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import os
import cv2
import time
import numpy as np
import numexpr as ne
from multiprocessing.dummy import Process, Queue
from options.hifi_test_options import HifiTestOptions
from HifiFaceAPI_parallel_base import Consumer0Base, Consumer2Base, Consumer1BaseONNX
def np_norm(x):
return (x - np.average(x)) / np.std(x)
def reverse2wholeimage_hifi_trt_roi(swaped_img, mat_rev, img_mask, frame, roi_img, roi_box):
target_image = cv2.warpAffine(swaped_img, mat_rev, roi_img.shape[:2][::-1], borderMode=cv2.BORDER_REPLICATE)[
...,
::-1]
local_dict = {
'img_mask': img_mask,
'target_image': target_image,
'roi_img': roi_img,
}
img = ne.evaluate('img_mask * (target_image * 255)+(1 - img_mask) * roi_img', local_dict=local_dict,
global_dict=None)
img = img.astype(np.uint8)
frame[roi_box[1]:roi_box[3], roi_box[0]:roi_box[2]] = img
return frame
def get_max_face(np_rois):
roi_areas = []
for index in range(np_rois.shape[0]):
roi_areas.append((np_rois[index, 2] - np_rois[index, 0]) * (np_rois[index, 3] - np_rois[index, 1]))
return np.argmax(np.array(roi_areas))
class Consumer0(Consumer0Base):
def __init__(self, opt, frame_queue_in, queue_list: list, block=True, fps_counter=False):
super().__init__(opt, frame_queue_in, None, queue_list, block, fps_counter)
def run(self):
counter = 0
start_time = time.time()
kpss_old = None
rois_old = faces_old = Ms_old = masks_old = None
while True:
frame = self.frame_queue_in.get()
if frame is None:
break
try:
_, bboxes, kpss = self.scrfd_detector.get_bboxes(frame, max_num=0)
rois, faces, Ms, masks = self.face_alignment.forward(
frame, bboxes, kpss, limit=5, min_face_size=30,
crop_size=(self.crop_size, self.crop_size), apply_roi=True
)
except (TypeError, IndexError, ValueError) as e:
self.queue_list[0].put([None, frame])
continue
if len(faces)==0:
self.queue_list[0].put([None, frame])
continue
elif len(faces)==1:
face = np.array(faces[0])
mat = Ms[0]
roi_box = rois[0]
else:
max_index = get_max_face(np.array(rois))
face = np.array(faces[max_index])
mat = Ms[max_index]
roi_box = rois[max_index]
roi_img = frame[roi_box[1]:roi_box[3], roi_box[0]:roi_box[2]]
# "The default normalization to the range of -1 to 1, where the model input is in RGB format
face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
self.queue_list[0].put([face, mat, [], frame, roi_img, roi_box])
if self.fps_counter:
counter += 1
if (time.time() - start_time) > 10:
print("Consumer0 FPS: {}".format(counter / (time.time() - start_time)))
counter = 0
start_time = time.time()
self.queue_list[0].put(None)
print('co stop')
class Consumer1(Consumer1BaseONNX):
def __init__(self, opt, feature_list, queue_list: list, block=True, fps_counter=False):
super().__init__(opt, feature_list, queue_list, block, fps_counter)
def run(self):
counter = 0
start_time = time.time()
while True:
something_in = self.queue_list[0].get()
if something_in is None:
break
elif len(something_in) == 2:
self.queue_list[1].put([None, something_in[1]])
continue
if len(self.feature_list) > 1:
self.feature_list.pop(0)
image_latent = self.feature_list[0][0]
mask_out, swap_face_out = self.predict(something_in[0], image_latent[0].reshape(1, -1))
mask = cv2.warpAffine(mask_out[0][0].astype(np.float32), something_in[1],
something_in[4].shape[:2][::-1])
mask[mask > 0.2] = 1
mask = mask[:, :, np.newaxis].astype(np.uint8)
swap_face = swap_face_out[0].transpose((1, 2, 0)).astype(np.float32)
self.queue_list[1].put(
[swap_face, something_in[1], mask, something_in[3], something_in[4], something_in[5]])
if self.fps_counter:
counter += 1
if (time.time() - start_time) > 10:
print("Consumer1 FPS: {}".format(counter / (time.time() - start_time)))
counter = 0
start_time = time.time()
self.queue_list[1].put(None)
print('c1 stop')
class Consumer2(Consumer2Base):
def __init__(self, queue_list: list, frame_queue_out, block=True, fps_counter=False):
super().__init__(queue_list, frame_queue_out, block, fps_counter)
self.face_detect_flag = True
def forward_func(self, something_in):
# do your work here.
if len(something_in) == 2:
self.face_detect_flag = False
frame = something_in[1]
frame_out = frame.astype(np.uint8)
else:
self.face_detect_flag = True
# swap_face = something_in[0]
swap_face = ((something_in[0] + 1) / 2)
frame_out = reverse2wholeimage_hifi_trt_roi(
swap_face, something_in[1], something_in[2],
something_in[3], something_in[4], something_in[5]
)
self.frame_queue_out.put([frame_out, self.face_detect_flag])
# cv2.imshow('output', frame_out)
# cv2.waitKey(1)
class HifiFaceRealTime:
def __init__(self, feature_dict_list_, frame_queue_in, frame_queue_out, gpu=True, model_name=''):
self.opt = HifiTestOptions().parse()
if model_name != '':
self.opt.model_name = model_name
self.opt.input_size = 256
self.feature_dict_list = feature_dict_list_
self.frame_queue_in = frame_queue_in
self.frame_queue_out = frame_queue_out
self.gpu = gpu
def forward(self):
self.q0 = Queue(2)
self.q1 = Queue(2)
self.c0 = Consumer0(self.opt, self.frame_queue_in, [self.q0], fps_counter=False)
self.c1 = Consumer1(self.opt, self.feature_dict_list, [self.q0, self.q1], fps_counter=False)
self.c2 = Consumer2([self.q1], self.frame_queue_out, fps_counter=False)
self.c0.start()
self.c1.start()
self.c2.start()
self.c0.join()
self.c1.join()
self.c2.join()
return
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