632 lines
25 KiB
Python
632 lines
25 KiB
Python
import math, time, os
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import numpy as np
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from scipy.signal import savgol_filter
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from typing import Callable
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from PIL import Image, ImageDraw
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import numpy as np
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import cv2
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from modules.ui import plaintext_to_html
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import modules.shared as shared
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from modules.paths_internal import script_path
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from .helpers import (
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fix_env_Path_ffprobe,
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closest_upper_divisible_by_eight,
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load_model_from_setting,
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do_upscaleImg,
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)
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from .sd_helpers import renderImg2Img, renderTxt2Img
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from .image import shrink_and_paste_on_blank
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from .video import ContinuousVideoWriter
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from .InfZoomConfig import InfZoomConfig
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class InfZoomer:
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def __init__(self, config: InfZoomConfig) -> None:
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self.C = config
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self.prompts = {}
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self.main_frames = []
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self.out_config = {}
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for x in self.C.prompts_array:
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try:
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key = int(x[0])
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value = str(x[1])
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self.prompts[key] = value
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except ValueError:
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pass
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assert len(self.C.prompts_array) > 0, "prompts is empty"
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fix_env_Path_ffprobe()
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self.out_config = self.prepare_output_path()
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self.current_seed = self.C.seed
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# knowing the mask_height and desired outputsize find a compromise due to align 8 contraint of diffuser
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self.width = closest_upper_divisible_by_eight(self.C.outputsizeW)
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self.height = closest_upper_divisible_by_eight(self.C.outputsizeH)
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if self.width > self.height:
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self.mask_width = self.C.outpaint_amount_px
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self.mask_height = math.trunc(self.C.outpaint_amount_px * self.height/self.width)
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else:
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self.mask_height = self.C.outpaint_amount_px
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self.mask_width = math.trunc(self.C.outpaint_amount_px * self.width/self.height)
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# here we leave slightly the desired ratio since if size+2*mask_size % 8 != 0
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# distribute "aligning pixels" to the mask size equally.
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# only consider mask_size since image size is alread 8-aligned
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self.mask_width -= self.mask_width % 4
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self.mask_height -= self.mask_height % 4
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assert 0 == (2*self.mask_width+self.width) % 8
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assert 0 == (2*self.mask_height+self.height) % 8
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print (f"Adapted sizes for diffusers to: {self.width}x{self.height}+mask:{self.mask_width}x{self.mask_height}. New ratio: {(self.width+self.mask_width)/(self.height+self.mask_height)} ")
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self.num_interpol_frames = round(self.C.video_frame_rate * self.C.zoom_speed) - 1 # keyframe not to be interpolated
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if (self.C.outpaintStrategy == "Corners"):
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self.fnOutpaintMainFrames = self.outpaint_steps_cornerStrategy
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self.fnInterpolateFrames = self.interpolateFramesOuterZoom
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elif (self.C.outpaintStrategy == "Center"):
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self.fnOutpaintMainFrames = self.outpaint_steps_v8hid
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self.fnInterpolateFrames = self.interpolateFramesSmallCenter
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else:
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raise ValueError("Unsupported outpaint strategy in Infinite Zoom")
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self.outerZoom = True # scale from overscan to target viewport
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# object properties, different from user input config
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out_config = {}
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prompts = {}
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main_frames:Image = []
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outerZoom: bool
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mask_width: int
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mask_height: int
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current_seed: int
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contVW: ContinuousVideoWriter
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fnOutpaintMainFrames: Callable
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fnInterpolateFrames: Callable
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def create_zoom(self):
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for i in range(self.C.batchcount):
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print(f"Batch {i+1}/{self.C.batchcount}")
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result = self.create_zoom_single()
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return result
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def create_zoom_single(self):
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self.main_frames.append(self.prepareInitImage())
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load_model_from_setting("infzoom_inpainting_model", self.C.progress, "Loading Model for inpainting/img2img: ")
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processed = self.fnOutpaintMainFrames()
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if (self.C.upscale_do):
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self.doUpscaling()
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if self.C.video_zoom_mode:
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self.main_frames = self.main_frames[::-1]
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if not self.outerZoom:
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self.contVW = ContinuousVideoWriter(
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self.out_config["video_filename"],
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self.main_frames[0],
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self.C.video_frame_rate,
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int(self.C.video_start_frame_dupe_amount),
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self.C.video_ffmpeg_opts
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)
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self.fnInterpolateFrames() # changes main_frame and writes to video
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print("Video saved in: " + os.path.join(script_path, self.out_config["video_filename"]))
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return (
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self.out_config["video_filename"],
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self.main_frames,
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processed.js(),
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plaintext_to_html(processed.info),
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plaintext_to_html(""),
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)
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def doUpscaling(self):
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for idx,mf in enumerate(self.main_frames):
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print (f"\033[KInfZoom: Upscaling mainframe: {idx} \r",end="")
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self.main_frames[idx]=do_upscaleImg(mf, self.C.upscale_do, self.C.upscaler_name, self.C.upscale_by)
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self.mask_width = math.trunc(self.mask_width*self.C.upscale_by)
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self.mask_height = math.trunc(self.mask_height *self.C.upscale_by)
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if self.C.outpaintStrategy == "Corners":
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self.width = self.main_frames[0].width-2*self.mask_width
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self.height = self.main_frames[0].height-2*self.mask_height
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else:
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self.width = self.main_frames[0].width
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self.height = self.main_frames[0].height
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def prepareInitImage(self) -> Image:
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if self.C.custom_init_image:
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current_image = Image.new(mode="RGBA", size=(self.width, self.height))
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current_image = current_image.convert("RGB")
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current_image = cv2_to_pil(cv2.resize(
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pil_to_cv2(self.C.custom_init_image),
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(self.width, self.height),
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interpolation=cv2.INTER_AREA
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)
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)
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self.save2Collect(current_image, f"init_custom.png")
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else:
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load_model_from_setting("infzoom_txt2img_model", self.C.progress, "Loading Model for txt2img: ")
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processed, newseed = self.renderFirstFrame()
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if len(processed.images) > 0:
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current_image = processed.images[0]
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self.save2Collect(current_image, f"init_txt2img.png")
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self.current_seed = newseed
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return current_image
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def renderFirstFrame(self):
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pr = self.getInitialPrompt()
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return renderTxt2Img(
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f"{self.C.common_prompt_pre}\n{pr}\n{self.C.common_prompt_suf}".strip(),
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self.C.negative_prompt,
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self.C.sampler,
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self.C.num_inference_steps,
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self.C.guidance_scale,
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self.current_seed,
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self.width,
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self.height
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)
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def getInitialPrompt(self):
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return self.prompts[min(k for k in self.prompts.keys() if k >= 0)]
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def outpaint_steps_cornerStrategy(self):
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currentImage = self.main_frames[-1]
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# just 30 radius to get inpaint connected between outer and innter motive
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masked_image = create_mask_with_circles(
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currentImage,
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self.mask_width, self.mask_height,
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overmask=self.C.overmask,
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radius=min(self.mask_height,self.mask_height)*0.2
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)
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new_width= masked_image.width
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new_height=masked_image.height
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outpaint_steps=self.C.num_outpainting_steps
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for i in range(outpaint_steps):
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print (f"Outpaint step: {str(i + 1)}/{str(outpaint_steps)} Seed: {str(self.current_seed)}")
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currentImage = self.main_frames[-1]
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if self.C.custom_exit_image and ((i + 1) == outpaint_steps):
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currentImage = cv2_to_pil(cv2.resize(
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pil_to_cv2(self.C.custom_exit_image),
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(self.C.width, self.C.height),
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interpolation=cv2.INTER_AREA
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)
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)
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if 0 == self.outerZoom:
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self.main_frames.append(currentImage.convert("RGB"))
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self.save2Collect(currentImage, self.out_config, f"exit_img.png")
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else:
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expanded_image = cv2_to_pil(
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cv2.resize(pil_to_cv2(currentImage),
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(new_width,new_height),
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interpolation=cv2.INTER_AREA
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)
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)
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#expanded_image = Image.new("RGB",(new_width,new_height),"black")
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expanded_image.paste(currentImage, (self.mask_width,self.mask_height))
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pr = self.prompts[max(k for k in self.prompts.keys() if k <= i)]
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processed, newseed = renderImg2Img(
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f"{self.C.common_prompt_pre}\n{pr}\n{self.C.common_prompt_suf}".strip(),
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self.C.negative_prompt,
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self.C.sampler,
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self.C.num_inference_steps,
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self.C.guidance_scale,
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-1, # try to avoid massive repeatings: self.current_seed,
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new_width, #outpaintsizeW
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new_height, #outpaintsizeH
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expanded_image,
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masked_image,
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self.C.inpainting_denoising_strength,
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self.C.inpainting_mask_blur,
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self.C.inpainting_fill_mode,
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False, # self.C.inpainting_full_res,
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0 #self.C.inpainting_padding,
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)
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#
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if len(processed.images) > 0:
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expanded_image = processed.images[0]
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zoomed_img = cv2_to_pil(cv2.resize(
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pil_to_cv2(expanded_image),
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(self.width,self.height),
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interpolation=cv2.INTER_AREA
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)
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)
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if self.outerZoom:
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self.main_frames[-1] = expanded_image # replace small image
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self.save2Collect(processed.images[0], f"outpaint_step_{i}.png")
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if (i < outpaint_steps-1):
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self.main_frames.append(zoomed_img) # prepare next frame with former content
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else:
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zoomed_img = cv2_to_pil(cv2.resize(
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expanded_image,
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(self.width,self.height),
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interpolation=cv2.INTER_AREA
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)
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)
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self.main_frames.append(zoomed_img)
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processed.images[0]=self.main_frames[-1]
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self.save2Collect(processed.images[0], f"outpaint_step_{i}.png")
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return processed
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def outpaint_steps_v8hid(self):
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for i in range(self.C.num_outpainting_steps):
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print (f"Outpaint step: {str(i + 1)} / {str(self.C.num_outpainting_steps)} Seed: {str(self.current_seed)}")
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current_image = self.main_frames[-1]
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current_image = shrink_and_paste_on_blank(
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current_image, self.mask_width, self.mask_height
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)
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mask_image = np.array(current_image)[:, :, 3]
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mask_image = Image.fromarray(255 - mask_image).convert("RGB")
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if self.C.custom_exit_image and ((i + 1) == self.C.num_outpainting_steps):
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current_image = cv2_to_pil(
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cv2.resize( pil_to_cv2(
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self.C.custom_exit_image),
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(self.width, self.height),
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interpolation=cv2.INTER_AREA)
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)
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self.main_frames.append(current_image.convert("RGB"))
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# print("using Custom Exit Image")
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self.save2Collect(current_image, f"exit_img.png")
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else:
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pr = self.prompts[max(k for k in self.prompts.keys() if k <= i)]
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processed, newseed = renderImg2Img(
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f"{self.C.common_prompt_pre}\n{pr}\n{self.C.common_prompt_suf}".strip(),
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self.C.negative_prompt,
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self.C.sampler,
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self.C.num_inference_steps,
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self.C.guidance_scale,
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self.current_seed,
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self.width,
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self.height,
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current_image,
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mask_image,
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self.C.inpainting_denoising_strength,
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self.C.inpainting_mask_blur,
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self.C.inpainting_fill_mode,
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self.C.inpainting_full_res,
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self.C.inpainting_padding,
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)
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if len(processed.images) > 0:
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self.main_frames.append(processed.images[0].convert("RGB"))
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self.save2Collect(processed.images[0], f"outpain_step_{i}.png")
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seed = newseed
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# TODO: seed behavior
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return processed
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def calculate_interpolation_steps_linear(self, original_size, target_size, steps):
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width, height = original_size
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target_width, target_height = target_size
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if width <= 0 or height <= 0 or target_width <= 0 or target_height <= 0 or steps <= 0:
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return []
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width_step = (width - target_width) / (steps+1) #+1 enforce steps BETWEEN keyframe, dont reach the target size. interval like []
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height_step = (height - target_height) / (steps+1)
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scaling_steps = [(round(width - i * width_step), round(height - i * height_step)) for i in range(1,steps+1)]
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#scaling_steps.insert(0,original_size) # initial size is in the list
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return scaling_steps
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def interpolateFramesOuterZoom(self):
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if 0 == self.C.video_zoom_mode:
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current_image = self.main_frames[0]
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elif 1 == self.C.video_zoom_mode:
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current_image = self.main_frames[-1]
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else:
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raise ValueError("unsupported Zoom mode in INfZoom")
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outzoomSize = (self.width+self.mask_width*2, self.height+self.mask_height*2)
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target_size = (self.width, self.height) # mask border, hide blipping
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scaling_steps = self.calculate_interpolation_steps_linear(outzoomSize, target_size, self.num_interpol_frames)
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print(f"Before: {scaling_steps}, length: {len(scaling_steps)}")
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# all sizes EVEN
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for i,s in enumerate(scaling_steps):
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scaling_steps[i] = (s[0]+s[0]%2, s[1]+s[1]%2)
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# ODD steps producing jumps. even steps on even resolution is what we need.
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#scaling_steps[i] = (s[0]+1, s[1]+1)
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print(f"After EVEN: {scaling_steps}, length: {len(scaling_steps)}")
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def calculate_differences(lst):
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# Es wird eine leere Liste initialisiert, in der die Differenzen gespeichert werden
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diff_lst = []
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# Durchlaufen der Liste
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for i in range(1, len(lst)):
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# Differenz zwischen aufeinanderfolgenden Tupeln berechnen
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diff = (lst[i][0]-lst[i-1][0], lst[i][1]-lst[i-1][1])
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# Die Differenz zum diff_lst hinzufügen
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diff_lst.append(diff)
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return diff_lst
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# Beispielliste von Tupeln
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print(calculate_differences(scaling_steps))
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print ("Ratios:")
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for s in scaling_steps:
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print(f"{str(s[0]/s[1])}",end=";")
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self.contVW = ContinuousVideoWriter(self.out_config["video_filename"],
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self.cropCenterTo(current_image,(target_size)),
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self.C.video_frame_rate,int(self.C.video_start_frame_dupe_amount-1),
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self.C.video_ffmpeg_opts)
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for i in range(len(self.main_frames)):
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if 0 == self.C.video_zoom_mode:
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current_image = self.main_frames[0+i]
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else:
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current_image = self.main_frames[-1-i]
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lastFrame = self.cropCenterTo(current_image,target_size)
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self.contVW.append([lastFrame])
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cv2_image = pil_to_cv2(current_image)
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# Resize and crop using OpenCV2
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for j in range(self.num_interpol_frames):
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print(f"\033[KInfZoom: Interpolate frame(CV2): main/inter: {i}/{j} \r", end="")
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resized_image = cv2.resize(
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cv2_image,
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(scaling_steps[j][0], scaling_steps[j][1]),
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interpolation=cv2.INTER_AREA
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)
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cropped_image_cv2 = cv2_crop_center(resized_image, target_size)
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cropped_image_pil = cv2_to_pil(cropped_image_cv2)
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self.contVW.append([cropped_image_pil])
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lastFrame = cropped_image_pil
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self.contVW.finish(lastFrame, int(self.C.video_last_frame_dupe_amount))
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""" USING PIL:
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for i in range(len(self.main_frames)):
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if 0 == self.C.video_zoom_mode:
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current_image = self.main_frames[0+i]
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else:
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current_image = self.main_frames[-1-i]
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self.contVW.append([
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self.cropCenterTo(current_image,(self.width, self.height))
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])
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# interpolation steps between 2 inpainted images (=sequential zoom and crop)
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for j in range(self.num_interpol_frames - 1):
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print (f"\033[KInfZoom: Interpolate frame: main/inter: {i}/{j} \r",end="")
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#todo: howto zoomIn when writing each frame; self.main_frames are inverted, howto interpolate?
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scaled_image = current_image.resize(scaling_steps[j], Image.LANCZOS)
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cropped_image = self.cropCenterTo(scaled_image,(self.width, self.height))
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self.contVW.append([cropped_image])
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"""
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def interpolateFramesSmallCenter(self):
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if self.C.video_zoom_mode:
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firstImage = self.main_frames[0]
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else:
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firstImage = self.main_frames[-1]
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self.contVW = ContinuousVideoWriter(self.out_config["video_filename"],
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(firstImage,(self.width,self.height)),
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self.C.video_frame_rate,int(self.C.video_start_frame_dupe_amount),
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self.C.video_ffmpeg_opts)
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for i in range(len(self.main_frames) - 1):
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# interpolation steps between 2 inpainted images (=sequential zoom and crop)
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for j in range(self.num_interpol_frames - 1):
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|
|
|
print (f"\033[KInfZoom: Interpolate frame: main/inter: {i}/{j} \r",end="")
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#todo: howto zoomIn when writing each frame; self.main_frames are inverted, howto interpolate?
|
|
if self.C.video_zoom_mode:
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|
current_image = self.main_frames[i + 1]
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|
else:
|
|
current_image = self.main_frames[i + 1]
|
|
|
|
|
|
interpol_image = current_image
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|
self.save2Collect(interpol_image, f"interpol_img_{i}_{j}].png")
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|
|
|
interpol_width = math.ceil(
|
|
( 1 - (1 - 2 * self.mask_width / self.width) **(1 - (j + 1) / self.num_interpol_frames) )
|
|
* self.width / 2
|
|
)
|
|
|
|
interpol_height = math.ceil(
|
|
( 1 - (1 - 2 * self.mask_height / self.height) ** (1 - (j + 1) / self.num_interpol_frames) )
|
|
* self.height/2
|
|
)
|
|
|
|
interpol_image = interpol_image.crop(
|
|
(
|
|
interpol_width,
|
|
interpol_height,
|
|
self.width - interpol_width,
|
|
self.height - interpol_height,
|
|
)
|
|
)
|
|
|
|
interpol_image = interpol_image.resize((self.width, self.height))
|
|
self.save2Collect(interpol_image, f"interpol_resize_{i}_{j}.png")
|
|
|
|
# paste the higher resolution previous image in the middle to avoid drop in quality caused by zooming
|
|
interpol_width2 = math.ceil(
|
|
(1 - (self.width - 2 * self.mask_width) / (self.width - 2 * interpol_width))
|
|
/ 2 * self.width
|
|
)
|
|
|
|
interpol_height2 = math.ceil(
|
|
(1 - (self.height - 2 * self.mask_height) / (self.height - 2 * interpol_height))
|
|
/ 2 * self.height
|
|
)
|
|
|
|
prev_image_fix_crop = shrink_and_paste_on_blank(
|
|
self.main_frames[i], interpol_width2, interpol_height2
|
|
)
|
|
|
|
interpol_image.paste(prev_image_fix_crop, mask=prev_image_fix_crop)
|
|
self.save2Collect(interpol_image, f"interpol_prevcrop_{i}_{j}.png")
|
|
|
|
self.contVW.append([interpol_image])
|
|
|
|
self.contVW.append([current_image])
|
|
|
|
|
|
def prepare_output_path(self):
|
|
isCollect = shared.opts.data.get("infzoom_collectAllResources", False)
|
|
output_path = shared.opts.data.get("infzoom_outpath", "outputs")
|
|
|
|
save_path = os.path.join(
|
|
output_path, shared.opts.data.get("infzoom_outSUBpath", "infinite-zooms")
|
|
)
|
|
|
|
if isCollect:
|
|
save_path = os.path.join(save_path, "iz_collect" + str(int(time.time())))
|
|
|
|
if not os.path.exists(save_path):
|
|
os.makedirs(save_path)
|
|
|
|
video_filename = os.path.join(
|
|
save_path, "infinite_zoom_" + str(int(time.time())) + ".mp4"
|
|
)
|
|
|
|
return {
|
|
"isCollect": isCollect,
|
|
"save_path": save_path,
|
|
"video_filename": video_filename,
|
|
}
|
|
|
|
|
|
def save2Collect(self, img, name):
|
|
if self.out_config["isCollect"]:
|
|
img.save(f'{self.out_config["save_path"]}/{name}.png')
|
|
|
|
|
|
def frame2Collect(self,all_frames):
|
|
self.save2Collect(all_frames[-1], self.out_config, f"frame_{len(all_frames)}")
|
|
|
|
|
|
def frames2Collect(self, all_frames):
|
|
for i, f in enumerate(all_frames):
|
|
self.save2Collect(f, self.out_config, f"frame_{i}")
|
|
|
|
|
|
def crop_inner_image(self, outpainted_img, width_offset, height_offset):
|
|
width, height = outpainted_img.size
|
|
|
|
center_x, center_y = int(width / 2), int(height / 2)
|
|
|
|
# Crop the image to the center
|
|
cropped_img = outpainted_img.crop(
|
|
(
|
|
center_x - width_offset,
|
|
center_y - height_offset,
|
|
center_x + width_offset,
|
|
center_y + height_offset,
|
|
)
|
|
)
|
|
prev_step_img = cropped_img.resize((width, height), resample=Image.LANCZOS)
|
|
# resized_img = resized_img.filter(ImageFilter.SHARPEN)
|
|
|
|
return prev_step_img
|
|
|
|
def cropCenterTo(self, im: Image, toSize: tuple[int,int]):
|
|
width, height = im.size
|
|
left = (width - toSize[0])//2
|
|
top = (height - toSize[1])//2
|
|
right = (width + toSize[0])//2
|
|
bottom = (height + toSize[1])//2
|
|
return im.crop((left, top, right, bottom))
|
|
|
|
def create_mask_with_circles(original_image, border_width, border_height, overmask: int, radius=4):
|
|
# Create a new image with border and draw a mask
|
|
new_width = original_image.width + 2 * border_width
|
|
new_height = original_image.height + 2 * border_height
|
|
|
|
# Create new image, default is black
|
|
mask = Image.new('RGB', (new_width, new_height), 'white')
|
|
|
|
# Draw black rectangle
|
|
draw = ImageDraw.Draw(mask)
|
|
draw.rectangle([border_width+overmask, border_height+overmask, new_width - border_width-overmask, new_height - border_height-overmask], fill='black')
|
|
|
|
# Coordinates for circles
|
|
circle_coords = [
|
|
(border_width, border_height), # Top-left
|
|
(new_width - border_width, border_height), # Top-right
|
|
(border_width, new_height - border_height), # Bottom-left
|
|
(new_width - border_width, new_height - border_height), # Bottom-right
|
|
(new_width // 2, border_height), # Middle-top
|
|
(new_width // 2, new_height - border_height), # Middle-bottom
|
|
(border_width, new_height // 2), # Middle-left
|
|
(new_width - border_width, new_height // 2) # Middle-right
|
|
]
|
|
|
|
# Draw circles
|
|
for coord in circle_coords:
|
|
draw.ellipse([coord[0] - radius, coord[1] - radius, coord[0] + radius, coord[1] + radius], fill='white')
|
|
return mask
|
|
|
|
|
|
|
|
|
|
|
|
def pil_to_cv2(image):
|
|
return cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
|
|
|
|
def cv2_to_pil(image):
|
|
return Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
|
|
|
|
def cv2_crop_center(img, toSize: tuple[int,int]):
|
|
y,x = img.shape[:2]
|
|
startx = x//2-(toSize[0]//2)
|
|
starty = y//2-(toSize[1]//2)
|
|
return img[starty:starty+toSize[1],startx:startx+toSize[0]]
|