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https://github.com/iperov/DeepFaceLab.git
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Manual extractor: now you can specify face rectangle manually using ‘R Mouse button’.
It is useful for small, blurry, undetectable faces, animal faces. https://i.imgur.com/8kmVgg8.jpg Warning: such frames must be used only with XSeg workflow ! Landmarks cannot be placed on the face precisely, and they are actually used for positioning the red frame. Try to keep the red frame the same as the adjacent frames.
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Colombo
parent
e77865ce18
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f91a604c6d
1 changed files with 95 additions and 56 deletions
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@ -10,6 +10,7 @@ from pathlib import Path
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import cv2
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import numpy as np
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from numpy import linalg as npla
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import facelib
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from core import imagelib
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@ -94,7 +95,7 @@ class ExtractSubprocessor(Subprocessor):
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self.cached_image = ( filepath, image )
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h, w, c = image.shape
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dflimg = DFLIMG.load (filepath)
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extract_from_dflimg = (h == w and (dflimg is not None and dflimg.has_data()) )
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@ -302,16 +303,16 @@ class ExtractSubprocessor(Subprocessor):
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if not cpu_only:
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if type == 'landmarks-manual':
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devices = [devices.get_best_device()]
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result = []
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for device in devices:
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count = 1
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if count == 1:
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result += [ (device.index, 'GPU', device.name, device.total_mem_gb) ]
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else:
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for i in range(count):
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for i in range(count):
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result += [ (device.index, 'GPU', f"{device.name} #{i}", device.total_mem_gb) ]
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return result
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@ -358,6 +359,7 @@ class ExtractSubprocessor(Subprocessor):
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self.cache_text_lines_img = (None, None)
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self.hide_help = False
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self.landmarks_accurate = True
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self.force_landmarks = False
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self.landmarks = None
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self.x = 0
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@ -397,7 +399,6 @@ class ExtractSubprocessor(Subprocessor):
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def get_data(self, host_dict):
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if self.type == 'landmarks-manual':
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need_remark_face = False
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redraw_needed = False
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while len (self.input_data) > 0:
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data = self.input_data[0]
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filepath, data_rects, data_landmarks = data.filepath, data.rects, data.landmarks
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@ -410,11 +411,12 @@ class ExtractSubprocessor(Subprocessor):
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self.landmarks = data_landmarks.pop()
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data_rects.clear()
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data_landmarks.clear()
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redraw_needed = True
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self.rect_locked = True
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self.rect_size = ( self.rect[2] - self.rect[0] ) / 2
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self.x = ( self.rect[0] + self.rect[2] ) / 2
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self.y = ( self.rect[1] + self.rect[3] ) / 2
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self.redraw()
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if len(data_rects) == 0:
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if self.cache_original_image[0] == filepath:
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@ -440,8 +442,8 @@ class ExtractSubprocessor(Subprocessor):
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self.text_lines_img = self.cache_text_lines_img[1]
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else:
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self.text_lines_img = (imagelib.get_draw_text_lines ( self.image, sh,
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[ '[Mouse click] - lock/unlock selection',
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'[Mouse wheel] - change rect',
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[ '[L Mouse click] - lock/unlock selection. [Mouse wheel] - change rect',
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'[R Mouse Click] - manual face rectangle',
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'[Enter] / [Space] - confirm / skip frame',
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'[,] [.]- prev frame, next frame. [Q] - skip remaining frames',
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'[a] - accuracy on/off (more fps)',
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@ -453,8 +455,10 @@ class ExtractSubprocessor(Subprocessor):
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while True:
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io.process_messages(0.0001)
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new_x = self.x
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new_y = self.y
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if not self.force_landmarks:
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new_x = self.x
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new_y = self.y
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new_rect_size = self.rect_size
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mouse_events = io.get_mouse_events(self.wnd_name)
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@ -465,8 +469,19 @@ class ExtractSubprocessor(Subprocessor):
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diff = 1 if new_rect_size <= 40 else np.clip(new_rect_size / 10, 1, 10)
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new_rect_size = max (5, new_rect_size + diff*mod)
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elif ev == io.EVENT_LBUTTONDOWN:
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self.rect_locked = not self.rect_locked
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self.extract_needed = True
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if self.force_landmarks:
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self.x = new_x
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self.y = new_y
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self.force_landmarks = False
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self.rect_locked = True
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self.redraw()
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else:
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self.rect_locked = not self.rect_locked
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self.extract_needed = True
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elif ev == io.EVENT_RBUTTONDOWN:
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self.force_landmarks = not self.force_landmarks
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if self.force_landmarks:
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self.rect_locked = False
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elif not self.rect_locked:
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new_x = np.clip (x, 0, w-1) / self.view_scale
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new_y = np.clip (y, 0, h-1) / self.view_scale
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@ -532,11 +547,35 @@ class ExtractSubprocessor(Subprocessor):
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self.landmarks_accurate = not self.landmarks_accurate
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break
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if self.x != new_x or \
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if self.force_landmarks:
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pt2 = np.float32([new_x, new_y])
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pt1 = np.float32([self.x, self.y])
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pt_vec_len = npla.norm(pt2-pt1)
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pt_vec = pt2-pt1
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if pt_vec_len != 0:
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pt_vec /= pt_vec_len
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self.rect_size = pt_vec_len
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self.rect = ( int(self.x-self.rect_size),
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int(self.y-self.rect_size),
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int(self.x+self.rect_size),
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int(self.y+self.rect_size) )
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if pt_vec_len > 0:
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lmrks = np.concatenate ( (np.zeros ((17,2), np.float32), LandmarksProcessor.landmarks_2D), axis=0 )
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lmrks -= lmrks[30:31,:]
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mat = cv2.getRotationMatrix2D( (0, 0), -np.arctan2( pt_vec[1], pt_vec[0] )*180/math.pi , pt_vec_len)
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mat[:, 2] += (self.x, self.y)
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self.landmarks = LandmarksProcessor.transform_points(lmrks, mat )
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self.redraw()
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elif self.x != new_x or \
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self.y != new_y or \
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self.rect_size != new_rect_size or \
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self.extract_needed or \
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redraw_needed:
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self.extract_needed:
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self.x = new_x
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self.y = new_y
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self.rect_size = new_rect_size
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@ -545,11 +584,7 @@ class ExtractSubprocessor(Subprocessor):
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int(self.x+self.rect_size),
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int(self.y+self.rect_size) )
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if redraw_needed:
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redraw_needed = False
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return ExtractSubprocessor.Data (filepath, landmarks_accurate=self.landmarks_accurate)
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else:
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return ExtractSubprocessor.Data (filepath, rects=[self.rect], landmarks_accurate=self.landmarks_accurate)
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return ExtractSubprocessor.Data (filepath, rects=[self.rect], landmarks_accurate=self.landmarks_accurate)
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else:
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is_frame_done = True
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@ -571,6 +606,40 @@ class ExtractSubprocessor(Subprocessor):
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if not self.type != 'landmarks-manual':
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self.input_data.insert(0, data)
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def redraw(self):
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(h,w,c) = self.image.shape
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if not self.hide_help:
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image = cv2.addWeighted (self.image,1.0,self.text_lines_img,1.0,0)
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else:
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image = self.image.copy()
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view_rect = (np.array(self.rect) * self.view_scale).astype(np.int).tolist()
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view_landmarks = (np.array(self.landmarks) * self.view_scale).astype(np.int).tolist()
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if self.rect_size <= 40:
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scaled_rect_size = h // 3 if w > h else w // 3
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p1 = (self.x - self.rect_size, self.y - self.rect_size)
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p2 = (self.x + self.rect_size, self.y - self.rect_size)
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p3 = (self.x - self.rect_size, self.y + self.rect_size)
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wh = h if h < w else w
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np1 = (w / 2 - wh / 4, h / 2 - wh / 4)
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np2 = (w / 2 + wh / 4, h / 2 - wh / 4)
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np3 = (w / 2 - wh / 4, h / 2 + wh / 4)
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mat = cv2.getAffineTransform( np.float32([p1,p2,p3])*self.view_scale, np.float32([np1,np2,np3]) )
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image = cv2.warpAffine(image, mat,(w,h) )
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view_landmarks = LandmarksProcessor.transform_points (view_landmarks, mat)
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landmarks_color = (255,255,0) if self.rect_locked else (0,255,0)
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LandmarksProcessor.draw_rect_landmarks (image, view_rect, view_landmarks, self.face_type, self.image_size, landmarks_color=landmarks_color)
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self.extract_needed = False
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io.show_image (self.wnd_name, image)
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#override
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def on_result (self, host_dict, data, result):
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if self.type == 'landmarks-manual':
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@ -579,37 +648,7 @@ class ExtractSubprocessor(Subprocessor):
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if len(landmarks) != 0 and landmarks[0] is not None:
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self.landmarks = landmarks[0]
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(h,w,c) = self.image.shape
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if not self.hide_help:
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image = cv2.addWeighted (self.image,1.0,self.text_lines_img,1.0,0)
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else:
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image = self.image.copy()
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view_rect = (np.array(self.rect) * self.view_scale).astype(np.int).tolist()
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view_landmarks = (np.array(self.landmarks) * self.view_scale).astype(np.int).tolist()
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if self.rect_size <= 40:
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scaled_rect_size = h // 3 if w > h else w // 3
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p1 = (self.x - self.rect_size, self.y - self.rect_size)
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p2 = (self.x + self.rect_size, self.y - self.rect_size)
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p3 = (self.x - self.rect_size, self.y + self.rect_size)
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wh = h if h < w else w
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np1 = (w / 2 - wh / 4, h / 2 - wh / 4)
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np2 = (w / 2 + wh / 4, h / 2 - wh / 4)
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np3 = (w / 2 - wh / 4, h / 2 + wh / 4)
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mat = cv2.getAffineTransform( np.float32([p1,p2,p3])*self.view_scale, np.float32([np1,np2,np3]) )
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image = cv2.warpAffine(image, mat,(w,h) )
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view_landmarks = LandmarksProcessor.transform_points (view_landmarks, mat)
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landmarks_color = (255,255,0) if self.rect_locked else (0,255,0)
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LandmarksProcessor.draw_rect_landmarks (image, view_rect, view_landmarks, self.face_type, self.image_size, landmarks_color=landmarks_color)
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self.extract_needed = False
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io.show_image (self.wnd_name, image)
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self.redraw()
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else:
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self.result.append ( result )
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io.progress_bar_inc(1)
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@ -690,14 +729,14 @@ def main(detector=None,
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cpu_only = False,
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force_gpu_idxs = None,
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):
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if not input_path.exists():
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io.log_err ('Input directory not found. Please ensure it exists.')
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return
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if face_type is not None:
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face_type = FaceType.fromString(face_type)
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if face_type is None:
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if manual_output_debug_fix and output_path.exists():
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files = pathex.get_image_paths(output_path)
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@ -705,14 +744,14 @@ def main(detector=None,
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dflimg = DFLIMG.load(Path(files[0]))
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if dflimg is not None and dflimg.has_data():
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face_type = FaceType.fromString ( dflimg.get_face_type() )
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if face_type is None:
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face_type = io.input_str ("Face type", 'wf', ['f','wf','head'], help_message="Full face / whole face / head. 'Whole face' covers full area of face include forehead. 'head' covers full head, but requires XSeg for src and dst faceset.").lower()
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face_type = {'f' : FaceType.FULL,
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'wf' : FaceType.WHOLE_FACE,
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'head' : FaceType.HEAD}[face_type]
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image_size = 512 if face_type < FaceType.HEAD else 768
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image_size = 512 if face_type < FaceType.HEAD else 768
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if detector is None:
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io.log_info ("Choose detector type.")
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