staged simplify
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yuyuyzl committed
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@@ -25,6 +25,7 @@ parser.add_argument('--ifm', type=str)
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parser.add_argument('--anime4k', action='store_true')
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parser.add_argument('--alpha_split', action='store_true')
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parser.add_argument('--cache', type=str, default='100mb')
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parser.add_argument('--simplify', type=int, default=1)
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args = parser.parse_args()
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args.output_w = int(args.output_size.split('x')[0])
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args.output_h = int(args.output_size.split('x')[1])
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@@ -56,6 +56,7 @@ def create_default_blender_data():
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ifm_converter = tha2.poser.modes.mode_20_wx.IFacialMocapPoseConverter20()
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class IFMClientProcess(Process):
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def __init__(self):
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super().__init__()
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@@ -138,11 +139,11 @@ class ModelClientProcess(Process):
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def __init__(self, input_image):
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super().__init__()
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self.should_terminate = Value('b', False)
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self.updated=Value('b', False)
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self.updated = Value('b', False)
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self.data = None
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self.input_image = input_image
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self.output_queue=Queue()
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self.input_queue=Queue()
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self.output_queue = Queue()
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self.input_queue = Queue()
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def run(self):
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model = None
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@@ -164,32 +165,88 @@ class ModelClientProcess(Process):
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hit = 0
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while True:
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input = None
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model_input = None
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try:
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while not self.input_queue.empty():
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input = self.input_queue.get_nowait()
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model_input = self.input_queue.get_nowait()
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except queue.Empty:
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continue
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if input is None: continue
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input=[round(x*100)/100 for x in input]
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input[ifm_converter.iris_rotation_y_index - 12]=round(input[ifm_converter.iris_rotation_y_index - 12]*100)/100
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input[ifm_converter.iris_rotation_x_index - 12]=round(input[ifm_converter.iris_rotation_x_index - 12]*100)/100
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input_hash=hash(tuple(input))
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cached=model_cache.get(input_hash)
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tot+=1
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if model_input is None: continue
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simplify_arr = [1000] * ifm_converter.pose_size
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if args.simplify >= 1:
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simplify_arr = [200] * ifm_converter.pose_size
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simplify_arr[ifm_converter.eye_wink_left_index] = 50
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simplify_arr[ifm_converter.eye_wink_right_index] = 50
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simplify_arr[ifm_converter.eye_happy_wink_left_index] = 50
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simplify_arr[ifm_converter.eye_happy_wink_right_index] = 50
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simplify_arr[ifm_converter.eye_surprised_left_index] = 30
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simplify_arr[ifm_converter.eye_surprised_right_index] = 30
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simplify_arr[ifm_converter.iris_rotation_x_index] = 25
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simplify_arr[ifm_converter.iris_rotation_y_index] = 25
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simplify_arr[ifm_converter.eye_raised_lower_eyelid_left_index] = 10
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simplify_arr[ifm_converter.eye_raised_lower_eyelid_right_index] = 10
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simplify_arr[ifm_converter.mouth_lowered_corner_left_index] = 5
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simplify_arr[ifm_converter.mouth_lowered_corner_right_index] = 5
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simplify_arr[ifm_converter.mouth_raised_corner_left_index] = 5
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simplify_arr[ifm_converter.mouth_raised_corner_right_index] = 5
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if args.simplify >= 2:
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simplify_arr[ifm_converter.head_x_index]=100
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simplify_arr[ifm_converter.head_y_index]=100
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simplify_arr[ifm_converter.eye_surprised_left_index] = 10
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simplify_arr[ifm_converter.eye_surprised_right_index] = 10
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model_input[ifm_converter.eye_wink_left_index - 12] += model_input[
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ifm_converter.eye_happy_wink_left_index - 12]
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model_input[ifm_converter.eye_happy_wink_left_index - 12] = model_input[
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ifm_converter.eye_wink_left_index - 12] / 2
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model_input[ifm_converter.eye_wink_left_index - 12] = model_input[
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ifm_converter.eye_wink_left_index - 12] / 2
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model_input[ifm_converter.eye_wink_right_index - 12] += model_input[
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ifm_converter.eye_happy_wink_right_index - 12]
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model_input[ifm_converter.eye_happy_wink_right_index - 12] = model_input[
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ifm_converter.eye_wink_right_index - 12] / 2
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model_input[ifm_converter.eye_wink_right_index - 12] = model_input[
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ifm_converter.eye_wink_right_index - 12] / 2
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uosum = model_input[ifm_converter.mouth_uuu_index - 12] + \
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model_input[ifm_converter.mouth_ooo_index - 12]
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model_input[ifm_converter.mouth_ooo_index - 12] = uosum
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model_input[ifm_converter.mouth_uuu_index - 12] = 0
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is_open = (model_input[ifm_converter.mouth_aaa_index - 12] + model_input[
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ifm_converter.mouth_iii_index - 12] + uosum) > 0
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model_input[ifm_converter.mouth_lowered_corner_left_index - 12] = 0
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model_input[ifm_converter.mouth_lowered_corner_right_index - 12] = 0
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model_input[ifm_converter.mouth_raised_corner_left_index - 12] = 0.5 if is_open else 0
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model_input[ifm_converter.mouth_raised_corner_right_index - 12] = 0.5 if is_open else 0
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simplify_arr[ifm_converter.mouth_lowered_corner_left_index] = 0
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simplify_arr[ifm_converter.mouth_lowered_corner_right_index] = 0
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simplify_arr[ifm_converter.mouth_raised_corner_left_index] = 0
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simplify_arr[ifm_converter.mouth_raised_corner_right_index] = 0
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if args.simplify >= 3:
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simplify_arr[ifm_converter.iris_rotation_x_index] = 20
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simplify_arr[ifm_converter.iris_rotation_y_index] = 20
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simplify_arr[ifm_converter.eye_wink_left_index] = 25
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simplify_arr[ifm_converter.eye_wink_right_index] = 25
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simplify_arr[ifm_converter.eye_happy_wink_left_index] = 25
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simplify_arr[ifm_converter.eye_happy_wink_right_index] = 25
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for i in range(12, len(simplify_arr)):
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if simplify_arr[i] > 0:
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model_input[i - 12] = round(model_input[i - 12] * simplify_arr[i]) / simplify_arr[i]
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input_hash = hash(tuple(model_input))
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cached = model_cache.get(input_hash)
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tot += 1
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if not cached is None:
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self.output_queue.put_nowait(cached)
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model_cache.move_to_end(input_hash)
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hit+=1
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if (args.perf)or hit%100==0:
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print('cached',str(hit/tot*100)+'%')
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hit += 1
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if (args.perf) or hit % 10 == 0:
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print('cached', str(hit / tot * 100) + '%')
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else:
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if args.perf:
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tic = time.perf_counter()
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for i in range(27):
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mouth_eye_vector[0, i] = input[i]
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mouth_eye_vector[0, i] = model_input[i]
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for i in range(3):
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pose_vector[0, i] = input[i+27]
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pose_vector[0, i] = model_input[i + 27]
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if model is None:
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output_image = input_image
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else:
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@@ -208,9 +265,9 @@ class ModelClientProcess(Process):
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tic = time.perf_counter()
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self.output_queue.put_nowait(postprocessed_image)
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if args.max_cache_len>0:
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model_cache[input_hash]=postprocessed_image
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if len(model_cache)>args.max_cache_len:
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if args.max_cache_len > 0:
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model_cache[input_hash] = postprocessed_image
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if len(model_cache) > args.max_cache_len:
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model_cache.popitem(0)
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@@ -252,12 +309,13 @@ def main():
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if args.output_webcam:
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cam_scale = 1
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cam_width_scale=1
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cam_width_scale = 1
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if args.anime4k:
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cam_scale = 2
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if args.alpha_split:
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cam_width_scale=2
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cam = pyvirtualcam.Camera(width=args.output_w * cam_scale * cam_width_scale, height=args.output_h * cam_scale, fps=60,
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cam_width_scale = 2
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cam = pyvirtualcam.Camera(width=args.output_w * cam_scale * cam_width_scale, height=args.output_h * cam_scale,
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fps=60,
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backend=args.output_webcam,
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fmt=
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{'unitycapture': pyvirtualcam.PixelFormat.RGBA, 'obs': pyvirtualcam.PixelFormat.RGB}[
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@@ -289,8 +347,8 @@ def main():
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blender_data = create_default_blender_data()
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tic1 = 0
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model_output=None
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model_process=ModelClientProcess(input_image)
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model_output = None
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model_process = ModelClientProcess(input_image)
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model_process.daemon = True
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model_process.start()
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@@ -411,17 +469,16 @@ def main():
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pose_vector_c[1] = y_angle * 2.0 # temp weight
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pose_vector_c[2] = (z_angle + 1.5) * 2 # temp weight
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model_input_arr = mouth_eye_vector_c
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model_input_arr.extend(pose_vector_c)
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model_process.input_queue.put_nowait(model_input_arr)
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has_model_output=0
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has_model_output = 0
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try:
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new_model_output = model_output
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while not model_process.output_queue.empty():
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has_model_output+=1
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has_model_output += 1
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new_model_output = model_process.output_queue.get_nowait()
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model_output = new_model_output
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except queue.Empty:
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@@ -434,7 +491,7 @@ def main():
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# if not should_output:
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# continue
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postprocessed_image=model_output
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postprocessed_image = model_output
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if args.perf:
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print('===')
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@@ -483,9 +540,10 @@ def main():
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print("anime4k", (time.perf_counter() - tic) * 1000)
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tic = time.perf_counter()
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if args.alpha_split:
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alpha_image=cv2.merge([postprocessed_image[:, :, 3],postprocessed_image[:, :, 3],postprocessed_image[:, :, 3]])
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alpha_image=cv2.cvtColor(alpha_image, cv2.COLOR_RGB2RGBA)
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postprocessed_image=cv2.hconcat([postprocessed_image,alpha_image])
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alpha_image = cv2.merge(
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[postprocessed_image[:, :, 3], postprocessed_image[:, :, 3], postprocessed_image[:, :, 3]])
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alpha_image = cv2.cvtColor(alpha_image, cv2.COLOR_RGB2RGBA)
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postprocessed_image = cv2.hconcat([postprocessed_image, alpha_image])
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if args.debug:
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output_frame = cv2.cvtColor(postprocessed_image, cv2.COLOR_RGBA2BGRA)
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