
output_alpha = self.sess.run(self.sess.graph.get_tensor_by_name('output_alpha:0'),
feed_dict={'input_image:0': image_feed})
return output_alpha
def get_human_bbox(self, mask):
'''
:param mask:
:return: [x,y,w,h]
'''
print('dtype:{}, max:{},shape:{}'.format(mask.dtype, np.max(mask), mask.shape))
ret, thresh = cv2.threshold(mask, 127, 255, 0)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
if len(contours) == 0:
return None
contoursArea = [cv2.contourArea(c) for c in contours]
max_area_index = contoursArea.index(max(contoursArea))
bbox = cv2.boundingRect(contours[max_area_index])
return bbox
def release(self):
self.sess.close()
class hair_segmenter(object):
def __init__(self, model_dir, is_encrypted_model=False):
head_path = os.path.join(model_dir, 'Matting_headparser_6_18.pb')
face_path = os.path.join(model_dir, 'segment_face.pb')
detect_path = os.path.join(model_dir, 'face_detect.pb')
self.sess = self.load_sess(head_path)
image = np.ones((512, 512, 3))
output_png = self.sess.run(self.sess.graph.get_tensor_by_name('output_alpha:0'),
feed_dict={'input_image:0': image})
self.sess_detect = self.load_sess(detect_path)
oboxes, scores, num_detections = self.sess_detect.run(
[self.sess_detect.graph.get_tensor_by_name('tower_0/boxes:0'),
self.sess_detect.graph.get_tensor_by_name('tower_0/scores:0'),
self.sess_detect.graph.get_tensor_by_name('tower_0/num_detections:0')],
feed_dict={'tower_0/images:0': image[np.newaxis], 'training_flag:0': False})
faceRects = []
self.sess_face = self.load_sess(face_path)
image = np.ones((512, 512, 3))
output_alpha = self.sess_face.run(self.sess_face.graph.get_tensor_by_name('output_alpha_face:0'),
feed_dict={'input_image_face:0': image})
def load_sess(self, model_path):
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
with tf.gfile.FastGFile(model_path, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
sess.graph.as_default()
tf.import_graph_def(graph_def, name='')
sess.run(tf.global_variables_initializer())
return sess
def image_preprocess(self, img):
if len(img.shape) == 2:
img = np.dstack((img, img, img))
elif img.shape[2] == 4:
img = img[:, :, :3]
img = img[:, :, ::-1]
img = img.astype(np.float32)
return img
def run_head(self, image):
image_feed = self.image_preprocess(image)
output_img_value = self.sess.run(self.sess.graph.get_tensor_by_name('output_alpha:0'),
feed_dict={'input_image:0': image_feed})
# mask = output_img_value[:,:,-1]
output_img_value = cv2.cvtColor(output_img_value, cv2.COLOR_RGBA2BGRA)
return output_img_value
def run(self, image):
h, w, c = image.shape
faceRects = self.detect_face(image)
face_num = len(faceRects)
print('face_num:{}'.format(face_num))
all_head_alpha = []
all_face_mask = []
for i in range(face_num):
y1 = faceRects[i][0]
y2 = faceRects[i][1]
x1 = faceRects[i][2]
x2 = faceRects[i][3]
pad_y1, pad_y2, pad_x1, pad_x2 = self.pad_box(y1, y2, x1, x2, 0.15, 0.15, 0.15, 0.15, h, w)
temp_img = image.copy()
roi_img = temp_img[pad_y1:pad_y2, pad_x1:pad_x2]
output_alpha = self.sess_face.run(self.sess_face.graph.get_tensor_by_name('output_alpha_face:0'),
feed_dict={'input_image_face:0': roi_img[:, :, ::-1]})
face_mask = np.zeros((h, w, 3))
face_mask[pad_y1:pad_y2, pad_x1:pad_x2] = output_alpha
all_face_mask.append(face_mask)
# cv2.imwrite(str(i)+'face.jpg',face_mask)
# cv2.imwrite(str(i)+'face_roi.jpg',roi_img)
for i in range(face_num):
y1 = faceRects[i][0]
y2 = faceRects[i][1]
x1 = faceRects[i][2]
x2 = faceRects[i][3]
pad_y1, pad_y2, pad_x1, pad_x2 = self.pad_box(y1, y2, x1, x2, 1.47, 1.47, 1.3, 2.0, h, w)
temp_img = image.copy()
for j in range(face_num):
y1 = faceRects[j][0]
y2 = faceRects[j][1]
x1 = faceRects[j][2]
x2 = faceRects[j][3]
small_y1, small_y2, small_x1, small_x2 = self.pad_box(y1, y2, x1, x2, -0.1, -0.1, -0.1, -0.1, h, w)
small_width = small_x2 - small_x1
small_height = small_y2 - small_y1
if (
small_x1 < 0 or small_y1 < 0 or small_width < 3 or small_height < 3 or small_x2 > w or small_y2 > h):
continue
# if(i!=j):
# temp_img[small_y1:small_y2,small_x1:small_x2]=0
if (i != j):
temp_img = temp_img * (1.0 - all_face_mask[j] / 255.0)
roi_img = temp_img[pad_y1:pad_y2, pad_x1:pad_x2]
output_alpha = self.sess.run(self.sess.graph.get_tensor_by_name('output_alpha:0'),
feed_dict={'input_image:0': roi_img[:, :, ::-1]})
head_alpha = np.zeros((h, w))
head_alpha[pad_y1:pad_y2, pad_x1:pad_x2] = output_alpha[:, :, 2]
all_head_alpha.append(head_alpha)
print('all_head_alpha', all_head_alpha)
# return all_head_alpha[0]
def detect_face(self, img):
h, w, c = img.shape
input_img = cv2.resize(img[:, :, ::-1], (512, 512))
boxes, scores, num_detections = self.sess_detect.run(
[self.sess_detect.graph.get_tensor_by_name('tower_0/boxes:0'),
self.sess_detect.graph.get_tensor_by_name('tower_0/scores:0'),
self.sess_detect.graph.get_tensor_by_name('tower_0/num_detections:0')],
feed_dict={'tower_0/images:0': input_img[np.newaxis], 'training_flag:0': False})
faceRects = []
for i in range(num_detections[0]):
if scores[0, i] < 0.5:
continue
y1 = np.int(boxes[0, i, 0] * h)
x1 = np.int(boxes[0, i, 1] * w)
y2 = np.int(boxes[0, i, 2] * h)
x2 = np.int(boxes[0, i, 3] * w)
if x2 <= x1 + 3 or y2 <= y1 + 3:
continue
faceRects.append((y1, y2, x1, x2, y2 - y1, x2 - x1))
sorted(faceRects, key=lambda x: x[4] * x[5], reverse=True)
return faceRects
def pad_box(self, y1, y2, x1, x2, left_ratio, right_ratio, top_ratio, bottom_ratio, h, w):
box_w = x2 - x1
box_h = y2 - y1
pad_y1 = np.maximum(np.int(y1 - top_ratio * box_h), 0)
pad_y2 = np.minimum(np.int(y2 + bottom_ratio * box_h), h - 1)
pad_x1 = np.maximum(np.int(x1 - left_ratio * box_w), 0)
pad_x2 = np.minimum(np.int(x2 + right_ratio * box_w), w - 1)
return pad_y1, pad_y2, pad_x1, pad_x2
if __name__ == "__main__":
img = cv2.imread('12345/images/0001.jpg')
print(img.shape)
fp = human_segmenter(model_path='assets/matting_human.pb')
rgba = fp.run(img)
# cv2.imwrite("human_mask1.png",mask)
print("test done")
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