詳解視訊中動作識別模型與程式碼實踐

import os
import moxing as mox
if not os.path.exists('videos'):
 mox.file.copy("obs://ai-course-common-26-bj4-v2/video/video.tar.gz", "./video.tar.gz")
 # 使用tar命令解壓資源包
 os.system("tar xf ./video.tar.gz")
 # 使用rm命令刪除壓縮包
 os.system("rm ./video.tar.gz")
INFO:root:Using MoXing-v1.17.3-
INFO:root:Using OBS-Python-SDK-3.20.7

video_name = "./data/v_TaiChi_g01_c01.avi"

from IPython.display import clear_output, Image, display, HTML
import time
import cv2
import base64
import numpy as np
def arrayShow(img):
 _,ret = cv2.imencode('.jpg', img) 
 return Image(data=ret) 
cap = cv2.VideoCapture(video_name)
while True:
 try:
 clear_output(wait=True)
        ret, frame = cap.read()
 if ret:
 tmp = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
 img = arrayShow(frame)
            display(img)
 time.sleep(0.05)
 else:
 break
 except KeyboardInterrupt:
 cap.release()
cap.release()

import cv2
import os
# 視訊資料集儲存位置
video_path = './dataset_subset/'
# 生成的影象資料集儲存位置
save_path = './dataset/'
# 如果檔案路徑不存在則建立路徑
if not os.path.exists(save_path):
 os.mkdir(save_path)
# 獲取動作列表
action_list = os.listdir(video_path)
# 遍歷所有動作
for action in action_list:
 if action.startswith(".")==False:
 if not os.path.exists(save_path+action):
 os.mkdir(save_path+action)
 video_list = os.listdir(video_path+action)
 # 遍歷所有視訊
 for video in video_list:
            prefix = video.split('.')[0]
 if not os.path.exists(os.path.join(save_path, action, prefix)):
 os.mkdir(os.path.join(save_path, action, prefix))
 save_name = os.path.join(save_path, action, prefix) + '/'
 video_name = video_path+action+'/'+video
 # 讀取視訊檔
 # cap為視訊的幀
            cap = cv2.VideoCapture(video_name)
 # fps為影格率
            fps = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
 fps_count = 0
 for i in range(fps):
                ret, frame = cap.read()
 if ret:
 # 將幀畫面寫入圖片檔案中
                    cv2.imwrite(save_name+str(10000+fps_count)+'.jpg',frame)
 fps_count += 1

from keras.layers import Dense,Dropout,Conv3D,Input,MaxPool3D,Flatten,Activation, ZeroPadding3D
from keras.regularizers import l2
from keras.models import Model, Sequential
# 輸入資料為 112×112 的圖片，16幀， 3通道
input_shape = (112,112,16,3)
# 權重衰減率
weight_decay = 0.005
# 型別數量，我們使用UCF-101 為資料集，所以為101
nb_classes = 101
# 構建模型結構
inputs = Input(input_shape)
x = Conv3D(64,(3,3,3),strides=(1,1,1),padding='same',
           activation='relu',kernel_regularizer=l2(weight_decay))(inputs)
x = MaxPool3D((2,2,1),strides=(2,2,1),padding='same')(x)
x = Conv3D(128,(3,3,3),strides=(1,1,1),padding='same',
           activation='relu',kernel_regularizer=l2(weight_decay))(x)
x = MaxPool3D((2,2,2),strides=(2,2,2),padding='same')(x)
x = Conv3D(128,(3,3,3),strides=(1,1,1),padding='same',
           activation='relu',kernel_regularizer=l2(weight_decay))(x)
x = MaxPool3D((2,2,2),strides=(2,2,2),padding='same')(x)
x = Conv3D(256,(3,3,3),strides=(1,1,1),padding='same',
           activation='relu',kernel_regularizer=l2(weight_decay))(x)
x = MaxPool3D((2,2,2),strides=(2,2,2),padding='same')(x)
x = Conv3D(256, (3, 3, 3), strides=(1, 1, 1), padding='same',
           activation='relu',kernel_regularizer=l2(weight_decay))(x)
x = MaxPool3D((2, 2, 2), strides=(2, 2, 2), padding='same')(x)
x = Flatten()(x)
x = Dense(2048,activation='relu',kernel_regularizer=l2(weight_decay))(x)
x = Dropout(0.5)(x)
x = Dense(2048,activation='relu',kernel_regularizer=l2(weight_decay))(x)
x = Dropout(0.5)(x)
x = Dense(nb_classes,kernel_regularizer=l2(weight_decay))(x)
x = Activation('softmax')(x)
model = Model(inputs, x)
Using TensorFlow backend.
/home/ma-user/anaconda3/envs/TensorFlow-1.13.1/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
/home/ma-user/anaconda3/envs/TensorFlow-1.13.1/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/home/ma-user/anaconda3/envs/TensorFlow-1.13.1/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
/home/ma-user/anaconda3/envs/TensorFlow-1.13.1/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/home/ma-user/anaconda3/envs/TensorFlow-1.13.1/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
/home/ma-user/anaconda3/envs/TensorFlow-1.13.1/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
 np_resource = np.dtype([("resource", np.ubyte, 1)])
WARNING:tensorflow:From /home/ma-user/anaconda3/envs/TensorFlow-1.13.1/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
WARNING:tensorflow:From /home/ma-user/anaconda3/envs/TensorFlow-1.13.1/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.
Instructions for updating:
Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.

model.summary()

model.input
<tf.Tensor 'input_1:0' shape=(?, 112, 112, 16, 3) dtype=float32>

# 參照必要的庫
from keras.optimizers import SGD,Adam
from keras.utils import np_utils
import numpy as np
import random
import cv2
import matplotlib.pyplot as plt
# 自定義callbacks
from schedules import onetenth_4_8_12
INFO:matplotlib.font_manager:font search path ['/home/ma-user/anaconda3/envs/TensorFlow-1.13.1/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf', '/home/ma-user/anaconda3/envs/TensorFlow-1.13.1/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/afm', '/home/ma-user/anaconda3/envs/TensorFlow-1.13.1/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/pdfcorefonts']
INFO:matplotlib.font_manager:generated new fontManager

img_path = save_path # 圖片檔案儲存位置
results_path = './results' # 訓練結果儲存位置
if not os.path.exists(results_path):
 os.mkdir(results_path)

cates = os.listdir(img_path)
train_list = []
test_list = []
# 遍歷所有的動作型別
for cate in cates:
    videos = os.listdir(os.path.join(img_path, cate))
    length = len(videos)//5
 # 訓練集大小，隨機取視訊檔加入訓練集
    train= random.sample(videos, length*4)
 train_list.extend(train)
 # 將餘下的視訊加入測試集
 for video in videos:
 if video not in train:
 test_list.append(video)
print("訓練集為：") 
print( train_list)
print("共%d 個視訊\n"%(len(train_list)))
print("驗證集為：") 
print(test_list)
print("共%d 個視訊"%(len(test_list)))

def process_data(img_path, file_list,batch_size=16,train=True):
    batch = np.zeros((batch_size,16,112,112,3),dtype='float32')
    labels = np.zeros(batch_size,dtype='int')
 cate_list = os.listdir(img_path)
 def read_classes():
        path = "./classInd.txt"
 with open(path, "r+") as f:
            lines = f.readlines()
        classes = {}
 for line in lines:
 c_id = line.split()[0]
 c_name = line.split()[1]
            classes[c_name] =c_id 
 return classes
 classes_dict = read_classes()
 for file in file_list:
        cate = file.split("_")[1]
 img_list = os.listdir(os.path.join(img_path, cate, file))
 img_list.sort()
 batch_img = []
 for i in range(batch_size):
            path = os.path.join(img_path, cate, file)
            label = int(classes_dict[cate])-1
            symbol = len(img_list)//16
 if train:
 # 隨機進行裁剪
 crop_x = random.randint(0, 15)
 crop_y = random.randint(0, 58)
 # 隨機進行翻轉
 is_flip = random.randint(0, 1)
 # 以16 幀為單位
 for j in range(16):
 img = img_list[symbol + j]
                    image = cv2.imread( path + '/' + img)
                    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
                    image = cv2.resize(image, (171, 128))
 if is_flip == 1:
                        image = cv2.flip(image, 1)
                    batch[i][j][:][:][:] = image[crop_x:crop_x + 112, crop_y:crop_y + 112, :]
                    symbol-=1
 if symbol<0:
 break
                labels[i] = label
 else:
 for j in range(16):
 img = img_list[symbol + j]
                    image = cv2.imread( path + '/' + img)
                    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
                    image = cv2.resize(image, (171, 128))
                    batch[i][j][:][:][:] = image[8:120, 30:142, :]
                    symbol-=1
 if symbol<0:
 break
                labels[i] = label
 return batch, labels
batch, labels = process_data(img_path, train_list)
print("每個batch的形狀為：%s"%(str(batch.shape)))
print("每個label的形狀為：%s"%(str(labels.shape)))
每個batch的形狀為：(16, 16, 112, 112, 3)
每個label的形狀為：(16,)

def generator_train_batch(train_list, batch_size, num_classes, img_path):
 while True:
 # 讀取一個batch的資料
 x_train, x_labels = process_data(img_path, train_list, batch_size=16,train=True)
        x = preprocess(x_train)
 # 形成input要求的資料格式
        y = np_utils.to_categorical(np.array(x_labels), num_classes)
        x = np.transpose(x, (0,2,3,1,4))
 yield x, y
def generator_val_batch(test_list, batch_size, num_classes, img_path):
 while True:
 # 讀取一個batch的資料
 y_test,y_labels = process_data(img_path, train_list, batch_size=16,train=False)
        x = preprocess(y_test)
 # 形成input要求的資料格式
        x = np.transpose(x,(0,2,3,1,4))
        y = np_utils.to_categorical(np.array(y_labels), num_classes)
 yield x, y

def preprocess(inputs):
 inputs[..., 0] -= 99.9
 inputs[..., 1] -= 92.1
 inputs[..., 2] -= 82.6
 inputs[..., 0] /= 65.8
 inputs[..., 1] /= 62.3
 inputs[..., 2] /= 60.3
 return inputs
# 訓練一個epoch大約需4分鐘
# 類別數量
num_classes = 101
# batch大小
batch_size = 4
# epoch數量
epochs = 1
# 學習率大小
lr = 0.005
# 優化器定義
sgd = SGD(lr=lr, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
# 開始訓練
history = model.fit_generator(generator_train_batch(train_list, batch_size, num_classes,img_path),
 steps_per_epoch= len(train_list) // batch_size,
                              epochs=epochs,
                              callbacks=[onetenth_4_8_12(lr)],
 validation_data=generator_val_batch(test_list, batch_size,num_classes,img_path),
 validation_steps= len(test_list) // batch_size,
                              verbose=1)
# 對訓練結果進行儲存
model.save_weights(os.path.join(results_path, 'weights_c3d.h5'))
WARNING:tensorflow:From /home/ma-user/anaconda3/envs/TensorFlow-1.13.1/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
Epoch 1/1
20/20 [==============================] - 442s 22s/step - loss: 28.7099 - acc: 0.9344 - val_loss: 27.7600 - val_acc: 1.0000

from IPython.display import clear_output, Image, display, HTML
import time
import cv2
import base64
import numpy as np

from models import c3d_model
model = c3d_model()
model.load_weights(os.path.join(results_path, 'weights_c3d.h5'), by_name=True) # 載入剛訓練的模型

def arrayShow(img):
 _,ret = cv2.imencode('.jpg', img) 
 return Image(data=ret) 

# 載入所有的類別和編號
with open('./ucfTrainTestlist/classInd.txt', 'r') as f:
 class_names = f.readlines()
 f.close()
# 讀取視訊檔
video = './videos/v_Punch_g03_c01.avi'
cap = cv2.VideoCapture(video)
clip = []
# 將視訊畫面傳入模型
while True:
 try:
 clear_output(wait=True)
        ret, frame = cap.read()
 if ret:
 tmp = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
 clip.append(cv2.resize(tmp, (171, 128)))
 # 每16幀進行一次預測
 if len(clip) == 16:
                inputs = np.array(clip).astype(np.float32)
                inputs = np.expand_dims(inputs, axis=0)
 inputs[..., 0] -= 99.9
 inputs[..., 1] -= 92.1
 inputs[..., 2] -= 82.6
 inputs[..., 0] /= 65.8
 inputs[..., 1] /= 62.3
 inputs[..., 2] /= 60.3
                inputs = inputs[:,:,8:120,30:142,:]
                inputs = np.transpose(inputs, (0, 2, 3, 1, 4))
 # 獲得預測結果
 pred = model.predict(inputs)
                label = np.argmax(pred[0])
 # 將預測結果繪製到畫面中
                cv2.putText(frame, class_names[label].split(' ')[-1].strip(), (20, 20),
                            cv2.FONT_HERSHEY_SIMPLEX, 0.6,
 (0, 0, 255), 1)
                cv2.putText(frame, "prob: %.4f" % pred[0][label], (20, 40),
                            cv2.FONT_HERSHEY_SIMPLEX, 0.6,
 (0, 0, 255), 1)
 clip.pop(0)
 # 播放預測後的視訊 
            lines, columns, _ = frame.shape
            frame = cv2.resize(frame, (int(columns), int(lines)))
 img = arrayShow(frame)
            display(img)
 time.sleep(0.02)
 else:
 break
 except:
 print(0)
cap.release()

import numpy as np
import tensorflow as tf
import i3d
WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.
For more information, please see:
 * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
 * https://github.com/tensorflow/addons
If you depend on functionality not listed there, please file an issue.

# 輸入圖片大小
_IMAGE_SIZE = 224
#  視訊的幀數
_SAMPLE_VIDEO_FRAMES = 79
# 輸入資料包括兩部分：RGB和光流
# RGB和光流資料已經經過提前計算
_SAMPLE_PATHS = {
 'rgb': 'data/v_CricketShot_g04_c01_rgb.npy',
 'flow': 'data/v_CricketShot_g04_c01_flow.npy',
}
# 提供了多種可以選擇的預訓練權重
# 其中，imagenet系列模型從ImageNet的2D權重中拓展而來，其餘為視訊資料下的預訓練權重
_CHECKPOINT_PATHS = {
 'rgb': 'data/checkpoints/rgb_scratch/model.ckpt',
 'flow': 'data/checkpoints/flow_scratch/model.ckpt',
 'rgb_imagenet': 'data/checkpoints/rgb_imagenet/model.ckpt',
 'flow_imagenet': 'data/checkpoints/flow_imagenet/model.ckpt',
}
# 記錄類別檔案
_LABEL_MAP_PATH = 'data/label_map.txt'
# 類別數量為400
NUM_CLASSES = 400

imagenet_pretrained = True
# 載入動作型別
kinetics_classes = [x.strip() for x in open(_LABEL_MAP_PATH)]
tf.logging.set_verbosity(tf.logging.INFO)

rgb_input = tf.placeholder(tf.float32, shape=(1, _SAMPLE_VIDEO_FRAMES, _IMAGE_SIZE, _IMAGE_SIZE, 3))
with tf.variable_scope('RGB', reuse=tf.AUTO_REUSE):
 rgb_model = i3d.InceptionI3d(NUM_CLASSES, spatial_squeeze=True, final_endpoint='Logits')
 rgb_logits, _ = rgb_model(rgb_input, is_training=False, dropout_keep_prob=1.0)
rgb_variable_map = {}
for variable in tf.global_variables():
 if variable.name.split('/')[0] == 'RGB':
 rgb_variable_map[variable.name.replace(':0', '')] = variable
rgb_saver = tf.train.Saver(var_list=rgb_variable_map, reshape=True)

flow_input = tf.placeholder(tf.float32,shape=(1, _SAMPLE_VIDEO_FRAMES, _IMAGE_SIZE, _IMAGE_SIZE, 2))
with tf.variable_scope('Flow', reuse=tf.AUTO_REUSE):
 flow_model = i3d.InceptionI3d(NUM_CLASSES, spatial_squeeze=True, final_endpoint='Logits')
 flow_logits, _ = flow_model(flow_input, is_training=False, dropout_keep_prob=1.0)
flow_variable_map = {}
for variable in tf.global_variables():
 if variable.name.split('/')[0] == 'Flow':
 flow_variable_map[variable.name.replace(':0', '')] = variable
flow_saver = tf.train.Saver(var_list=flow_variable_map, reshape=True) 

model_logits = rgb_logits + flow_logits
model_predictions = tf.nn.softmax(model_logits)

with tf.Session() as sess:
 feed_dict = {}
 if imagenet_pretrained:
 rgb_saver.restore(sess, _CHECKPOINT_PATHS['rgb_imagenet']) # 載入rgb流的模型
 else:
 rgb_saver.restore(sess, _CHECKPOINT_PATHS['rgb'])
 tf.logging.info('RGB checkpoint restored')
 if imagenet_pretrained:
 flow_saver.restore(sess, _CHECKPOINT_PATHS['flow_imagenet']) # 載入flow流的模型
 else:
 flow_saver.restore(sess, _CHECKPOINT_PATHS['flow'])
 tf.logging.info('Flow checkpoint restored') 
 start_time = time.time()
 rgb_sample = np.load(_SAMPLE_PATHS['rgb']) # 載入rgb流的輸入資料
 tf.logging.info('RGB data loaded, shape=%s', str(rgb_sample.shape))
 feed_dict[rgb_input] = rgb_sample
 flow_sample = np.load(_SAMPLE_PATHS['flow']) # 載入flow流的輸入資料
 tf.logging.info('Flow data loaded, shape=%s', str(flow_sample.shape))
 feed_dict[flow_input] = flow_sample
 out_logits, out_predictions = sess.run(
 [model_logits, model_predictions],
 feed_dict=feed_dict)
 out_logits = out_logits[0]
 out_predictions = out_predictions[0]
 sorted_indices = np.argsort(out_predictions)[::-1]
 print('Inference time in sec: %.3f' % float(time.time() - start_time))
 print('Norm of logits: %f' % np.linalg.norm(out_logits))
 print('\nTop classes and probabilities')
 for index in sorted_indices[:20]:
 print(out_predictions[index], out_logits[index], kinetics_classes[index])
WARNING:tensorflow:From /home/ma-user/anaconda3/envs/TensorFlow-1.13.1/lib/python3.6/site-packages/tensorflow/python/training/saver.py:1266: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.
Instructions for updating:
Use standard file APIs to check for files with this prefix.
INFO:tensorflow:Restoring parameters from data/checkpoints/rgb_imagenet/model.ckpt
INFO:tensorflow:RGB checkpoint restored
INFO:tensorflow:Restoring parameters from data/checkpoints/flow_imagenet/model.ckpt
INFO:tensorflow:Flow checkpoint restored
INFO:tensorflow:RGB data loaded, shape=(1, 79, 224, 224, 3)
INFO:tensorflow:Flow data loaded, shape=(1, 79, 224, 224, 2)
Inference time in sec: 1.511
Norm of logits: 138.468643
Top classes and probabilities
1.0 41.813675 playing cricket
1.497162e-09 21.49398 hurling (sport)
3.8431236e-10 20.13411 catching or throwing baseball
1.549242e-10 19.22559 catching or throwing softball
1.1360187e-10 18.915354 hitting baseball
8.801105e-11 18.660116 playing tennis
2.4415466e-11 17.37787 playing kickball
1.153184e-11 16.627766 playing squash or racquetball
6.1318893e-12 15.996157 shooting goal (soccer)
4.391727e-12 15.662376 hammer throw
2.2134352e-12 14.9772005 golf putting
1.6307096e-12 14.67167 throwing discus
1.5456218e-12 14.618079 javelin throw
7.6690325e-13 13.917259 pumping fist
5.1929587e-13 13.527372 shot put
4.2681337e-13 13.331245 celebrating
2.7205462e-13 12.880901 applauding
1.8357015e-13 12.487494 throwing ball
1.6134511e-13 12.358444 dodgeball
1.1388395e-13 12.010078 tap dancing