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evaluate.py
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evaluate.py
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# -*- coding: utf-8 -*-
import os
from model import *
from sklearn.svm import SVC
from sklearn.preprocessing import normalize
import numpy as np
import cv2
import matplotlib
matplotlib.use('AGG')
import matplotlib.pyplot as plt
import random
def preprocessing(data):
data /= 255.
data -= 0.5
data *= 2.
return data
def generate_features(image_path_ori, image_path_crop,model_ori, model_crop):
feature_dim = 1024
seq_length = 16
train_num = 1536
test_num = 215
x_train = np.zeros((1, seq_length, 112, 112, 3), dtype='float32')
x_train_crop = np.zeros((1, seq_length, 64, 64, 3), dtype='float32')
x_val = np.zeros((1, seq_length, 112, 112, 3), dtype='float32')
x_val_crop = np.zeros((1, seq_length, 64, 64, 3), dtype='float32')
y_train = []
y_val = []
train_features_ori = np.zeros((train_num,feature_dim),dtype='float32')
train_features_crop = np.zeros((train_num,feature_dim),dtype='float32')
train_features = np.zeros((train_num,2*feature_dim),dtype='float32')
val_features = np.zeros((test_num,2*feature_dim),dtype='float32')
val_features_ori = np.zeros((test_num,feature_dim),dtype='float32')
val_features_crop = np.zeros((test_num,feature_dim),dtype='float32')
actions = os.listdir(image_path_ori)
actions.sort(key=str.lower)
train_num_count = 0
total_count_train = 0
total_count_val = 0
label_count = 0
for action in actions:
print(action)
samples = os.listdir(image_path_ori+action)
samples.sort(key=str.lower)
for sample in samples:
if train_num_count < 64:
train_num_count += 1
images = os.listdir(image_path_ori+action+'/'+sample)
images_crop = os.listdir(image_path_crop + action + '/' + sample)
images.sort(key=str.lower)
images_crop.sort(key=str.lower)
frames = [i for i in range(len(images) - 16)]
start_frame = random.sample(frames, 4)
# start_frame = [0,16,32]
for frame in start_frame:
for j in range(seq_length):
img = cv2.imread(image_path_ori+action+'/'+sample+'/'+images[frame+j])
img_crop = cv2.imread(image_path_crop+action+'/'+sample+'/'+images_crop[frame+j])
img = cv2.resize(img,(112,112))
img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
img_crop = cv2.cvtColor(img_crop,cv2.COLOR_BGR2RGB)
x_train[0,j,:,:,:] = img
x_train_crop[0,j,:,:,:] = img_crop
y_train.append(label_count)
x_train = preprocessing(x_train)
x_train_crop = preprocessing(x_train_crop)
x_train = np.transpose(x_train,(0,2,3,1,4))
x_train_crop = np.transpose(x_train_crop,(0,2,3,1,4))
feature_ori = model_ori.predict(x_train)
feature_crop = model_crop.predict(x_train_crop)
train_features_ori[total_count_train,:] = np.array(feature_ori)
train_features_crop[total_count_train,:] = np.array(feature_crop)
train_features[total_count_train,0:feature_dim] = np.array(feature_ori)
train_features[total_count_train,feature_dim:2*feature_dim] = np.array(feature_crop)
total_count_train += 1
x_train = np.zeros((1, seq_length, 112, 112, 3), dtype='float32')
x_train_crop = np.zeros((1, seq_length, 64, 64, 3), dtype='float32')
else:
train_num_count += 1
images = os.listdir(image_path_ori + action + '/' + sample)
images_crop = os.listdir(image_path_crop + action + '/' + sample)
images.sort(key=str.lower)
images_crop.sort(key=str.lower)
frames = [i for i in range(len(images) - 16)]
start_frame = random.sample(frames, 1)
# start_frame = [0]
for frame in start_frame:
for j in range(seq_length):
img = cv2.imread(image_path_ori + action + '/' + sample + '/' + images[frame+j])
img_crop = cv2.imread(image_path_crop + action + '/' + sample + '/' + images_crop[frame+j])
img = cv2.resize(img, (112, 112))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img_crop = cv2.cvtColor(img_crop, cv2.COLOR_BGR2RGB)
x_val[0, j, :, :, :] = img
x_val_crop[0,j,:,:,:] = img_crop
y_val.append(label_count)
x_val = preprocessing(x_val)
x_val_crop = preprocessing(x_val_crop)
x_val = np.transpose(x_val, (0, 2, 3, 1, 4))
x_val_crop = np.transpose(x_val_crop, (0, 2, 3, 1, 4))
feature_ori = model_ori.predict(x_val)
feature_crop = model_crop.predict(x_val_crop)
val_features_ori[total_count_val,:] = np.array(feature_ori)
val_features_crop[total_count_val,:] = np.array(feature_crop)
val_features[total_count_val, 0:feature_dim] = np.array(feature_ori)
val_features[total_count_val, feature_dim:2*feature_dim] = np.array(feature_crop)
total_count_val += 1
x_val = np.zeros((1, seq_length, 112, 112, 3), dtype='float32')
x_val_crop = np.zeros((1, seq_length, 64, 64, 3), dtype='float32')
label_count += 1
train_num_count = 0
y_train = np.array(y_train)
y_val = np.array(y_val)
return train_features,y_train,val_features,y_val,train_features_ori,\
val_features_ori,train_features_crop,val_features_crop
def plot_confusion_matrix(y_true, y_pred, labels, prefix):
from sklearn.metrics import confusion_matrix
cmap = plt.cm.binary
action_names = ['boxing','handclapping','handwaving','jogging','running','walking']
cm = confusion_matrix(y_true, y_pred)
tick_marks = np.array(range(len(labels))) + 0.5
np.set_printoptions(precision=2)
cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
plt.figure(figsize=(10, 8), dpi=120)
ind_array = np.arange(len(labels))
x, y = np.meshgrid(ind_array, ind_array)
intFlag = 0
for x_val, y_val in zip(x.flatten(), y.flatten()):
#
if (intFlag):
c = cm[y_val][x_val]
plt.text(x_val, y_val, "%d" % (c,), color='red', fontsize=8, va='center', ha='center')
else:
c = cm_normalized[y_val][x_val]
if (c > 0.01):
plt.text(x_val, y_val, "%0.2f" % (c,), color='red', fontsize=7, va='center', ha='center')
else:
plt.text(x_val, y_val, "%d" % (0,), color='red', fontsize=7, va='center', ha='center')
if(intFlag):
plt.imshow(cm, interpolation='nearest', cmap=cmap)
else:
plt.imshow(cm_normalized, interpolation='nearest', cmap=cmap)
plt.gca().set_xticks(tick_marks, minor=True)
plt.gca().set_yticks(tick_marks, minor=True)
plt.gca().xaxis.set_ticks_position('none')
plt.gca().yaxis.set_ticks_position('none')
plt.grid(True, which='minor', linestyle='-')
plt.gcf().subplots_adjust(bottom=0.15)
plt.title('')
plt.colorbar()
xlocations = np.array(range(len(labels)))
plt.xticks(xlocations, action_names, rotation=90)
plt.yticks(xlocations, action_names)
plt.ylabel('Index of True Classes')
plt.xlabel('Index of Predict Classes')
plt.savefig(prefix+'_confusion_matrix.jpg', dpi=300)
plt.close()
# plt.show()
def svc(traindata, trainlabel, testdata, testlabel, labels, prefix):
print("Start training SVM...")
svcClf = SVC(C=10.0, kernel="linear", cache_size=3000)
svcClf.fit(traindata, trainlabel)
pred_testlabel = svcClf.predict(testdata)
num = len(pred_testlabel)
accuracy = len([1 for i in range(num) if testlabel[i] == pred_testlabel[i]]) / float(num)
print("svm Accuracy:", accuracy)
plot_confusion_matrix(testlabel,pred_testlabel,labels,prefix)
if __name__ == "__main__":
original_size_image_path = '/home/deep/datasets/kth/images/original/'
crop_image_path = '/home/deep/datasets/kth/images/crop/'
results_savedPath = 'results/'
_, model_ori = model_original_size()
_, model_crop = model_crop_size()
model_ori.load_weights(results_savedPath+'original/original_size.h5',by_name=True)
model_crop.load_weights(results_savedPath+'crop/crop_size.h5',by_name=True)
train_features,train_labels,val_features,val_labels,\
train_features_ori,val_features_ori,\
train_features_crop,val_features_crop = generate_features(original_size_image_path,
crop_image_path,
model_ori,
model_crop)
train_features = normalize(train_features, norm='l2')
val_features = normalize(val_features, norm='l2')
train_features_ori = normalize(train_features_ori, norm='l2')
val_features_ori = normalize(val_features_ori, norm='l2')
train_features_crop = normalize(train_features_crop, norm='l2')
val_features_crop = normalize(val_features_crop, norm='l2')
nb_classes = 6
labels = [i for i in range(nb_classes)]
print("test with global information..")
svc(train_features_ori,train_labels,val_features_ori,val_labels,labels,'global')
print("test with partial information..")
svc(train_features_crop,train_labels,val_features_crop,val_labels,labels,'crop')
print("test with global and local information..")
svc(train_features,train_labels,val_features,val_labels,labels,'merge')