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origFeatureExtractor.py
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origFeatureExtractor.py
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import warnings
import cv2
import cv2.cuda as cv2cuda
import matplotlib.pyplot as plt
import numpy as np
import pylab
import seaborn as sns
# Importing Keras libraries
import tensorflow as tf
from keras import Input
from keras.applications import VGG16
from keras.applications import imagenet_utils
from keras.backend import sigmoid
import keras.backend as K
from keras.callbacks import ModelCheckpoint, Callback
from keras.layers import Activation, BatchNormalization, MaxPooling1D, GlobalAveragePooling1D, Conv2D, \
GlobalAveragePooling2D, Conv1D, MaxPool1D
from keras.layers import Dense, MaxPooling2D
from keras.layers import Dropout, Flatten
from keras.models import Sequential
from keras.optimizers import Adam, SGD
from keras.preprocessing import image
from keras.utils.generic_utils import get_custom_objects
from keras.wrappers.scikit_learn import KerasRegressor
from scipy.stats import stats
# Train whole data then test on decades
from sklearn.linear_model import Ridge
from sklearn.metrics import r2_score, mean_squared_error
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import MinMaxScaler
# Importing sklearn libraries
epochs = 100
batch_size = 32
image_count = 10
limit_data = False
# max_samples = 100
# test_run = True
generate_data = False
load_image_data = True
load_features = False
load_features_flat = False
show_dist_graphs = False
normalize = False
use_orb = False
limit_memory = False
remove_outliers = True
threshold = 1
def swish(x, beta = 1):
return (x * sigmoid(beta * x))
get_custom_objects().update({'swish': Activation(swish)})
def tilted_loss(q,y,f):
e = (y-f)
return K.mean(K.maximum(q*e, (q-1)*e), axis=-1)
def create_features(dataSource, pre_model):
x_scratch = []
for comic in dataSource:
img_path = comic[3]
# print(img_path)
img = image.load_img(img_path, target_size=(224, 224))
img_data = image.img_to_array(img)
# img_data = cv2.cvtColor(img_data, cv2.COLOR_BGR2GRAY)
img_data = np.expand_dims(img_data, axis=0)
img_data = imagenet_utils.preprocess_input(img_data)
x_scratch.append(img_data)
x = np.vstack(x_scratch)
features = pre_model.predict(x, batch_size=batch_size)
features_flatten = features.reshape((features.shape[0], 7 * 7 * 512))
return x, features, features_flatten
# return x, features
class EarlyStoppingByLossVal(Callback):
def __init__(self, monitor='val_loss', value=0.00001, verbose=0):
super(Callback, self).__init__()
self.monitor = monitor
self.value = value
self.verbose = verbose
def on_epoch_end(self, epoch, logs={}):
current = logs.get(self.monitor)
if current is None:
warnings.warn("Early stopping requires %s available!" % self.monitor, RuntimeWarning)
if current < self.value:
if self.verbose > 0:
print("Epoch %05d: early stopping THR" % epoch)
self.model.stop_training = True
def plot_loss(history):
start_epoch = 2
fig = plt.figure(figsize=(10, 5))
plt.plot(history.history['loss'][start_epoch:])
plt.plot(history.history['val_loss'][start_epoch:])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper right')
plt.show()
if limit_memory:
# Save memory just in case
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
try:
tf.config.experimental.set_virtual_device_configuration(gpus[0], [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=1024)])
except RuntimeError as e:
print(e)
#Load up the clean data
clean_data = np.load('cleanData.npy',allow_pickle=True)
sift = cv2.xfeatures2d.SIFT_create()
surf = cv2.xfeatures2d.SURF_create()
orb_feature_count = 1000
orb = cv2.ORB_create(nfeatures=orb_feature_count, scoreType=cv2.ORB_FAST_SCORE)
def get_orb_features(dataSource):
x_scratch = np.zeros(shape=(1,orb_feature_count, 32))
count = 0
for comic in dataSource:
if(count % 100 == 0):
print("Comic:" + str(count) + " of " + str(len(dataSource)))
count = count + 1
img_path = comic[3]
# print(img_path)
img = cv2.imread(img_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# descriptors = sift.detectAndCompute(img, None)
# descriptors = surf.detectAndCompute(img, None)
keypoints, descriptors = orb.detectAndCompute(img, None)
try:
while len(descriptors) < orb_feature_count:
newrow = np.zeros(shape=(32))
descriptors = np.vstack((descriptors,newrow))
while len(descriptors) > orb_feature_count:
descriptors = np.delete(descriptors, 0,0)
descriptors = descriptors[np.newaxis,:,:]
except (RuntimeError, TypeError, NameError):
descriptors = np.zeros(shape=(1,orb_feature_count, 32))
x_scratch = np.vstack((x_scratch,descriptors))
x = np.asarray(x_scratch)
x = np.delete(x, 0,0)
# features = pre_model.predict(x, batch_size=batch_size)
# features_flatten = features.reshape((features.shape[0], 7 * 7 * 512))
return x
# img = cv2.drawKeypoints(img, keypoints_orb, None)
# cv2.imshow("Image", img)
# cv2.waitKey(0)
# img_data = image.img_to_array(img)
# # img_data = cv2.cvtColor(img_data, cv2.COLOR_BGR2GRAY)
# img_data = np.expand_dims(img_data, axis=0)
# img_data = np.squeeze(img_data, axis=0)
if limit_data:
clean_data = clean_data[:image_count]
data_y = []
data = clean_data
def create_y(data):
for comic in data:
data_y.append(int(comic[6]))
np.save("data_y.npy", data_y)
np.savetxt('data_y.csv', data_y, delimiter=',')
if generate_data:
if (use_orb):
print("getting Orb")
features = get_orb_features(data)
np.save("features_orb.npy", features)
print("Orb Saved")
else:
create_y(data)
model = VGG16( weights="imagenet",include_top=False)
# model = VGG16( include_top=False)
model.summary()
print("Creating Features")
image_data, features, features_flatten = create_features(clean_data, model)
np.save("image_data.npy", image_data)
np.save("features.npy", features)
np.save("features_flatten.npy", features_flatten)
print("Loading Data Set")
if load_image_data:
print("Loading image data")
image_data = np.load("image_data.npy")
if load_features:
print("Loading feature data")
if use_orb:
features_orb_0 = np.load("features_orb_0.npy", allow_pickle=True)
features_orb_1 = np.load("features_orb_1.npy", allow_pickle=True)
features_orb_2 = np.load("features_orb_2.npy", allow_pickle=True)
features_orb_3 = np.load("features_orb_3.npy", allow_pickle=True)
features_orb_4 = np.load("features_orb_4.npy", allow_pickle=True)
features_orb_5 = np.load("features_orb_5.npy", allow_pickle=True)
features_orb_6 = np.load("features_orb_6.npy", allow_pickle=True)
features_orb_7 = np.load("features_orb_7.npy", allow_pickle=True)
features_orb_8 = np.load("features_orb_8.npy", allow_pickle=True)
features_orb_9 = np.load("features_orb_9.npy", allow_pickle=True)
features = np.concatenate((features_orb_0,
features_orb_1,
features_orb_2,
features_orb_3,
features_orb_4,
features_orb_5,
features_orb_6,
features_orb_7,
features_orb_8,
features_orb_9), axis=0)
else:
features = np.load("features.npy")
if load_features_flat:
print("Loading flattened feature data")
features_flatten = np.load("features_flatten.npy")
data_y = np.load("data_y.npy")
if limit_data:
data_y = data_y[:image_count]
if remove_outliers:
#Remove outliers
print("Removing Outliers")
z = np.abs(stats.zscore(data[:,6].astype(int)))
# print(np.where(z > threshold))
print(data.shape)
print(data_y.shape)
data = data[(z < threshold)]
data_y = data_y[(z < threshold)]
print("Outliers Removed")
print(data.shape)
print(data_y.shape)
#used to reduce the image pool to run faster tests
np.random.rand(42)
if limit_data:
print("Test Run Activated Running on Reduced Data Set")
print("Image Count: " + str(image_count))
indices = np.random.permutation(data.shape[0])[:image_count]
else:
indices = np.random.permutation(data.shape[0])
index = int(0.8 * len(indices))
percent = int(0.1 * len(indices))
#train test validation split
training_idx, test_idx, val_idx = indices[:index], indices[index:index+percent], indices[index+percent:]
train_data, test_data, val_data = data[training_idx,:], data[test_idx,:], data[val_idx,:]
# features = np.reshape(features, (features.shape[0],112,112,32))
# # feature = np.reshape(feature, (224,112))
# pic = features[1]
# pylab.imshow(pic)
# pylab.show()
# features = np.squeeze(features, axis=0)
if load_image_data:
train, test, val = image_data[training_idx,:], image_data[test_idx,:], image_data[val_idx,:]
if load_features:
print(features.shape)
train, test, val = features[training_idx], features[test_idx], features[val_idx]
if load_features_flat:
train, test, val = features_flatten[training_idx,:], features_flatten[test_idx,:], features_flatten[val_idx,:]
train_y, test_y, val_y = data_y[training_idx], data_y[test_idx], data_y[val_idx]
if normalize:
exp = .2
train_y = np.power(train_y.astype(float),exp)
val_y = np.power(val_y.astype(float),exp)
scaler = MinMaxScaler()
# train_y = train_y.reshape(-1, 1)
# # train_y = scaler.fit(train_y)
# normalized_train_y = scaler.fit_transform(train_y)
# inverse_train_y = scaler.inverse_transform(normalized_train_y)
#
# val_y = val_y.reshape(-1, 1)
# # val_y = scaler.fit(val_y)
# normalized_val_y = scaler.fit_transform(val_y)
# inverse_train_y = scaler.inverse_transform(normalized_val_y)
# train_y = np.power(train_y.astype(float), exp)
# val_y = np.power(val_y.astype(float),exp)
if show_dist_graphs:
yPlot = data_y
y_pos = np.sort(yPlot.astype(int))
sns.set(color_codes=True)
sns.distplot(y_pos,bins=20)
plt.show()
if normalize:
yPlot = data_y
exp = .2
yPlot = np.power(yPlot.astype(float),exp)
yPlot = yPlot.reshape(-1, 1)
# yPlot = scaler.fit(yPlot)
# normalized_yPlot = scaler.fit_transform(yPlot)
# inverse_train_y = scaler.inverse_transform(normalized_yPlot)
y_pos = np.sort(yPlot)
sns.set(color_codes=True)
sns.distplot(y_pos)
plt.show()
# yPlot = train_y
# y_pos = np.sort(yPlot.astype(int))
# x_pos = np.arange(len(yPlot))
# plt.scatter(x_pos, y_pos, alpha=0.5, color='blue')
# # plt.show()
#
# yPlot = test_y
# y_pos = np.sort(yPlot.astype(int))
# x_pos = np.arange(len(yPlot))
# plt.scatter(x_pos, y_pos, alpha=0.5, color='red')
# # plt.show()
#
# yPlot = val_y
# y_pos = np.sort(yPlot.astype(int))
# x_pos = np.arange(len(yPlot))
# plt.scatter(x_pos, y_pos, alpha=0.5,color='green')
# plt.show()
# Creating a checkpointer
checkpointer = ModelCheckpoint(filepath='scratchmodel.best.hdf5',
verbose=1, save_best_only=True)
callbacks = [
EarlyStoppingByLossVal(monitor='val_loss', value=0.001, verbose=1),
checkpointer]
optimizer = Adam(learning_rate=0.01, beta_1=0.9, beta_2=0.999, amsgrad=False)
# optimizer = SGD(lr=0.01, momentum=0.9)
def baseline_model():
# model = Sequential()
# model.add(MaxPooling1D())
# model = Sequential()
# model.add(Conv1D(3,3, activation='relu', input_shape=train.shape[1:]))
# model.add(Conv1D(3,3, activation='relu'))
# model.add(MaxPool1D(2))
# model.add(Dropout(0.5))
#
# model.add(Conv1D(3,3, activation='relu'))
# model.add(Conv1D(3,3, activation='relu'))
# model.add(MaxPool1D(2))
# model.add(Dropout(0.5))
# model.add(Flatten())
# model.add(Dense(1024, activation="swish", input_shape=train.shape[1:]))
# model.add(Dropout(0.5))
# model.add(Dense(1, activation=swish))
#
# model.compile(loss='mse', optimizer=optimizer, metrics=['mse', 'mae','mape'])
# model.add(Dense(1024, activation='relu', input_shape=train.shape[1:]))
# model.add(Dropout(0.5))
# model.add(Dense(64, activation='relu'))
# model.add(Dropout(0.5))
# model.add(Flatten())
# model.add(Dense(1, activation='relu'))
# model = Sequential()
# model.add(GlobalAveragePooling1D(input_shape=train.shape[1:]))
# model.add(Flatten())
# model.add(BatchNormalization(epsilon=1e-05, momentum=0.1))
# # model.add(Dense(256, activation=swish))
# # model.add(Dropout(0.5))
# # # model.add(Dense(1024, activation="relu"))
# # # model.add(Dropout(0.5))
# # # model.add(Dense(64, activation="relu"))
# # # model.add(Dropout(0.5))
# # # model.add(Dense(1, activation="relu"))
# # model.add(Dense(1))
# model.add(Dense(1024, activation=swish,input_shape=train.shape[1:]))
# model.add(BatchNormalization(epsilon=1e-05, momentum=0.1))
# model.add(Dropout(0.5))
# model.add(Dense(1024, activation=swish, use_bias=True))
# model.add(BatchNormalization(epsilon=1e-05, momentum=0.1))
# model.add(Dropout(0.5))
# model.add(Dense(256, activation=swish, use_bias=True))
# model.add(Flatten())
# model.add(Dense(1))
# model.compile(loss='mse', optimizer="adagrad", metrics=['mse', 'mae','mape'])
#
# Building up a Sequential model
stride_val = 2
model_scratch = Sequential()
model_scratch.add(Conv2D(32, (3, 3), activation=swish, strides=stride_val, input_shape=train.shape[1:]))
if stride_val == 1: model_scratch.add(MaxPooling2D(pool_size=(2, 2)))
model_scratch.add(Conv2D(64, (3, 3), activation=swish, strides=stride_val))
if stride_val == 1:model_scratch.add(MaxPooling2D(pool_size=(2, 2)))
model_scratch.add(Conv2D(64, (3, 3), activation=swish, strides=stride_val))
if stride_val == 1:model_scratch.add(MaxPooling2D(pool_size=(2, 2)))
model_scratch.add(Conv2D(128, (3, 3), activation=swish, strides=stride_val))
if stride_val == 1:model_scratch.add(MaxPooling2D(pool_size=(2, 2)))
model_scratch.add(Conv2D(256, (3, 3), activation=swish, strides=stride_val))
if stride_val == 1:model_scratch.add(MaxPooling2D(pool_size=(2, 2)))
model_scratch.add(Conv2D(512, (3, 3), activation=swish, strides=stride_val))
if stride_val == 1:model_scratch.add(MaxPooling2D(pool_size=(2, 2)))
model_scratch.add(Flatten())
model_scratch.add(Dense(512, activation=swish))
model_scratch.add(BatchNormalization(epsilon=1e-05, momentum=0.1))
model_scratch.add(Dropout(0.5))
model_scratch.add(Dense(512, activation=swish))
model_scratch.add(BatchNormalization(epsilon=1e-05, momentum=0.1))
model_scratch.add(Dropout(0.5))
model_scratch.add(Dense(1, activation=swish))
model_scratch.compile(loss=lambda y, f: tilted_loss(quantile, y, f), optimizer='adagrad' , metrics=['mse', 'mae','mape'])
model_scratch.summary()
return model_scratch
quantile = 0.9
estimator = KerasRegressor(build_fn=baseline_model, nb_epoch=100, batch_size= 100, verbose=False)
# kfold = KFold(n_splits=10, random_state=seed)
# results = cross_val_score(estimator, train_features, y_train, cv=kfold)
# print("Results: %.2f (%.2f) MSE" % (results.mean(), results.std()))
# train_flat_trick = np.reshape(train, len(train)^2, 1)
# train = train.reshape(len(train),1000,32,1)
# test = test.reshape(len(test),1000,32,1)
# val = val.reshape(len(val),1000,32,1)
# ridge=Ridge()
# # parameters= {'alpha':[x for x in [.001,.0015,0.002]]}
#
# nsamples, nx, ny = train.shape
# d2_train_dataset = train.reshape((nsamples,nx*ny))
#
# nsamples, nx, ny = test.shape
# d2_test_dataset = test.reshape((nsamples,nx*ny))
#
# nsamples, nx, ny = val.shape
# d2_val_dataset = val.reshape((nsamples,nx*ny))
# ridge_reg=GridSearchCV(ridge, param_grid=parameters)
# ridge_reg.fit(d2_train_dataset,train_y)
# print("The best value of Alpha is: ",ridge_reg.best_params_)
# ridge_mod=Ridge(alpha=.0015)
# ridge_mod.fit(d2_train_dataset,train_y)
# y_pred_train=ridge_mod.predict(d2_train_dataset)
# y_pred_test=ridge_mod.predict(d2_test_dataset)
# y_pred_val=ridge_mod.predict(d2_val_dataset)
#
# print('Root Mean Square Error train = ' + str(np.sqrt(mean_squared_error(train_y, y_pred_train))))
# print('Root Mean Square Error val = ' + str(np.sqrt(mean_squared_error(val_y, y_pred_val))))
# print('Root Mean Square Error test = ' + str(np.sqrt(mean_squared_error(test_y, y_pred_test))))
history = estimator.fit(train, train_y, batch_size=batch_size, epochs=epochs,
validation_data=(val, val_y), callbacks = callbacks,
verbose=1, shuffle=True)
prediction = estimator.predict(test)
# pic = features[0,:,:,128]
# pylab.imshow(pic)
# pylab.show()
if normalize:
prediction = np.power(prediction, 5)
# prediction = prediction.reshape(-1,1)
# prediction = scaler.inverse_transform(prediction)
test_error = np.abs(test_y - prediction)
mean_error = np.mean(test_error)
min_error = np.min(test_error)
max_error = np.max(test_error)
std_error = np.std(test_error)
print("Mean Error:" + str(mean_error))
print("Min Error:" + str(min_error))
print("Max Error:" + str(max_error))
print("Std Error:" + str(std_error))
# print('Root Mean Square Error train = ' + str(np.sqrt(mean_squared_error(train_y, y_pred_train))))
# print('Root Mean Square Error val = ' + str(np.sqrt(mean_squared_error(val_y, y_pred_val))))
# print('Root Mean Square Error test = ' + str(np.sqrt(mean_squared_error(test_y, y_pred_test))))
plot_loss(history)
plt.yscale('log')
plt.scatter(test_y, test_error)
plt.xlabel("True Values")
plt.ylabel("Error")
plt.show()
plt.scatter(test_y, prediction)
plt.xlabel("index")
plt.ylabel("Prediction")
plt.show()
# y_pos = np.sort(test_error.astype(int))
# x_pos = np.arange(len(y_pos))
# plt.scatter(x_pos, y_pos, alpha=0.5, color='red')
# plt.show()
#
# y_pos = np.sort(prediction.astype(int))
# x_pos = np.arange(len(y_pos))
# plt.scatter(x_pos, y_pos, alpha=0.5, color='yellow')
# plt.show()