def get_valid_box(image, points):
"""
Try to get a valid face box which meets the requirments.
The function follows these steps:
1. Try method 1, if failed:
2. Try method 0, if failed:
3. Return None
"""
# Try method 1 first.
def _get_postive_box(raw_boxes, points):
for box in raw_boxes:
# Move box down.
diff_height_width = (box[3] - box[1]) - (box[2] - box[0])
offset_y = int(abs(diff_height_width / 2))
box_moved = move_box(box, [0, offset_y])
# Make box square.
square_box = get_square_box(box_moved)
# Remove false positive boxes.
if points_in_box(points, square_box):
return square_box
return None
# Try to get a positive box from face detection results.
_, raw_boxes = fd.get_facebox(image, threshold=0.5)
positive_box = _get_postive_box(raw_boxes, points)
if positive_box is not None:
if box_in_image(positive_box, image) is True:
return positive_box
return fit_box(positive_box, image, points)
def extract_face(file, tail, count):
"""Extract face area from image."""
image = read_image(file)
conf, raw_boxes = fd.get_facebox(image=image, threshold=0.9)
# fd.draw_result(image, conf, raw_boxes)
for box in raw_boxes:
# Move box down.
diff_height_width = (box[3] - box[1]) - (box[2] - box[0])
offset_y = int(abs(diff_height_width / 2))
box_moved = pt.move_box(box, [0, offset_y])
# Make box square.
facebox = pt.get_square_box(box_moved)
face_image = image[facebox[1]:facebox[3], facebox[0]:facebox[2]]
# Save the Image.
image_url = os.path.join(TARGET_DIR, str(count) + '-' + tail)
if face_image.shape[0] * face_image.shape[1] != 0:
preview_img = face_image.copy()
preview_img = cv2.resize(preview_img, (512, 512))
cv2.imshow('preview', preview_img)
if cv2.waitKey() == 27:
face_image = cv2.resize(face_image, (128, 128))
cv2.imwrite(image_url, face_image)
print("New file saved:", image_url)
count += 1
return face_image
def preview(point_file):
"""
Preview points on image.
"""
# Read the points from file.
raw_points = read_points(point_file)
# Safe guard, make sure point importing goes well.
assert len(raw_points) == 68, "The landmarks should contain 68 points."
# Read the image.
head, tail = os.path.split(point_file)
image_file = tail.split('.')[-2]
img_jpg = os.path.join(head, image_file + ".jpg")
img_png = os.path.join(head, image_file + ".png")
if os.path.exists(img_jpg):
img = cv2.imread(img_jpg)
else:
img = cv2.imread(img_png)
#Faust add+[
conf, facebox = fd.get_facebox(img, threshold=0.5)
fd.draw_result(img, conf, facebox)
#Faust add+]
# Fast check: all points are in image.
if points_are_valid(raw_points, img) is False:
return None
# Get the valid facebox.
facebox = get_valid_box(img, raw_points)
if facebox is None:
print("Using minimal box.")
facebox = get_minimal_box(raw_points)
# fd.draw_box(img, [facebox], box_color=(255, 0, 0))
# Extract valid image area.
face_area = img[facebox[1]:facebox[3], facebox[0]:facebox[2]]
# Check if resize is needed.
width = facebox[2] - facebox[0]
height = facebox[3] - facebox[1]
if width != height:
print('opps!', width, height)
if (width != 128) or (height != 128):
face_area = cv2.resize(face_area, (256, 256))
# Show the result.
cv2.imshow("face", face_area)
if cv2.waitKey(10) == 27:
cv2.waitKey()
def extract_face(image):
conf, raw_boxes = fd.get_facebox(image=image, threshold=0.9)
for box in raw_boxes:
diff_height_width = (box[3] - box[1]) - (box[2] - box[0])
offset_y = int(abs(diff_height_width / 2))
box_moved = pt.move_box(box, [0, offset_y])
facebox = pt.get_square_box(box_moved)
if pt.box_in_image(facebox, image):
return facebox
return None
def extract_face(file):
global new_img
"""Extract face area from image."""
image = read_image(file)
conf, raw_boxes = fd.get_facebox(image=image, threshold=0.9)
# fd.draw_result(image, conf, raw_boxes)
for box in raw_boxes:
# Move box down.
diff_height_width = (box[3] - box[1]) - (box[2] - box[0])
offset_y = int(abs(diff_height_width / 2))
box_moved = pt.move_box(box, [0, offset_y])
# Make box square.
facebox = pt.get_square_box(box_moved)
face_image = image[facebox[1]:facebox[3], facebox[0]:facebox[2]]
if face_image.shape[0] * face_image.shape[1] != 0:
preview_img = face_image.copy()
new_img = cv2.resize(preview_img, (128, 128))
return new_img
def change_box(img, DATA_DIR, pointThr=40):
'''
实现框的转换，使其只剩下有特征点标记的框以及改变其大小，使其能包含所有的框
:param img: 图片
:pointthr:超过多少个点在框里判定这个框为有效框
:data_dir:数据路径
:return: 新的框的坐标
'''
confidences, faceboxes = fd.get_facebox(img, 0.5)
points = pt.read_points(DATA_DIR)
newFacebox = []
m = 0
for i in faceboxes:
for j in points:
if ((i[0] < j[0] < i[2]) and (i[1] < j[1] < i[3])):
m = m + 1
if m > pointThr:
newFacebox.append(i)
m = 0
# print(faceboxes)
# print(newFacebox)
# print("$$$$$$$$$")
newConf = [confidences[faceboxes.index(newFacebox[0])]]
left_x, left_y, right_x, right_y = newFacebox[0]
#将边界框的位置总体向下移动，移动的大小为边界框的高度与宽度之差的一半
height = right_y - left_y
width = right_x - left_x
left_y = left_y + (height - width) / 2
right_y = right_y + (height - width) / 2
#使得其变为正方形
if height != width:
left_x = left_x - (height - width) / 2
right_x = right_x + (height - width) / 2
newFacebox = [[int(left_x), int(left_y), int(right_x), int(right_y)]]
# print(newConf,newFacebox)
return newConf, newFacebox
def process(video_dir, save_dir, train_file_list):
minsize = 20
#error =
# if not os.path.exists(train_file_list):
# head, tail = os.path.split(train_file_list)
# os.makedirs(head)
total_face = 0
with open(train_file_list, 'a+') as f_trainList:
for dir_path, dir_names, _ in os.walk(video_dir):
# frame_count = 0
for dir_name in dir_names:
print('processing directory: ' + dir_path + '/' + dir_name)
video_dir_name = os.path.join(dir_path, dir_name)
if dir_name in ['Normal']:
label = '0'
elif dir_name in ['Talking']:
label = '0'
elif dir_name in ['Yawning']:
label = '1'
else:
print("Too bad, label invalid.")
continue
video_names = os.listdir(video_dir_name)
for video_name in video_names:
# video = cv2.VideoCapture()
frame_count = -1
cap = cv2.VideoCapture(os.path.join(video_dir_name, video_name))
if cap.isOpened():
ftotal = cap.get(cv2.CAP_PROP_FRAME_COUNT)
read_success = True
else:
read_success = False
# cv2.imwrite(img_save_path, img)
while read_success:
read_success, img = cap.read()
try:
img.shape
except:
break
frame_count += 1
if frame_count % SAMPLE_STEP == 0:
# img = img.transpose(2, 0, 1)
conf, raw_boxes = fd.get_facebox(image=img, threshold=0.7)
if len(raw_boxes) == 0:
# if face can't be detected between n frames
print('***too sad, the face can not be detected!')
continue
elif len(raw_boxes) == 1:
bbox = raw_boxes[0]
elif len(raw_boxes) > 1:
#print('---multi detect!')
# idx = np.argsort(-raw_boxes[:, 4])
# TODO. sort
bbox = raw_boxes[0]
bbox = rectify_bbox(bbox, img)
x = int(bbox[0])
y = int(bbox[1])
w = int(bbox[2] - bbox[0])
h = int(bbox[3] - bbox[1])
face = img[y:y + h, x:x + w, :]
# cv2.imwrite('face.jpg', face)
face_sp = np.shape(face)
#if face_sp[0] <= 16 or face_sp[1] <= 16:
# continue
face_resize = face
face_resize = cv2.resize(face_resize, (CROPPED_WIDTH, CROPPED_HEIGHT))
if DEBUG:
cv2.imshow('face', face)
ch = cv2.waitKey(40000) & 0xFF
cv2.imshow('face_trans', face_resize)
ch = cv2.waitKey(40000) & 0xFF
img = drawBoxes(img, bbox)
# for i in range(5):
# cv2.circle(img, (point[i], point[5+i]), 2, [0, 0, 255])
cv2.imshow('img', img)
ch = cv2.waitKey(40000) & 0xFF
if ch == 27:
break
total_face += 1
img_file_name = video_name.split('.')[0]
img_file_name = img_file_name + '-' + str(total_face) + '_' + str(label) + '.jpg'
# f_trainList.write(img_file_name + ' ' + label + '\n')
img_save_path = os.path.join(save_dir, img_file_name)
f_trainList.write(img_save_path + ' ' + label + '\n')
cv2.imwrite(img_save_path, face_resize)
# print('Processing %d / %d' % (frame_count, ftotal))
cap.release()
print('Total face img: {}'.format(total_face))
