def getListTestFile(test_stip_folder, test_label_save_file, index_of_actor):
test_stip_file_list = []
for i in range(1, number_action + 1):
folder_path = test_stip_folder + "\\Action_" + str(i)
temp_list_test_sample = getAllTextFileFromFolder(folder_path)
list_test_sample = removeTrainActorFiles(temp_list_test_sample,
actor_name, index_of_actor)
for j in range(0, len(list_test_sample)):
temp_file = test_stip_folder + str(i) + "\\" + list_test_sample[j]
test_label_save_file.write(str(i) + "\n")
test_stip_file_list.append(temp_file)
test_label_save_file.close()
return test_stip_file_list
def getHistogramByUsingRandomForestModel(rf_model_list, stip_samples_cam,
index_of_actor, histogram_cam_folder):
for i in range(1, number_action + 1):
rf = rf_model_list[i - 1]
leaf_list = getForestLeafs(rf_model_list[i - 1], n_estimators)
data_file_path = histogram_cam_folder + "\\histogram_action_" + str(
i) + ".txt"
label_file_path = histogram_cam_folder + "\\labels_" + str(i) + ".txt"
label_save_file = open(label_file_path, "a")
label_save_file.truncate()
save_file = open(data_file_path, "a")
save_file.truncate()
for j in range(1, number_action + 1):
train_folder = stip_samples_cam + "\\Action_" + str(j)
temp_list_train_sample = getAllTextFileFromFolder(train_folder)
list_train_sample = removeTestActorFiles(temp_list_train_sample,
actor_name,
index_of_actor)
for k in range(0, len(list_train_sample)):
stip_file_path = train_folder + '\\' + list_train_sample[k]
read_file = open(stip_file_path, "r")
read_file.readline()
read_file.readline()
read_file.readline()
file = read_file.readlines()
features = np.loadtxt(file, ndmin=2)
if features.size > 0:
X = features[:, 7:]
rf_output = rf.apply(X)
#print(str(rf_output))
histogram = convertRFOutputToHistogram(
rf_output, leaf_list, max_depth, n_estimators)
#print(str(histogram))
for item in histogram:
save_file.write("%s\t" % item)
save_file.write("\n")
if j == i:
label_save_file.write("1\n")
else:
label_save_file.write("0\n")
save_file.close()
label_save_file.close()
return None
def randomDescriptorForTrainingRandomForest(index_of_actor, stip_samples_cam,
rf_train_cam):
for i in range(1, number_action + 1):
# positive descriptors
pos_count = 0
positive_folder = stip_samples_cam + "\\Action_" + str(i)
temp_list_positive_file = getAllTextFileFromFolder(positive_folder)
list_positive_file = removeTestActorFiles(temp_list_positive_file,
actor_name, index_of_actor)
rf_train_file_path = rf_train_cam + "\\train_rf_" + str(i) + ".txt"
save_file = open(rf_train_file_path, "a")
save_file.truncate()
rf_label_file_path = rf_train_folder + "labels_" + str(i) + ".txt"
file_label = open(rf_label_file_path, "a")
file_label.truncate()
for j in range(0, len(list_positive_file)):
stip_file_path = positive_folder + '\\' + list_positive_file[j]
read_file = open(stip_file_path, "r")
read_file.readline()
read_file.readline()
read_file.readline()
while True:
if pos_count < number_descriptor:
line = read_file.readline()
if line == "":
read_file.close()
break
else:
save_file.write(line)
pos_count += 1
file_label.write("1\n")
else:
break
# get negative descriptors by random 11 others actions
list_action_random = []
k = 1
while len(list_action_random) < number_action - 1:
if k != i:
list_action_random.append(k)
k += 1
for j in range(0, len(list_action_random)):
neg_count = 0
negative_folder = stip_samples_cam + "\\Action_" + str(
list_action_random[j])
temp_list_positive_file = getAllTextFileFromFolder(negative_folder)
list_negative_file = removeTestActorFiles(temp_list_positive_file,
actor_name,
index_of_actor)
print(len(list_negative_file))
while neg_count < 5 * (pos_count / (number_action - 1)):
k = rand.choice(range(0, number_train_sample_per_action))
stip_file_path = negative_folder + '\\' + list_negative_file[k]
read_file = open(stip_file_path, "r")
read_file.readline()
read_file.readline()
read_file.readline()
while True:
if neg_count < 5 * (pos_count / (number_action - 1)):
line = read_file.readline()
if line == "":
read_file.close()
break
else:
save_file.write(line)
neg_count += 1
file_label.write("2\n")
else:
break
file_label.close()
save_file.close()
return None
label_file_path = "E:\\A-DATA\\histogram-train-svm\\Cam3\\labels_"
test_label_file_path = "E:\\A-DATA\\test-label\\labels.txt"
number_descriptor = 600
number_neg_class = 8
number_train_sample_per_action = 33
number_test_sample_per_action = 3
number_action = 12
###########################################################################
# Random 800 descriptors positive and negative for training Random Forest #
###########################################################################
for i in range(1, number_action + 1):
# positive descriptors
pos_count = 0
list_positive_file = getAllTextFileFromFolder(stip_folder + str(i))
data_file_path = rf_train_data + str(i) + ".txt"
save_file = open(data_file_path, "a")
save_file.truncate()
for j in range(0, len(list_positive_file)):
stip_file_path = stip_folder + str(i) + '\\' + list_positive_file[j]
read_file = open(stip_file_path, "r")
read_file.readline()
read_file.readline()
read_file.readline()
while True:
if pos_count < number_descriptor:
line = read_file.readline()
if line == "":
read_file.close()