import cv2
import os
import numpy as np
from keras.applications.vgg16 import VGG16, preprocess_input
from keras.preprocessing.image import img_to_array
from keras.optimizers import SGD
MAX_NB_CLASSES = 20
def extract_vgg16_features_live(model, video_input_file_path):
print('Extracting frames from video: ', video_input_file_path)
vidcap = cv2.VideoCapture(video_input_file_path)
success, image = vidcap.read()
features = []
success = True
count = 0
while success:
vidcap.set(cv2.CAP_PROP_POS_MSEC, (count * 1000)) # added this line
success, image = vidcap.read()
# print('Read a new frame: ', success)
if success:
img = cv2.resize(image, (224, 224), interpolation=cv2.INTER_AREA)
input = img_to_array(img)
input = np.expand_dims(input, axis=0)
input = preprocess_input(input)
feature = model.predict(input).ravel()
features.append(feature)
count = count + 1
unscaled_features = np.array(features)
return unscaled_features
def extract_vgg16_features(model, video_input_file_path, feature_output_file_path):
if os.path.exists(feature_output_file_path):
return np.load(feature_output_file_path)
count = 0
print('Extracting frames from video: ', video_input_file_path)
vidcap = cv2.VideoCapture(video_input_file_path)
success, image = vidcap.read()
features = []
success = True
while success:
vidcap.set(cv2.CAP_PROP_POS_MSEC, (count * 1000)) # added this line
success, image = vidcap.read()
# print('Read a new frame: ', success)
if success:
img = cv2.resize(image, (224, 224), interpolation=cv2.INTER_AREA)
input = img_to_array(img)
input = np.expand_dims(input, axis=0)
input = preprocess_input(input)
feature = model.predict(input).ravel()
features.append(feature)
count = count + 1
unscaled_features = np.array(features)
np.save(feature_output_file_path, unscaled_features)
return unscaled_features
def scan_and_extract_vgg16_features(data_dir_path, output_dir_path, model=None, data_set_name=None):
if data_set_name is None:
data_set_name = 'UCF-101'
input_data_dir_path = data_dir_path + '/' + data_set_name
output_feature_data_dir_path = data_dir_path + '/' + output_dir_path
if model is None:
model = VGG16(include_top=True, weights='imagenet')
model.compile(optimizer=SGD(), loss='categorical_crossentropy', metrics=['accuracy'])
if not os.path.exists(output_feature_data_dir_path):
os.makedirs(output_feature_data_dir_path)
y_samples = []
x_samples = []
dir_count = 0
for f in os.listdir(input_data_dir_path):
file_path = input_data_dir_path + os.path.sep + f
if not os.path.isfile(file_path):
output_dir_name = f
output_dir_path = output_feature_data_dir_path + os.path.sep + output_dir_name
if not os.path.exists(output_dir_path):
os.makedirs(output_dir_path)
dir_count += 1
for ff in os.listdir(file_path):
video_file_path = file_path + os.path.sep + ff
output_feature_file_path = output_dir_path + os.path.sep + ff.split('.')[0] + '.npy'
x = extract_vgg16_features(model, video_file_path, output_feature_file_path)
y = f
y_samples.append(y)
x_samples.append(x)
if dir_count == MAX_NB_CLASSES:
break
return x_samples, y_samples
