1. 在图像中裁剪人脸区域
import face_alignment
import skimage.io
import numpy
from argparse import ArgumentParser
from skimage import img_as_ubyte
from skimage.transform import resize
from tqdm import tqdm
import os
import numpy as np
import warnings
warnings.filterwarnings("ignore")
def extract_bbox(frame, fa):
if max(frame.shape[0], frame.shape[1]) > 640:
scale_factor = max(frame.shape[0], frame.shape[1]) / 640.0
frame = resize(frame, (int(frame.shape[0] / scale_factor), int(frame.shape[1] / scale_factor)))
frame = img_as_ubyte(frame)
else:
scale_factor = 1
frame = frame[..., :3]
bboxes = fa.face_detector.detect_from_image(frame[..., ::-1])
if len(bboxes) == 0:
return []
return np.array(bboxes)[:, :-1] * scale_factor
def bb_intersection_over_union(boxA, boxB):
xA = max(boxA[0], boxB[0])
yA = max(boxA[1], boxB[1])
xB = min(boxA[2], boxB[2])
yB = min(boxA[3], boxB[3])
interArea = max(0, xB - xA + 1) * max(0, yB - yA + 1)
boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1)
boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1)
iou = interArea / float(boxAArea + boxBArea - interArea)
return iou
def join(tube_bbox, bbox):
xA = min(tube_bbox[0], bbox[0])
yA = min(tube_bbox[1], bbox[1])
xB = max(tube_bbox[2], bbox[2])
yB = max(tube_bbox[3], bbox[3])
return (xA, yA, xB, yB)
def compute_bbox(tube_bbox, frame_shape, inp, image_shape, increase_area=0.1):
left, top, right, bot = tube_bbox
width = right - left
height = bot - top
width_increase = max(increase_area, ((1 + 2 * increase_area) * height - width) / (2 * width))
height_increase = max(increase_area, ((1 + 2 * increase_area) * width - height) / (2 * height))
left = int(left - width_increase * width)
top = int(top - height_increase * height)
right = int(right + width_increase * width)
bot = int(bot + height_increase * height)
top, bot, left, right = max(0, top), min(bot, frame_shape[0]), max(0, left), min(right, frame_shape[1])
h, w = bot - top, right - left
scale = f'{image_shape[0]}:{image_shape[1]}'
return f'ffmpeg -i {inp} -filter:v "crop={w}:{h}:{left}:{top}, scale={scale}" crop.png'
def process_image(args):
device = 'cpu' if args.cpu else 'cuda'
fa = face_alignment.FaceAlignment(face_alignment.LandmarksType.TWO_D, flip_input=False, device=device)
frame = skimage.io.imread(args.inp)
frame_shape = frame.shape
bboxes = extract_bbox(frame, fa)
if len(bboxes) == 0:
print("No faces detected.")
return []
tube_bbox = bboxes[0]
command = compute_bbox(tube_bbox, frame_shape, inp=args.inp, image_shape=args.image_shape, increase_area=args.increase)
return [command]
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("--image_shape", default=(256, 256), type=lambda x: tuple(map(int, x.split(','))),
help="Image shape")
parser.add_argument("--increase", default=0.1, type=float, help='Increase bbox by this amount')
parser.add_argument("--iou_with_initial", type=float, default=0.25, help="The minimal allowed iou with inital bbox")
parser.add_argument("--inp", required=True, help='Input image')
parser.add_argument("--cpu", dest="cpu", action="store_true", help="cpu mode.")
args = parser.parse_args()
commands = process_image(args)
for command in commands:
print(command)
2. 在视频中裁剪人脸区域
import face_alignment
import skimage.io
import numpy
from argparse import ArgumentParser
from skimage import img_as_ubyte
from skimage.transform import resize
from tqdm import tqdm
import os
import imageio
import numpy as np
import warnings
warnings.filterwarnings("ignore")
def extract_bbox(frame, fa):
if max(frame.shape[0], frame.shape[1]) > 640:
scale_factor = max(frame.shape[0], frame.shape[1]) / 640.0
frame = resize(frame, (int(frame.shape[0] / scale_factor), int(frame.shape[1] / scale_factor)))
frame = img_as_ubyte(frame)
else:
scale_factor = 1
frame = frame[..., :3]
bboxes = fa.face_detector.detect_from_image(frame[..., ::-1])
if len(bboxes) == 0:
return []
return np.array(bboxes)[:, :-1] * scale_factor
def bb_intersection_over_union(boxA, boxB):
xA = max(boxA[0], boxB[0])
yA = max(boxA[1], boxB[1])
xB = min(boxA[2], boxB[2])
yB = min(boxA[3], boxB[3])
interArea = max(0, xB - xA + 1) * max(0, yB - yA + 1)
boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1)
boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1)
iou = interArea / float(boxAArea + boxBArea - interArea)
return iou
def join(tube_bbox, bbox):
xA = min(tube_bbox[0], bbox[0])
yA = min(tube_bbox[1], bbox[1])
xB = max(tube_bbox[2], bbox[2])
yB = max(tube_bbox[3], bbox[3])
return (xA, yA, xB, yB)
def compute_bbox(start, end, fps, tube_bbox, frame_shape, inp, image_shape, increase_area=0.1):
left, top, right, bot = tube_bbox
width = right - left
height = bot - top
width_increase = max(increase_area, ((1 + 2 * increase_area) * height - width) / (2 * width))
height_increase = max(increase_area, ((1 + 2 * increase_area) * width - height) / (2 * height))
left = int(left - width_increase * width)
top = int(top - height_increase * height)
right = int(right + width_increase * width)
bot = int(bot + height_increase * height)
top, bot, left, right = max(0, top), min(bot, frame_shape[0]), max(0, left), min(right, frame_shape[1])
h, w = bot - top, right - left
start = start / fps
end = end / fps
time = end - start
scale = f'{image_shape[0]}:{image_shape[1]}'
return f'ffmpeg -i {inp} -ss {start} -t {time} -filter:v "crop={w}:{h}:{left}:{top}, scale={scale}" crop.mp4'
def compute_bbox_trajectories(trajectories, fps, frame_shape, args):
commands = []
for i, (bbox, tube_bbox, start, end) in enumerate(trajectories):
if (end - start) > args.min_frames:
command = compute_bbox(start, end, fps, tube_bbox, frame_shape, inp=args.inp, image_shape=args.image_shape, increase_area=args.increase)
commands.append(command)
return commands
def process_video(args):
device = 'cpu' if args.cpu else 'cuda'
fa = face_alignment.FaceAlignment(face_alignment.LandmarksType.TWO_D, flip_input=False, device=device)
video = imageio.get_reader(args.inp)
trajectories = []
previous_frame = None
fps = video.get_meta_data()['fps']
commands = []
try:
for i, frame in tqdm(enumerate(video)):
frame_shape = frame.shape
bboxes = extract_bbox(frame, fa)
not_valid_trajectories = []
valid_trajectories = []
for trajectory in trajectories:
tube_bbox = trajectory[0]
intersection = 0
for bbox in bboxes:
intersection = max(intersection, bb_intersection_over_union(tube_bbox, bbox))
if intersection > args.iou_with_initial:
valid_trajectories.append(trajectory)
else:
not_valid_trajectories.append(trajectory)
commands += compute_bbox_trajectories(not_valid_trajectories, fps, frame_shape, args)
trajectories = valid_trajectories
for bbox in bboxes:
intersection = 0
current_trajectory = None
for trajectory in trajectories:
tube_bbox = trajectory[0]
current_intersection = bb_intersection_over_union(tube_bbox, bbox)
if intersection < current_intersection and current_intersection > args.iou_with_initial:
intersection = bb_intersection_over_union(tube_bbox, bbox)
current_trajectory = trajectory
if current_trajectory is None:
trajectories.append([bbox, bbox, i, i])
else:
current_trajectory[3] = i
current_trajectory[1] = join(current_trajectory[1], bbox)
except IndexError as e:
raise (e)
commands += compute_bbox_trajectories(trajectories, fps, frame_shape, args)
return commands
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("--image_shape", default=(256, 256), type=lambda x: tuple(map(int, x.split(','))),
help="Image shape")
parser.add_argument("--increase", default=0.1, type=float, help='Increase bbox by this amount')
parser.add_argument("--iou_with_initial", type=float, default=0.25, help="The minimal allowed iou with inital bbox")
parser.add_argument("--inp", required=True, help='Input image or video')
parser.add_argument("--min_frames", type=int, default=150, help='Minimum number of frames')
parser.add_argument("--cpu", dest="cpu", action="store_true", help="cpu mode.")
args = parser.parse_args()
commands = process_video(args)
for command in commands:
print (command)
