深度有趣 | 27 服飾關鍵點定位

摘要： 關鍵點定位是一類常見而有用的任務，某種意義上可以理解為一種特徵工程 人臉關鍵點定位，可用於人臉識別、表情識別 人體骨骼關鍵點定位，可用於姿態估計 手部關鍵點定位，可用於手勢識別 輸入是一張圖片，輸出是每個關鍵點的x、y座標，一般會歸一化到0～1區間中，所以可以...

關鍵點定位是一類常見而有用的任務，某種意義上可以理解為一種特徵工程

• 人臉關鍵點定位，可用於人臉識別、表情識別
• 人體骨骼關鍵點定位，可用於姿態估計
• 手部關鍵點定位，可用於手勢識別

輸入是一張圖片，輸出是每個關鍵點的x、y座標，一般會歸一化到0～1區間中，所以可以理解為迴歸問題

但是直接對座標值進行迴歸會導致較大誤差，更好的做法是輸出一個低解析度的熱圖，使得關鍵點所在位置輸出較高響應，而其他位置則輸出較低響應

CPM（Convolutional Pose Machines）的基本思想是使用多個級聯的stage，每個stage包含多個CNN並且都輸出熱圖

通過最小化每個stage的熱圖和ground truth之間的差距，從而得到越來越準確的關鍵點定位結果

資料

使用天池FashionAI全球挑戰賽提供的資料， ofollow,noindex">fashionai.alibaba.com/

其中服飾關鍵點定位賽題提供的訓練集包括4W多張圖片，測試集包括將近1W張圖片

每張圖片都指定了對應的服飾類別，共5類：上衣（blouse）、外套（outwear）、連身裙（dress）、半身裙（skirt）、褲子（trousers）

訓練集還提供了每張圖片對應的24個關鍵點的標註，包括x座標、y座標、是否可見三項資訊，但並不是每類服飾都有24個關鍵點

關於以上資料的更多介紹可以參考以下文章， zhuanlan.zhihu.com/p/34928763

為了簡化問題，以下僅使用dress類別的訓練集資料訓練CPM模型

實現

載入庫

# -*- coding: utf-8 -*-

import tensorflow as tf
import numpy as np
import pandas as pd
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split
import cv2
import matplotlib.pyplot as plt
%matplotlib inline
from imageio import imread, imsave
import os
import glob
from tqdm import tqdm
import warnings
warnings.filterwarnings('ignore')
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載入訓練集和測試集

train = pd.read_csv(os.path.join('data', 'train', 'train.csv'))
train_warm = pd.read_csv(os.path.join('data', 'train_warm', 'train_warm.csv'))
test = pd.read_csv(os.path.join('data', 'test', 'test.csv'))
print(len(train), len(train_warm), len(test))

columns = train.columns
print(len(columns), columns)

train['image_id'] = train['image_id'].apply(lambda x:os.path.join('train', x))
train_warm['image_id'] = train_warm['image_id'].apply(lambda x:os.path.join('train_warm', x))
train = pd.concat([train, train_warm])
del train_warm
train.head()
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僅保留dress類別的資料

train = train[train.image_category == 'dress']
test = test[test.image_category == 'dress']
print(len(train), len(test))
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拆分標註資訊中的x座標、y座標、是否可見

for col in columns:
if col in ['image_id', 'image_category']:
continue
train[col + '_x'] = train[col].apply(lambda x:float(x.split('_')[0]))
train[col + '_y'] = train[col].apply(lambda x:float(x.split('_')[1]))
train[col + '_s'] = train[col].apply(lambda x:float(x.split('_')[2]))
train.drop([col], axis=1, inplace=True)
train.head()
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將x座標和y座標進行歸一化

features = [
'neckline_left', 'neckline_right', 'center_front', 'shoulder_left', 'shoulder_right', 
'armpit_left', 'armpit_right', 'waistline_left', 'waistline_right', 
'cuff_left_in', 'cuff_left_out', 'cuff_right_in', 'cuff_right_out', 'hemline_left', 'hemline_right']

train = train.to_dict('records')
for i in tqdm(range(len(train))):
record = train[i]
img = imread(os.path.join('data', record['image_id']))
h = img.shape[0]
w = img.shape[1]
for col in features:
if record[col + '_s'] >= 0:
train[i][col + '_x'] /= w
train[i][col + '_y'] /= h
else:
train[i][col + '_x'] = 0
train[i][col + '_y'] = 0
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隨機選一些訓練資料並繪圖檢視

img_size = 256
r = 10
c = 10
puzzle = np.ones((img_size * r, img_size * c, 3))
random_indexs = np.random.choice(len(train), 100)
for i in range(100):
record = train[random_indexs[i]]
img = imread(os.path.join('data', record['image_id']))
img = cv2.resize(img, (img_size, img_size))
for col in features:
if record[col + '_s'] >= 0:
cv2.circle(img, (int(img_size * record[col + '_x']), int(img_size * record[col + '_y'])), 3, (120, 240, 120), 2)
img = img / 255.
r = i // 10
c = i % 10
puzzle[r * img_size: (r + 1) * img_size, c * img_size: (c + 1) * img_size, :] = img
plt.figure(figsize=(15, 15))
plt.imshow(puzzle)
plt.show()
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整理資料並分割訓練集和驗證集

X_train = []
Y_train = []
for i in tqdm(range(len(train))):
record = train[i]
img = imread(os.path.join('data', record['image_id']))
img = cv2.resize(img, (img_size, img_size))

y = []
for col in features:
y.append([record[col + '_x'], record[col + '_y']])

X_train.append(img)
Y_train.append(y)

X_train = np.array(X_train)
Y_train = np.array(Y_train)
print(X_train.shape)
print(Y_train.shape)

X_train, X_valid, Y_train, Y_valid = train_test_split(X_train, Y_train, test_size=0.1)
print(X_train.shape, Y_train.shape)
print(X_valid.shape, Y_valid.shape)
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定義一些引數和模型輸入

batch_size = 16
heatmap_size = 32
stages = 6
y_dim = Y_train.shape[1]

X = tf.placeholder(tf.float32, [None, img_size, img_size, 3], name='X')
Y = tf.placeholder(tf.float32, [None, heatmap_size, heatmap_size, y_dim + 1], name='Y')

def conv2d(inputs, filters, kernel_size, padding='same', activation=tf.nn.relu, name=''):
if name:
return tf.layers.conv2d(inputs, filters=filters, kernel_size=kernel_size, strides=1, padding=padding, 
activation=activation, name=name, kernel_initializer=tf.contrib.layers.xavier_initializer())
else:
return tf.layers.conv2d(inputs, filters=filters, kernel_size=kernel_size, strides=1, padding=padding, 
activation=activation, kernel_initializer=tf.contrib.layers.xavier_initializer())

def maxpool2d(inputs):
return tf.layers.max_pooling2d(inputs, pool_size=2, strides=2, padding='valid')
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定義CPM模型，使用6個stage

stage_heatmaps = []

# sub_stage
h0 = maxpool2d(conv2d(conv2d(X, 64, 3), 64, 3))
h0 = maxpool2d(conv2d(conv2d(h0, 128, 3), 128, 3))
h0 = maxpool2d(conv2d(conv2d(conv2d(conv2d(h0, 256, 3), 256, 3), 256, 3), 256, 3))
for i in range(6):
h0 = conv2d(h0, 512, 3)
sub_stage = conv2d(h0, 128, 3) # batch_size, 32, 32, 128

# stage_1
h0 = conv2d(sub_stage, 512, 1, padding='valid')
h0 = conv2d(h0, y_dim + 1, 1, padding='valid', activation=None, name='stage_1')
stage_heatmaps.append(h0)

# other stages
for stage in range(2, stages + 1):
h0 = tf.concat([stage_heatmaps[-1], sub_stage], axis=3)
for i in range(5):
h0 = conv2d(h0, 128, 7)
h0 = conv2d(h0, 128, 1, padding='valid')
h0 = conv2d(h0, y_dim + 1, 1, padding='valid', activation=None, name='stage_%d' % stage)
stage_heatmaps.append(h0)
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定義損失函式和優化器

global_step = tf.Variable(0, trainable=False)
learning_rate = tf.train.exponential_decay(0.001, global_step=global_step, decay_steps=1000, decay_rate=0.9)

losses = [0 for _ in range(stages)]
total_loss = 0
for stage in range(stages):
losses[stage] = tf.losses.mean_squared_error(Y, stage_heatmaps[stage])
total_loss += losses[stage]

total_loss_with_reg = total_loss + tf.contrib.layers.apply_regularization(tf.contrib.layers.l2_regularizer(1e-10), tf.trainable_variables())
total_loss = total_loss / stages
total_loss_with_reg = total_loss_with_reg / stages

optimizer = tf.contrib.layers.optimize_loss(total_loss_with_reg, global_step=global_step, learning_rate=learning_rate, 
optimizer='Adam', increment_global_step=True)
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由於訓練資料較少，因此定義一個數據增強的函式

def transform(X_batch, Y_batch):
X_data = []
Y_data = []

offset = 20
for i in range(X_batch.shape[0]):
img = X_batch[i]
# random rotation
degree = int(np.random.random() * offset - offset / 2)
rad = degree / 180 * np.pi
mat = cv2.getRotationMatrix2D((img_size / 2, img_size / 2), degree, 1)
img_ = cv2.warpAffine(img, mat, (img_size, img_size), borderValue=(255, 255, 255))
# random translation
x0 = int(np.random.random() * offset - offset / 2)
y0 = int(np.random.random() * offset - offset / 2)
mat = np.float32([[1, 0, x0], [0, 1, y0]])
img_ = cv2.warpAffine(img_, mat, (img_size, img_size), borderValue=(255, 255, 255))
# random flip
if np.random.random() > 0.5:
img_ = np.fliplr(img_)
flip = True
else:
flip = False

X_data.append(img_)

points = []
for j in range(y_dim):
x = Y_batch[i, j, 0] * img_size
y = Y_batch[i, j, 1] * img_size
# random rotation
dx = x - img_size / 2
dy = y - img_size / 2
x = int(dx * np.cos(rad) + dy * np.sin(rad) + img_size / 2)
y = int(-dx * np.sin(rad) + dy * np.cos(rad) + img_size / 2)
# random translation
x += x0
y += y0

x = x / img_size
y = y / img_size
points.append([x, y])
# random flip
if flip:
data = {features[j]: points[j] for j in range(y_dim)}
points = []
for j in range(y_dim):
col = features[j]
if col.find('left') >= 0:
col = col.replace('left', 'right')
elif col.find('right') >= 0:
col = col.replace('right', 'left')
[x, y] = data[col]
x = 1 - x
points.append([x, y])

Y_data.append(points)

X_data = np.array(X_data)
Y_data = np.array(Y_data)

# preprocess
X_data = (X_data / 255. - 0.5) * 2
Y_heatmap = []
for i in range(Y_data.shape[0]):
heatmaps = []
invert_heatmap = np.ones((heatmap_size, heatmap_size))
for j in range(Y_data.shape[1]):
x0 = int(Y_data[i, j, 0] * heatmap_size)
y0 = int(Y_data[i, j, 1] * heatmap_size)
x = np.arange(0, heatmap_size, 1, float)
y = x[:, np.newaxis]
cur_heatmap = np.exp(-((x - x0) ** 2 + (y - y0) ** 2) / (2.0 * 1.0 ** 2))
heatmaps.append(cur_heatmap)
invert_heatmap -= cur_heatmap
heatmaps.append(invert_heatmap)
Y_heatmap.append(heatmaps)
Y_heatmap = np.array(Y_heatmap)
Y_heatmap = np.transpose(Y_heatmap, (0, 2, 3, 1)) # batch_size, heatmap_size, heatmap_size, y_dim + 1

return X_data, Y_data, Y_heatmap
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檢視資料增強之後的圖片和關鍵點是否正確

X_batch = X_train[:batch_size]
Y_batch = Y_train[:batch_size]
X_data, Y_data, Y_heatmap = transform(X_batch, Y_batch)

n = int(np.sqrt(batch_size))
puzzle = np.ones((img_size * n, img_size * n, 3))
for i in range(batch_size):
img = (X_data[i] + 1) / 2
for j in range(y_dim):
cv2.circle(img, (int(img_size * Y_data[i, j, 0]), int(img_size * Y_data[i, j, 1])), 3, (120, 240, 120), 2)
r = i // n
c = i % n
puzzle[r * img_size: (r + 1) * img_size, c * img_size: (c + 1) * img_size, :] = img
plt.figure(figsize=(12, 12))
plt.imshow(puzzle)
plt.show()
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可以看到，在經過隨機旋轉、隨機平移、隨機水平翻轉之後，圖片和關鍵點依舊一一對應

訓練模型，使用early stopping

sess = tf.Session()
sess.run(tf.global_variables_initializer())

OUTPUT_DIR = 'samples'
if not os.path.exists(OUTPUT_DIR):
os.mkdir(OUTPUT_DIR)

for stage in range(stages):
tf.summary.scalar('loss/loss_stage_%d' % (stage + 1), losses[stage])
tf.summary.scalar('loss/total_loss', total_loss)
summary = tf.summary.merge_all()
writer = tf.summary.FileWriter(OUTPUT_DIR)

loss_valid_min = np.inf
saver = tf.train.Saver()

epochs = 100
patience = 10
for e in range(epochs):
loss_train = []
loss_valid = []

X_train, Y_train = shuffle(X_train, Y_train)
for i in tqdm(range(X_train.shape[0] // batch_size)):
X_batch = X_train[i * batch_size: (i + 1) * batch_size, :, :, :]
Y_batch = Y_train[i * batch_size: (i + 1) * batch_size, :, :]
X_data, Y_data, Y_heatmap = transform(X_batch, Y_batch)
_, ls, lr, stage_heatmaps_ = sess.run([optimizer, total_loss, learning_rate, stage_heatmaps], 
feed_dict={X: X_data, Y: Y_heatmap})
loss_train.append(ls)

if i > 0 and i % 100 == 0:
writer.add_summary(sess.run(summary, feed_dict={X: X_data, Y: Y_heatmap}), 
e * X_train.shape[0] // batch_size + i)
writer.flush()
loss_train = np.mean(loss_train)

demo_img = (X_data[0] + 1) / 2
demo_heatmaps = []
for stage in range(stages):
demo_heatmap = stage_heatmaps_[stage][0, :, :, :y_dim].reshape((heatmap_size, heatmap_size, y_dim))
demo_heatmap = cv2.resize(demo_heatmap, (img_size, img_size))
demo_heatmap = np.amax(demo_heatmap, axis=2)
demo_heatmap = np.reshape(demo_heatmap, (img_size, img_size, 1))
demo_heatmap = np.repeat(demo_heatmap, 3, axis=2)
demo_heatmaps.append(demo_heatmap)

demo_gt_heatmap = Y_heatmap[0, :, :, :y_dim].reshape((heatmap_size, heatmap_size, y_dim))
demo_gt_heatmap = cv2.resize(demo_gt_heatmap, (img_size, img_size))
demo_gt_heatmap = np.amax(demo_gt_heatmap, axis=2)
demo_gt_heatmap = np.reshape(demo_gt_heatmap, (img_size, img_size, 1))
demo_gt_heatmap = np.repeat(demo_gt_heatmap, 3, axis=2)

upper_img = np.concatenate((demo_heatmaps[0], demo_heatmaps[1], demo_heatmaps[2]), axis=1)
blend_img = 0.5 * demo_img + 0.5 * demo_gt_heatmap
lower_img = np.concatenate((demo_heatmaps[-1], demo_gt_heatmap, blend_img), axis=1)
demo_img = np.concatenate((upper_img, lower_img), axis=0)
imsave(os.path.join(OUTPUT_DIR, 'sample_%d.jpg' % e), demo_img)

X_valid, Y_valid = shuffle(X_valid, Y_valid)
for i in range(X_valid.shape[0] // batch_size):
X_batch = X_valid[i * batch_size: (i + 1) * batch_size, :, :, :]
Y_batch = Y_valid[i * batch_size: (i + 1) * batch_size, :, :]
X_data, Y_data, Y_heatmap = transform(X_batch, Y_batch)
ls = sess.run(total_loss, feed_dict={X: X_data, Y: Y_heatmap})
loss_valid.append(ls)
loss_valid = np.mean(loss_valid)

print('Epoch %d, lr %.6f, train loss %.6f, valid loss %.6f' % (e, lr, loss_train, loss_valid))
if loss_valid < loss_valid_min:
print('Saving model...')
saver.save(sess, os.path.join(OUTPUT_DIR, 'cpm'))
loss_valid_min = loss_valid
patience = 10
else:
patience -= 1
if patience == 0:
break
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經過87個epoch之後訓練提前停止，訓練集損失為0.001228，驗證集損失為0.001871，驗證集最低損失為0.001821

訓練集結果如下，上面三張圖依次為stage1、stage2、stage3的結果，下面三張圖依次為stage6、ground truth、groud truth和原圖

在本機上使用以下程式碼，對測試集中的圖片進行關鍵點定位

# -*- coding: utf-8 -*-

import tensorflow as tf
import numpy as np
import pandas as pd
from sklearn.utils import shuffle
import cv2
import matplotlib.pyplot as plt
from imageio import imread, imsave
import os

test = pd.read_csv(os.path.join('data', 'test', 'test.csv'))
test['image_id'] = test['image_id'].apply(lambda x:os.path.join('test', x))
test = test[test.image_category == 'dress']
test = test['image_id'].values

batch_size = 16
img_size = 256
test = shuffle(test)
test = test[:batch_size]
X_test = []
for i in range(batch_size):
img = imread(os.path.join('data', test[i]))
img = cv2.resize(img, (img_size, img_size))
X_test.append(img)
X_test = np.array(X_test)
print(X_test.shape)

sess = tf.Session()
sess.run(tf.global_variables_initializer())

OUTPUT_DIR = 'samples'
saver = tf.train.import_meta_graph(os.path.join(OUTPUT_DIR, 'cpm.meta'))
saver.restore(sess, tf.train.latest_checkpoint(OUTPUT_DIR))

stages = 6
y_dim = 15
heatmap_size = 32
graph = tf.get_default_graph()
X = graph.get_tensor_by_name('X:0')
stage_heatmap = graph.get_tensor_by_name('stage_%d/BiasAdd:0' % stages)

def visualize_result(imgs, heatmap, joints):
imgs = imgs.astype(np.int32)
coords = []
for i in range(imgs.shape[0]):
hp = heatmap[i, :, :, :joints].reshape((heatmap_size, heatmap_size, joints))
hp = cv2.resize(hp, (img_size, img_size))
coord = np.zeros((joints, 2))

for j in range(joints):
xy = np.unravel_index(np.argmax(hp[:, :, j]), (img_size, img_size))
coord[j, :] = [xy[0], xy[1]]
cv2.circle(imgs[i], (xy[1], xy[0]), 3, (120, 240, 120), 2)

coords.append(coord)

return imgs / 255., coords

heatmap = sess.run(stage_heatmap, feed_dict={X: (X_test / 255. - 0.5) * 2})
X_test, coords = visualize_result(X_test, heatmap, y_dim) 

n = int(np.sqrt(batch_size))
puzzle = np.ones((img_size * n, img_size * n, 3))
for i in range(batch_size):
img = X_test[i]
r = i // n
c = i % n
puzzle[r * img_size: (r + 1) * img_size, c * img_size: (c + 1) * img_size, :] = img
plt.figure(figsize=(12, 12))
plt.imshow(puzzle)
plt.show()
imsave('服飾關鍵點定位測試集結果.jpg', puzzle)
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測試集結果如下，對於大多數情況能夠得到很準確的關鍵點定位結果