def main():
thicc = 2
# TODO: set sleep threshold to 8
score = 0 # To evaluate the state of the driver(drowsy or not)
path = os.getcwd()
font = cv2.FONT_HERSHEY_COMPLEX_SMALL
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
height, width = frame.shape[:2]
classifier = Classifier(frame)
left_eye_pred = classifier.left_eye()
right_eye_pred = classifier.right_eye()
if left_eye_pred == 0 and right_eye_pred == 0:
score += 1
cv2.putText(frame, "Asleep", (10, height - 20), font, 1,
(255, 255, 255), 1, cv2.LINE_AA)
else:
score = -1
cv2.putText(frame, "Awake", (10, height - 20), font, 1,
(255, 255, 255), 1, cv2.LINE_AA)
if score < 0:
score = 0
cv2.putText(frame, "Score: " + str(score), (100, height - 20), font, 1,
(255, 255, 255), 1, cv2.LINE_AA)
if score > 15: # Using 15 as threshold to say the driver has had his/her eyes closed for too long
# Driver is feeling sleepy so we play the alarm
cv2.imwrite(os.path.join(path,
str(datetime.now) + '.jpg'),
frame) # TODO: Fix code crash after sound play
playsound() # Play sound
if thicc < 16:
thicc += 2
else:
thicc -= 2
if thicc < 2:
thicc = 2
cv2.readOpticalFlow(frame, (0, 0), (width, height), (0, 0, 255),
thicc)
cv2.imshow('frame', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
def test_with_image(img_path):
thicc = 2
score = 0 # To evaluate the state of the driver(drowsy or not)
path = os.getcwd()
font = cv2.FONT_HERSHEY_COMPLEX_SMALL
img = cv2.imread(img_path)
height, width = img.shape[:2]
classifier = Classifier(img)
left_eye_pred = classifier.left_eye()
right_eye_pred = classifier.right_eye()
if left_eye_pred == 0 and right_eye_pred == 0:
score += 1
cv2.putText(img, "Closed", (10, height - 20), font, 1, (255, 255, 255),
1, cv2.LINE_AA)
else:
score = -1
cv2.putText(img, "Opened", (10, height - 20), font, 1, (255, 255, 255),
1, cv2.LINE_AA)
if score < 0:
score = 0
cv2.putText(img, "Score: " + str(score), (100, height - 20), font, 1,
(255, 255, 255), 1, cv2.LINE_AA)
if score > 15: # Using 15 as threshold to say the driver has had his/her eyes closed for too long
# Driver is feeling sleepy so we play the alarm
cv2.imwrite(os.path.join(path, str(datetime.now) + '.jpg'), img)
#playsound() # Play sound
if thicc < 16:
thicc += 2
else:
thicc -= 2
if thicc < 2:
thicc = 2
cv2.readOpticalFlow(img, (0, 0), (width, height), (0, 0, 255), thicc)
cv2.imshow('frame', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
def test_edgeaware_interpolator(self):
# readGT
MAX_DIF = 1.0
MAX_MEAN_DIF = 1.0 / 256.0
src = cv.imread(self.find_file("cv/optflow/RubberWhale1.png"),
cv.IMREAD_COLOR)
self.assertFalse(src is None)
ref_flow = cv.readOpticalFlow(
self.find_file(
"cv/sparse_match_interpolator/RubberWhale_reference_result.flo"
))
self.assertFalse(ref_flow is None)
matches = np.genfromtxt(
self.find_file(
"cv/sparse_match_interpolator/RubberWhale_sparse_matches.txt")
).astype(np.float32)
from_points = matches[:, 0:2]
to_points = matches[:, 2:4]
interpolator = cv.ximgproc.createEdgeAwareInterpolator()
interpolator.setK(128)
interpolator.setSigma(0.05)
interpolator.setUsePostProcessing(True)
interpolator.setFGSLambda(500.0)
interpolator.setFGSSigma(1.5)
dense_flow = interpolator.interpolate(src, from_points, src, to_points)
self.assertTrue(cv.norm(dense_flow, ref_flow, cv.NORM_INF) <= MAX_DIF)
self.assertTrue(
cv.norm(dense_flow, ref_flow, cv.NORM_L1) <= (
MAX_MEAN_DIF * dense_flow.shape[0] * dense_flow.shape[1]))
def get_img_pair_and_opticalflow(self, idx):
sharp_file_path = self.sharp_file_paths[idx]
blur_file_path = sharp_file_path.replace("sharp", "blur")
opticalflow_file_path = sharp_file_path.replace("sharp", "opticalflow")
opticalflow_file_path = opticalflow_file_path.replace("png", "flo")
img_input = Image.open(blur_file_path).convert('RGB')
img_target = Image.open(sharp_file_path).convert('RGB')
opticalflow_1 = cv2.readOpticalFlow(opticalflow_file_path)
opticalflow_folder = os.path.dirname(opticalflow_file_path)
flow1_name = os.path.basename(opticalflow_file_path)
files = os.listdir(opticalflow_folder)
index = files.index(flow1_name)
opticalflow_2 = cv2.readOpticalFlow(os.path.join(opticalflow_folder, files[index+1]))
return img_input, img_target, opticalflow_1, opticalflow_2
def test_ric_interpolator(self):
# readGT
MAX_DIF = 6.0
MAX_MEAN_DIF = 60.0 / 256.0
src0 = cv.imread(self.find_file("cv/optflow/RubberWhale1.png"),
cv.IMREAD_COLOR)
self.assertFalse(src0 is None)
src1 = cv.imread(self.find_file("cv/optflow/RubberWhale2.png"),
cv.IMREAD_COLOR)
self.assertFalse(src1 is None)
ref_flow = cv.readOpticalFlow(
self.find_file(
"cv/sparse_match_interpolator/RubberWhale_reference_result.flo"
))
self.assertFalse(ref_flow is None)
matches = np.genfromtxt(
self.find_file(
"cv/sparse_match_interpolator/RubberWhale_sparse_matches.txt")
).astype(np.float32)
from_points = matches[:, 0:2]
to_points = matches[:, 2:4]
interpolator = cv.ximgproc.createRICInterpolator()
interpolator.setK(32)
interpolator.setSuperpixelSize(15)
interpolator.setSuperpixelNNCnt(150)
interpolator.setSuperpixelRuler(15.0)
interpolator.setSuperpixelMode(cv.ximgproc.SLIC)
interpolator.setAlpha(0.7)
interpolator.setModelIter(4)
interpolator.setRefineModels(True)
interpolator.setMaxFlow(250)
interpolator.setUseVariationalRefinement(True)
interpolator.setUseGlobalSmootherFilter(True)
interpolator.setFGSLambda(500.0)
interpolator.setFGSSigma(1.5)
dense_flow = interpolator.interpolate(src0, from_points, src1,
to_points)
self.assertTrue(cv.norm(dense_flow, ref_flow, cv.NORM_INF) <= MAX_DIF)
self.assertTrue(
cv.norm(dense_flow, ref_flow, cv.NORM_L1) <= (
MAX_MEAN_DIF * dense_flow.shape[0] * dense_flow.shape[1]))
def evaluate_of(of_alg, name):
print("\nEvaluating: " + name + " algorithm:")
sum_mepe = 0
for dataset in datasets:
first = cv.imread('./data/' + dataset + "/frame10.png",
cv.IMREAD_GRAYSCALE)
second = cv.imread('./data/' + dataset + "/frame11.png",
cv.IMREAD_GRAYSCALE)
flow_gt = cv.readOpticalFlow('./flow/' + dataset + '/flow10.flo')
#flow_gt_img = fl.flow_to_image(flow_gt)
#plt.imshow(flow_gt_img)
#plt.show()
flow = of_alg.calc(first, second, None)
#flow_img = fl.flow_to_image(flow_gt)
#plt.imshow(flow_img)
#plt.show()
mepe = fl.evaluate_flow(flow_gt, flow)
print("\tResult for " + dataset + " dataset: EPE= %f" % (mepe))
sum_mepe += mepe
print("\tAverage EPE of " + name + " algorithm is %f" % (sum_mepe / 8))
def test_with_image(img_path):
VGG_Face = Vgg_face_dag()
VGG_Face = vgg_face_dag(VGG_Face, "src/models/vgg_face_dag.pth")
thicc = 2
score = 0 # To evaluate the state of the driver(drowsy or not)
frame_count = 0
frames = []
path = os.getcwd()
font = cv2.FONT_HERSHEY_COMPLEX_SMALL
img = cv2.imread(img_path)
height, width = img.shape[:2]
classifier = Classifier(img)
left_eye_pred = classifier.left_eye()
right_eye_pred = classifier.right_eye()
frames.append(img)
drunk_pred = classifier.drunk_pred(frames, VGG_Face)
if drunk_pred == 1:
cv2.putText(img, "Drunk", (10, 20), font, 1, (255, 255, 255), 1,
cv2.LINE_AA)
else:
cv2.putText(img, "Sober", (10, 20), font, 1, (255, 255, 255), 1,
cv2.LINE_AA)
if left_eye_pred == 0 and right_eye_pred == 0:
score += 1
cv2.putText(img, "Asleep", (10, height - 20), font, 1, (255, 255, 255),
1, cv2.LINE_AA)
else:
score = -1
cv2.putText(img, "Awake", (10, height - 20), font, 1, (255, 255, 255),
1, cv2.LINE_AA)
if score < 0:
score = 0
cv2.putText(img, "Score: " + str(score), (100, height - 20), font, 1,
(255, 255, 255), 1, cv2.LINE_AA)
if score > 8: # Using 15 as threshold to say the driver has had his/her eyes closed for too long
# Driver is feeling sleepy so we play the alarm
cv2.imwrite(os.path.join(path, str(datetime.now) + '.jpg'), img)
#playsound() # Play sound
if thicc < 16:
thicc += 2
else:
thicc -= 2
if thicc < 2:
thicc = 2
cv2.readOpticalFlow(img, (0, 0), (width, height), (0, 0, 255), thicc)
cv2.imshow('frame', img)
k = cv2.waitKey(0)
if k == 27:
cv2.destroyAllWindows()
def preprocess_dataset(data_dir):
sub_folders = os.listdir(data_dir)
sharp_file_paths = []
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
df = pd.DataFrame(columns=[
'sharp_path', 'blur_path', 'opticalflow_1_path', 'opticalflow_2_path'
])
folder_number = 0
for folder_name in sub_folders:
if (folder_name == 'blur_img_all.pt'
or folder_name == 'sharp_img_all.pt'
or folder_name == 'opticalflow_all.pt'
or folder_name == 'log.xlsx' or folder_name == 'log.csv'
or folder_name == '.ipynb_checkpoints'):
continue
folder_number += 1
sharp_sub_folder = os.path.join(data_dir, folder_name, 'sharp')
sharp_file_names = os.listdir(sharp_sub_folder)
for file_name in sharp_file_names:
if (sharp_file_names.index(file_name) != 0
and sharp_file_names.index(file_name) !=
len(sharp_file_names) - 1):
sharp_file_path = os.path.join(sharp_sub_folder, file_name)
sharp_file_paths.append(sharp_file_path)
n_samples = len(sharp_file_paths)
blur_img_all = torch.zeros((n_samples, 3, 256, 256), dtype=torch.float16)
sharp_img_all = torch.zeros((n_samples, 3, 256, 256), dtype=torch.float16)
opticalflow_all = torch.zeros((n_samples, 2, 2, 128, 128),
dtype=torch.float16)
for idx in range(n_samples):
sharp_path = sharp_file_paths[idx]
blur_path = sharp_path.replace("sharp", "blur")
opticalflow_1_path = sharp_path.replace("sharp", "opticalflow")
opticalflow_1_path = opticalflow_1_path.replace("png", "flo")
opticalflow_folder = os.path.dirname(opticalflow_1_path)
flow1_name = os.path.basename(opticalflow_1_path)
files = os.listdir(opticalflow_folder)
index = files.index(flow1_name)
opticalflow_2_path = os.path.join(opticalflow_folder, files[index + 1])
"""
print(os.path.basename(blur_path))
print(os.path.basename(sharp_path))
print(flow1_name)
print(files[index+1])
"""
print("prepocessing: ", idx)
new_row = {
'sharp_path': os.path.basename(sharp_path),
'blur_path': os.path.basename(blur_path),
'opticalflow_1_path': flow1_name,
'opticalflow_2_path': files[index + 1]
}
df = df.append(new_row, ignore_index=True)
img_input = Image.open(blur_path).convert('RGB')
img_target = Image.open(sharp_path).convert('RGB')
opticalflow_1 = cv2.readOpticalFlow(opticalflow_1_path)
opticalflow_2 = cv2.readOpticalFlow(opticalflow_2_path)
img_input = transforms.ToTensor()(img_input).to(device)
img_target = transforms.ToTensor()(img_target).to(device)
opticalflow_1 = torch.Tensor(opticalflow_1).to(device)
opticalflow_2 = torch.Tensor(opticalflow_2).to(device)
img_input, img_target, opticalflow_1, opticalflow_2 = transform(
img_input, img_target, opticalflow_1, opticalflow_2, 256)
blur_img_all[idx, :, :, :] = img_input
sharp_img_all[idx, :, :, :] = img_target
opticalflow_all[idx, 0, :, :, :] = opticalflow_1
opticalflow_all[idx, 1, :, :, :] = opticalflow_2
torch.save(blur_img_all, os.path.join(data_dir, 'blur_img_all.pt'))
torch.save(sharp_img_all, os.path.join(data_dir, 'sharp_img_all.pt'))
torch.save(opticalflow_all, os.path.join(data_dir, 'opticalflow_all.pt'))
#blur_img_all, tensor, range[0,1], dtype = torch.float16, size = (2059, 3, 256, 256)
#sharp_img_all, tensor, range[0,1], dtype = torch.float16, size = (2059, 3, 256, 256)
#opticalflow_all, tensor, range[0,255], dtype = torch.float16, size = (2059, 2, 2, 128, 128)
# save prepocess file path
df.to_excel(os.path.join(data_dir, 'log.xlsx'))
df.to_csv((os.path.join(data_dir, 'log.csv')), index=False)
import cv2
from warping.warp import visulize_flow_file, optical_flow_warping
import torch
import os
if __name__ == '__main__':
base_dir = os.path.dirname(__file__)
# visualize the optical flow here
visulize_flow_file(os.path.join(base_dir, 'reference_frame_0010.flo'))
test = cv2.readOpticalFlow(
os.path.join(base_dir, 'reference_frame_0010.flo'))
test = torch.from_numpy(test)
H, W, C = test.shape
test = test.permute(2, 0, 1).unsqueeze(0)
test_img2 = cv2.imread(os.path.join(base_dir,
'frame_0011.png'))[:, :, ::-1]
test_img2 = test_img2 / 255.0
test_img2 = torch.from_numpy(test_img2).float()
test_img2 = test_img2.permute(2, 0, 1).unsqueeze(0)
out = optical_flow_warping(test_img2, test)[0]
out = out.permute(1, 2, 0).numpy()
# plt.imshow(out)
# plt.show()
cv2.imwrite(os.path.join(base_dir, 'warped.png'), out[:, :, ::-1] * 255)
def main():
VGG_Face = Vgg_face_dag()
VGG_Face = vgg_face_dag(VGG_Face, "src/models/vgg_face_dag.pth")
thicc = 2
score = 0 # To evaluate the state of the driver(drowsy or not)
frame_count = 0
frames = []
path = os.getcwd()
font = cv2.FONT_HERSHEY_COMPLEX_SMALL
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
height, width = frame.shape[:2]
classifier = Classifier(frame)
left_eye_pred = classifier.left_eye()
right_eye_pred = classifier.right_eye()
frames.append(frame)
if len(frames) == 10:
drunk_pred = classifier.drunk_pred(frames, VGG_Face)
frames = []
if drunk_pred == 1:
cv2.putText(frame, "Drunk", (width-10, height-20), font, 1, (255, 255, 255), 1, cv2.LINE_AA)
else:
cv2.putText(frame, "Sober", (width-10, height-20), font, 1, (255, 255, 255), 1, cv2.LINE_AA)
if left_eye_pred == 0 and right_eye_pred == 0:
score += 1
cv2.putText(frame, "Asleep", (10, height-20), font, 1, (255, 255, 255), 1, cv2.LINE_AA)
else:
score = -1
cv2.putText(frame, "Awake", (10, height-20), font, 1, (255, 255, 255), 1, cv2.LINE_AA)
if score < 0:
score = 0
cv2.putText(frame, "Score: "+str(score), (100, height-20), font, 1, (255, 255, 255), 1, cv2.LINE_AA)
if score >= 8: # Using 8 as threshold to say the driver has had his/her eyes closed for too long
# Driver is feeling sleepy so we play the alarm
#cv2.imwrite(os.path.join(path, str(datetime.now)+'.jpg'), frame)
# TODO: Fix code crash after sound play
playsound() # Play sound
if thicc < 16:
thicc += 2
else:
thicc -= 2
if thicc < 2:
thicc = 2
cv2.readOpticalFlow(frame, (0, 0), (width, height), (0, 0, 255), thicc)
cv2.imshow('frame', frame)
k = cv2.waitKey(1)
if k == 27:
cv2.destroyAllWindows()
parser.add_argument('-f', '--flow_name', required=True)
args = parser.parse_args()
img = cv2.imread('%s/%s' % (args.image_dir, args.image_name))
# print('img:',img.shape)
# img = img[:,:128,:]
# print('img:',img.shape)
# img = cv2.resize(img, (int(img.shape[0]*2.0), int(img.shape[1]*2.0)))
# print('img:',img.shape)
# cv2.imwrite('warped_image/moving-gif/00130/0.png', img)
img = torch.from_numpy(img.astype(np.float32))
img = img.unsqueeze(0).permute(0, 3, 1, 2)
# print('img:',img.size())
img = Variable(img).cuda()
flow = cv2.readOpticalFlow('%s/%s' % (args.flow_dir, args.flow_name))
flow = torch.from_numpy(flow.astype(np.float32))
flow = flow.unsqueeze(0).permute(0, 3, 1, 2)
# print('flow:',flow.size())
flow = Variable(flow).cuda()
warped_img = warp(img, flow)
warped_img = warped_img.squeeze(0).permute(1, 2, 0)
# print('warped_img:',warped_img.size())
warped_img = warped_img.cpu()
warped_img = np.float32(warped_img)
cv2.imwrite('Input/Paint/moving-gif_00058_%s.png' % (args.flow_name[:-4]),
warped_img)
# cv2.imwrite('Input/Editing/3-VAM9owl-o/start_scale=2,input=0.5/%s.png' % ( args.flow_name[:-4]), warped_img)
# if __name__ == '__main__':