def __getitem__(self, index):
# loading and preprocessing. TODO move them to transform classes
# buffer = self.loadvideo(self.fname[index])
try:
buffer = produce_frames(self.fname[index])
except:
index = 0
buffer = produce_frames(self.fname[index])
if self.mode == 'train':
buffer = self.randomflip(buffer)
# print("---------------")
# print(buffer.shape)
# size =(224,224)
# if size要改，crop函数里面的随机裁剪也要跟着改
# temp = []
# for i in buffer:
# # cv2.imshow("1",i)
# # cv2.waitKey(10)
# temp.append(i)
if self.mode == 'train':
a = 0
for i in buffer:
a = a + 1
cv2.imwrite(','.join(self.fname[index][0].split('/')) + '.jpg',
i)
break
print(self.fname[index][0])
buffer = np.concatenate(buffer, axis=2)
# print(buffer.shape)
# ________________________________________debug
# b = video_transform(buffer)
#
# b= b.numpy().transpose((1,2,3,0))*255
# a =0
# for i in b:
# a =a+1
# cv2.imwrite(str(a)+str(index)+'.jpg',i)
# a = 0
if self.mode == 'test':
return test_transform(buffer), torch.from_numpy(
np.array(self.labels[index])).long()
else:
# print(video_transform(buffer).shape,"123")
return video_transform(buffer), torch.from_numpy(
np.array(self.labels[index])).long(), self.fname[index]
def __getitem__(self, index):
# loading and preprocessing. TODO move them to transform classes
frame = cv2.imread(self.fname[index])
# frame = cv2.imread(self.fname[index])
# a = re.findall(r'Camera(\d+)',self.fname[index])[0]
# if a=='1':
# frame = cv2.imread(self.fname[index])
# frame = frame[172:480,59:488,:]
#
# if a=='2':
# frame = cv2.imread(self.fname[index])
# frame = frame[73:459,208:394,:]
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
if self.mode == 'train':
if np.random.random() < 0.5:
frame = cv2.flip(frame, flipCode=1)
# size =(112,112)
# if size要改，crop函数里面的随机裁剪也要跟着改
# print(buffer.shape)
# ________________________________________debug
# b = video_transform(frame)
# b= b.numpy().transpose((1,2,3,0))
#
# for i in b:
# cv2.imshow(str(self.labels[index]),i)
# cv2.waitKey(1000)
if self.mode == 'test':
return test_transform(frame), torch.from_numpy(
np.array(self.labels[index])).long()
else:
# print(video_transform(buffer).shape,"123")
return video_transform(frame), torch.from_numpy(
np.array(self.labels[index])).long()
def submit_video_2():
dic = {}
dic[0] = 7
dic[1] = 11
dic[2] = 6
dic[3] = 8
dic[4] = 10
model = TSN(5, 5, modality='RGB', partial_bn=True, is_shift=True)
pretrained_dict = torch.load(
'/data/xudw/temporal-shift-module-master/Fall_down_5_frame_5.13.pth.tar',
map_location='cpu')
try:
model_dict = model.module.state_dict()
except AttributeError:
model_dict = model.state_dict()
pretrained_dict = {
k: v
for k, v in pretrained_dict.items() if k in model_dict
}
print("load pretrain model")
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
model = model.cuda()
model.eval()
path = '/data/xudw/labels_submit_video_camera2/' #come from the singe frame label
import os
import csv
import re
import cv2
import pandas as pd
import numpy as np
with torch.no_grad():
for Subject in os.listdir(path):
c1 = re.findall('(\d+)', Subject)
Subject_id = c1[0]
for Activity in os.listdir(path + Subject):
c2 = re.findall('(\d+)', Activity)
Activity_id = c2[0]
for csv_1 in os.listdir(path + Subject + '/' + Activity):
csv_data = pd.read_csv(path + Subject + '/' + Activity +
'/' + csv_1)
c3 = re.findall('(\d+)', csv_1)
Trial_id = c3[-1]
loc = np.array(csv_data["Timestamp"])
loc = list(loc)
label = np.array(csv_data['Tag'])
label = list(label)
print(path + Subject + '/' + Activity + '/' + csv_1)
# csv_data.close()
f = open(path + Subject + '/' + Activity + '/' + csv_1,
'w',
encoding='utf-8',
newline='')
# 2. 基于文件对象构建 csv写入对象
csv_writer = csv.writer(f)
# 3. 构建列表头
csv_writer.writerow(["Timestamp", "Tag"])
frames = sorted(
os.listdir('/data/xudw/Fall_Down_data/' + 'Subject' +
Subject_id + 'Activity' + Activity_id +
'Trial' + Trial_id + 'Camera2'))
for i in range(len(loc)):
if 0 <= i <= 3:
print(loc[i], label[i])
csv_writer.writerow([loc[i], label[i]])
else:
buffer = []
frame_1 = cv2.imread('/data/xudw/Fall_Down_data/' +
'Subject' + Subject_id +
'Activity' + Activity_id +
'Trial' + Trial_id +
'Camera2/' + frames[i - 4])
frame_1 = frame_1[73:459, 208:394, :]
# if a == '2':
# frame = cv2.imread(i)
# frame = frame[73:459, 208:394, :]
frame_1 = cv2.cvtColor(frame_1, cv2.COLOR_BGR2RGB)
buffer.append(frame_1)
frame_2 = cv2.imread('/data/xudw/Fall_Down_data/' +
'Subject' + Subject_id +
'Activity' + Activity_id +
'Trial' + Trial_id +
'Camera2/' + frames[i - 3])
frame_2 = frame_2[73:459, 208:394, :]
frame_2 = cv2.cvtColor(frame_2, cv2.COLOR_BGR2RGB)
buffer.append(frame_2)
frame_3 = cv2.imread('/data/xudw/Fall_Down_data/' +
'Subject' + Subject_id +
'Activity' + Activity_id +
'Trial' + Trial_id +
'Camera2/' + frames[i - 2])
frame_3 = frame_3[73:459, 208:394, :]
frame_3 = cv2.cvtColor(frame_3, cv2.COLOR_BGR2RGB)
buffer.append(frame_3)
frame_4 = cv2.imread('/data/xudw/Fall_Down_data/' +
'Subject' + Subject_id +
'Activity' + Activity_id +
'Trial' + Trial_id +
'Camera2/' + frames[i - 1])
frame_4 = frame_4[73:459, 208:394, :]
frame_4 = cv2.cvtColor(frame_4, cv2.COLOR_BGR2RGB)
buffer.append(frame_4)
frame_5 = cv2.imread('/data/xudw/Fall_Down_data/' +
'Subject' + Subject_id +
'Activity' + Activity_id +
'Trial' + Trial_id +
'Camera2/' + frames[i])
frame_5 = frame_5[73:459, 208:394, :]
frame_5 = cv2.cvtColor(frame_5, cv2.COLOR_BGR2RGB)
buffer.append(frame_5)
temp = []
for j in buffer:
temp.append(j)
buffer = np.concatenate(temp, axis=2)
labels = model(
test_transform(buffer).unsqueeze(dim=0).cuda())
# print(label)
_, predicted = torch.max(labels.data, 1)
print(loc[i])
print(label[i])
print("----" * 10)
print(loc[i], dic[int(predicted)])
csv_writer.writerow(
[loc[i], str(dic[int(predicted)])])
def submit_2():
dic = {}
dic[0] = 7
dic[1] = 11
dic[2] = 6
dic[3] = 8
dic[4] = 10
model = TSN(5,
1,
base_model='resnet34',
modality='RGB',
partial_bn=True,
is_shift=False)
pretrained_dict = torch.load(
'/data/xudw/temporal-shift-module-master/Fall_down_Single_test111_5.13.pth.tar',
map_location='cpu')
try:
model_dict = model.module.state_dict()
except AttributeError:
model_dict = model.state_dict()
pretrained_dict = {
k: v
for k, v in pretrained_dict.items() if k in model_dict
}
print("load pretrain model")
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
model = model.cuda()
model.eval()
path = '/data/xudw/labels_submit_2/'
import os
import csv
import re
import cv2
import pandas as pd
import numpy as np
with torch.no_grad():
for Subject in os.listdir(path):
c1 = re.findall('(\d+)', Subject)
Subject_id = c1[0]
for Activity in os.listdir(path + Subject):
c2 = re.findall('(\d+)', Activity)
Activity_id = c2[0]
for csv_1 in os.listdir(path + Subject + '/' + Activity):
csv_data = pd.read_csv(path + Subject + '/' + Activity +
'/' + csv_1)
c3 = re.findall('(\d+)', csv_1)
Trial_id = c3[-1]
loc = np.array(csv_data["Timestamp"])
loc = list(loc)
label = np.array(csv_data['Tag'])
label = list(label)
# csv_data.close()
f = open(path + Subject + '/' + Activity + '/' + csv_1,
'w',
encoding='utf-8',
newline='')
# 2. 基于文件对象构建 csv写入对象
csv_writer = csv.writer(f)
# 3. 构建列表头
csv_writer.writerow(["Timestamp", "Tag"])
frames = sorted(
os.listdir('/data/xudw/Fall_Down_data/' + 'Subject' +
Subject_id + 'Activity' + Activity_id +
'Trial' + Trial_id + 'Camera2'))
print(path + Subject + '/' + Activity + '/' + csv_1)
for i in range(len(loc)):
frame = cv2.imread('/data/xudw/Fall_Down_data/' +
'Subject' + Subject_id +
'Activity' + Activity_id + 'Trial' +
Trial_id + 'Camera2/' + frames[i])
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
labels = model(
test_transform(frame).unsqueeze(dim=0).cuda())
# print(label)
_, predicted = torch.max(labels.data, 1)
print(label[i])
print(dic[int(predicted)])
csv_writer.writerow([loc[i], str(dic[int(predicted)])])