141 lines
5.2 KiB
Python
141 lines
5.2 KiB
Python
import requests
|
|
import cv2
|
|
import base64
|
|
import numpy as np
|
|
from tqdm import tqdm
|
|
import os
|
|
|
|
INPUT_VIDEO = "video_input.mp4"
|
|
OUTPUT_VIDEO = "result.mp4"
|
|
REF_IMAGE = "init.png"
|
|
|
|
PROMPT = "pixarstyle 3D cartoon version of Pulp Fiction apartment Scene, natural skin texture, 4k textures, hdr, intricate, highly detailed, sharp focus, cinematic look, hyperdetailed. White man, black man, strong man, 90's room, gun, burger and cola."
|
|
N_PROMPT = "cropped head, black and white, slanted eyes, deformed, bad anatomy, disfigured, poorly drawn face, mutation, mutated, extra limb, ugly, disgusting, poorly drawn hands, missing limb, floating limbs, disconnected limbs, malformed hands, blurry, ((((mutated hands and fingers)))), watermark, watermarked, oversaturated, censored, distorted hands, amputation, missing hands, obese, doubled face, double hands"
|
|
SEED = 2901260158
|
|
w,h = 1216, 512 # Width and height of the processed image. Note that actual image processed would be a W x 2H resolution. You should have enough VRAM to process it.
|
|
|
|
|
|
START_FROM_IND = 2235 # index of a frame to start a processing from. Might be helpful with long animations where you need to restart the script multiple times
|
|
SAVE_FRAMES = True # saves individual frames into 'out' folder if set True. Again might be helpful with long animations
|
|
|
|
def to_b64(img):
|
|
_, buffer = cv2.imencode('.png', img)
|
|
b64img = base64.b64encode(buffer).decode("utf-8")
|
|
return b64img
|
|
|
|
mask_img = np.zeros((h * 2,w,3), dtype=np.uint8)
|
|
mask_img[:h] = 255
|
|
b64_mask_img = to_b64(mask_img)
|
|
|
|
# load context image to make generations more stable
|
|
cont_img = cv2.imread(REF_IMAGE)
|
|
cont_img = cv2.resize(cont_img, (w,h))
|
|
|
|
class controlnetRequest():
|
|
def __init__(self, b64_full_img):
|
|
self.url = "http://localhost:7860/controlnet/img2img"
|
|
self.body = {
|
|
"init_images": [b64_full_img],
|
|
"mask": b64_mask_img,
|
|
"mask_blur": 0,
|
|
"inpainting_fill": 1,
|
|
"prompt": PROMPT,
|
|
"negative_prompt": N_PROMPT,
|
|
"seed": SEED,
|
|
"subseed": -1,
|
|
"subseed_strength": 0,
|
|
"batch_size": 1,
|
|
"n_iter": 1,
|
|
"steps": 20,
|
|
"cfg_scale": 4.5,
|
|
"denoising_strength":0.6,
|
|
"width": w,
|
|
"height": h * 2,
|
|
"restore_faces": False,
|
|
"eta": 0,
|
|
"sampler_index": "DPM++ 2S a",
|
|
"controlnet_units": [
|
|
{
|
|
"module": "hed",
|
|
"model": "control_hed-fp16 [13fee50b]",
|
|
"weight": 0.6,
|
|
"resize_mode": "Just Resize",
|
|
"lowvram": False,
|
|
"processor_res": 64,
|
|
"threshold_a": 64,
|
|
"threshold_b": 64,
|
|
"guidance": 0.6,
|
|
"guessmode": False
|
|
},
|
|
{
|
|
"module": "none",
|
|
"model": "t2iadapter_color_sd14v1 [8522029d]",
|
|
"weight": 0.6,
|
|
"resize_mode": "Just Resize",
|
|
"lowvram": False,
|
|
"processor_res": 64,
|
|
"threshold_a": 64,
|
|
"threshold_b": 64,
|
|
"guidance": 0.6,
|
|
"guessmode": False
|
|
}
|
|
]
|
|
}
|
|
|
|
def sendRequest(self):
|
|
r = requests.post(self.url, json=self.body)
|
|
return r.json()
|
|
|
|
# Open the input video file
|
|
input_video = cv2.VideoCapture(INPUT_VIDEO)
|
|
|
|
# Get useful info from the souce video
|
|
fps = int(input_video.get(cv2.CAP_PROP_FPS))
|
|
total_frames = int(input_video.get(cv2.CAP_PROP_FRAME_COUNT))
|
|
|
|
# Create an output video file with the same fps, width, and height as the input video
|
|
output_video = cv2.VideoWriter(OUTPUT_VIDEO, cv2.VideoWriter_fourcc(*'MP4V'), fps, (w, h))
|
|
|
|
for ind in tqdm(range(total_frames)):
|
|
# Read the next frame from the input video
|
|
if not input_video.isOpened(): break
|
|
ret, frame = input_video.read()
|
|
if not ret: break
|
|
|
|
if ind+1 < START_FROM_IND: continue
|
|
|
|
# Resize the frame to proper resolution
|
|
frame = cv2.resize(frame, (w,h))
|
|
|
|
full_img = cv2.vconcat([frame, cont_img])
|
|
full_img = cv2.resize(full_img, (w,h * 2))
|
|
b64_full_img = to_b64(full_img)
|
|
|
|
# Sending request to the web-ui
|
|
data_js = controlnetRequest(b64_full_img).sendRequest()
|
|
|
|
# Convert the byte array to a NumPy array
|
|
image_bytes = base64.b64decode(data_js["images"][0])
|
|
np_array = np.frombuffer(image_bytes, dtype=np.uint8)
|
|
|
|
# Convert the NumPy array to a cv2 image
|
|
cv2_image = cv2.imdecode(np_array, cv2.IMREAD_COLOR)
|
|
|
|
# Write the frame to the output video
|
|
cv2_image = cv2_image[:h]
|
|
output_video.write(cv2_image)
|
|
|
|
# show the last written frame - useful to catch any issue with the process
|
|
cv2.imshow('Out img', cv2_image)
|
|
|
|
if SAVE_FRAMES:
|
|
if not os.path.isdir('out'): os.makedirs('out')
|
|
cv2.imwrite(f'out/{ind+1:05d}.png', cv2_image)
|
|
if cv2.waitKey(1) & 0xFF == ord('q'): break # press Q to close the script while processing
|
|
|
|
# Release the input and output video files
|
|
input_video.release()
|
|
output_video.release()
|
|
|
|
# Close all windows
|
|
cv2.destroyAllWindows() |