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https://github.com/iperov/DeepFaceLab.git
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Refactor preview image code
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jh
parent
2bf695552b
commit
f94c77b226
1 changed files with 71 additions and 53 deletions
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@ -181,6 +181,64 @@ def trainerThread (s2c, c2s, e, args, device_args):
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c2s.put ( {'op':'close'} )
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def scale_previews(previews, preview_min_height, preview_max_height):
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preview_height = max((h for h, w, c in (im.shape for name, im in previews)))
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if preview_height > preview_max_height:
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preview_height = preview_max_height
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elif preview_height < preview_min_height:
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preview_height = preview_min_height
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# make all previews size equal
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for preview in previews[:]:
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(preview_name, preview_rgb) = preview
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(h, w, c) = preview_rgb.shape
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if h != preview_height:
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scale_factor = preview_height / float(h)
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previews.remove(preview)
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previews.append((preview_name, cv2.resize(preview_rgb, (0, 0),
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fx=scale_factor,
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fy=scale_factor,
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interpolation=cv2.INTER_AREA)))
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return previews
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def create_preview_pane_image(previews, selected_preview, loss_history,
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show_last_history_iters_count, batch_size):
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selected_preview_name = previews[selected_preview][0]
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selected_preview_rgb = previews[selected_preview][1]
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(h,w,c) = selected_preview_rgb.shape
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# HEAD
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head_lines = [
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'[s]:save [enter]:exit',
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'[p]:update [space]:next preview [l]:change history range',
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'Preview: "%s" [%d/%d]' % (selected_preview_name,selected_preview+1, len(previews) )
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]
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head_line_height = 15
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head_height = len(head_lines) * head_line_height
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head = np.ones ( (head_height,w,c) ) * 0.1
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for i in range(0, len(head_lines)):
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t = i*head_line_height
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b = (i+1)*head_line_height
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head[t:b, 0:w] += imagelib.get_text_image ( (head_line_height,w,c) , head_lines[i], color=[0.8]*c )
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final = head
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if loss_history is not None:
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if show_last_history_iters_count == 0:
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loss_history_to_show = loss_history
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else:
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loss_history_to_show = loss_history[-show_last_history_iters_count:]
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lh_img = models.ModelBase.get_loss_history_preview(loss_history_to_show, iter, batch_size, w, c)
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final = np.concatenate ( [final, lh_img], axis=0 )
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final = np.concatenate ( [final, selected_preview_rgb], axis=0 )
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final = np.clip(final, 0, 1)
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return (final*255).astype(np.uint8)
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def main(args, device_args):
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io.log_info ("Running trainer.\r\n")
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@ -236,24 +294,7 @@ def main(args, device_args):
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iter = input['iter'] if 'iter' in input.keys() else 0
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#batch_size = input['batch_size'] if 'iter' in input.keys() else 1
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if previews is not None:
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preview_height = max((h for h, w, c in (im.shape for name, im in previews)))
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if preview_height > preview_max_height:
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preview_height = preview_max_height
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elif preview_height < preview_min_height:
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preview_height = preview_min_height
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# make all previews size equal
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for preview in previews[:]:
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(preview_name, preview_rgb) = preview
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(h, w, c) = preview_rgb.shape
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if h != preview_height:
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scale_factor = preview_height / float(h)
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previews.remove(preview)
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previews.append((preview_name, cv2.resize(preview_rgb, (0, 0),
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fx=scale_factor,
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fy=scale_factor,
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interpolation=cv2.INTER_AREA)))
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previews = scale_previews(previews, preview_min_height, preview_max_height)
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selected_preview = selected_preview % len(previews)
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update_preview = True
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elif op == 'close':
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@ -261,41 +302,12 @@ def main(args, device_args):
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if update_preview:
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update_preview = False
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selected_preview_name = previews[selected_preview][0]
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selected_preview_rgb = previews[selected_preview][1]
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(h,w,c) = selected_preview_rgb.shape
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# HEAD
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head_lines = [
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'[s]:save [enter]:exit',
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'[p]:update [space]:next preview [l]:change history range',
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'Preview: "%s" [%d/%d]' % (selected_preview_name,selected_preview+1, len(previews) )
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]
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head_line_height = 15
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head_height = len(head_lines) * head_line_height
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head = np.ones ( (head_height,w,c) ) * 0.1
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for i in range(0, len(head_lines)):
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t = i*head_line_height
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b = (i+1)*head_line_height
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head[t:b, 0:w] += imagelib.get_text_image ( (head_line_height,w,c) , head_lines[i], color=[0.8]*c )
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final = head
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if loss_history is not None:
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if show_last_history_iters_count == 0:
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loss_history_to_show = loss_history
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else:
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loss_history_to_show = loss_history[-show_last_history_iters_count:]
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lh_img = models.ModelBase.get_loss_history_preview(loss_history_to_show, iter, batch_size, w, c)
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final = np.concatenate ( [final, lh_img], axis=0 )
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final = np.concatenate ( [final, selected_preview_rgb], axis=0 )
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final = np.clip(final, 0, 1)
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io.show_image( wnd_name, (final*255).astype(np.uint8) )
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preview_pane_image = create_preview_pane_image(previews,
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selected_preview,
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loss_history,
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show_last_history_iters_count,
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batch_size)
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io.show_image( wnd_name, preview_pane_image)
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is_showing = True
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key_events = io.get_key_events(wnd_name)
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@ -324,6 +336,12 @@ def main(args, device_args):
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elif key == ord(' '):
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selected_preview = (selected_preview + 1) % len(previews)
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update_preview = True
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elif key == ord('-'):
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# Decrease zoom
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pass
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elif key == ord('+'):
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# Increase zoom
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pass
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try:
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io.process_messages(0.1)
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