temporalvideo.py

import os
import glob
import requests
import json
import cv2
import numpy as np
import sys
import torch
from PIL import Image
from pprint import pprint
import base64
from io import BytesIO
import torchvision.transforms.functional as F
from torchvision.io import read_video, read_image, ImageReadMode
from torchvision.models.optical_flow import Raft_Large_Weights
from torchvision.models.optical_flow import raft_large
from torchvision.io import read_video, read_image, ImageReadMode
from torchvision.utils import flow_to_image
import cv2
from torchvision.io import write_jpeg
import pickle


device = "cuda" if torch.cuda.is_available() else "cpu"

model = raft_large(weights=Raft_Large_Weights.DEFAULT, progress=False).to(device)
model = model.eval()

# Replace with the actual path to your image file and folder
x_path = "./init.png"
y_folder = "./Input_Images"

output_folder = "output"
os.makedirs(output_folder, exist_ok=True)

def get_image_paths(folder):
    image_extensions = ("*.jpg", "*.jpeg", "*.png", "*.bmp")
    files = []
    for ext in image_extensions:
        files.extend(glob.glob(os.path.join(folder, ext)))
    return sorted(files)

y_paths = get_image_paths(y_folder)

def send_request(last_image_path, optical_flow_path,current_image_path):
    url = "http://localhost:7860/sdapi/v1/img2img"
    
    with open(last_image_path, "rb") as b:
       last_image_encoded = base64.b64encode(b.read()).decode("utf-8")
    
    # Load and process the last image
    last_image = cv2.imread(last_image_path)
    last_image = cv2.cvtColor(last_image, cv2.COLOR_BGR2RGB)
    last_image = cv2.resize(last_image, (512, 512))

    # Load and process the optical flow image
    flow_image = cv2.imread(optical_flow_path)
    flow_image = cv2.cvtColor(flow_image, cv2.COLOR_BGR2RGB)

    # Load and process the current image
    with open(current_image_path, "rb") as b:
       current_image = base64.b64encode(b.read()).decode("utf-8")


    # Concatenating the three images to make a 6-channel image
    six_channel_image = np.dstack((last_image, flow_image))

    # Serializing the 6-channel image
    serialized_image = pickle.dumps(six_channel_image)

    # Encoding the serialized image
    encoded_image = base64.b64encode(serialized_image).decode('utf-8')

    data = {
        "init_images": [current_image],
        "inpainting_fill": 0,
        "inpaint_full_res": True,
        "inpaint_full_res_padding": 1,
        "inpainting_mask_invert": 1,
        "resize_mode": 0,
        "denoising_strength": 0.4,
        "prompt": "1girl, woman",
        "negative_prompt": "",
        "alwayson_scripts": {
            "ControlNet":{
                "args": [
                    {
                        "input_image": current_image,
                        "module": "hed",
                        "model": "control_hed-fp16 [13fee50b]",
                        "weight": 0.7,
                        "guidance": 1,
                   },
                    {
                        "input_image": encoded_image,
                        "model": "temporalnetversion2 [b146ac48]",
                        "module": "none",
                        "weight": 0.6,
                        "guidance": 1,
                    },
                    {
                        "input_image": current_image,
                        "model": "control_v11p_sd15_openpose [cab727d4]",
                        "module": "openpose_full",
                        "weight": 0.7,
                        "guidance":1,
                    }
                    
                  
                ]
            }
        },
        "seed": 4123457655,
        "subseed": -1,
        "subseed_strength": -1,
        "sampler_index": "Euler a",
        "batch_size": 1,
        "n_iter": 1,
        "steps": 20,
        "cfg_scale": 6,
        "width": 512,
        "height": 512,
        "restore_faces": True,
        "include_init_images": True,
        "override_settings": {},
        "override_settings_restore_afterwards": True
    }
    response = requests.post(url, json=data)
    if response.status_code == 200:
        return response.content
    else:
        try:
            error_data = response.json()
            print("Error:")
            print(str(error_data))
            
        except json.JSONDecodeError:
            print(f"Error: Unable to parse JSON error data.")
        return None



def infer(frameA, frameB):
    
    
    input_frame_1 = read_image(str(frameA), ImageReadMode.RGB)
   
    input_frame_2 = read_image(str(frameB), ImageReadMode.RGB)
 
    
    #img1_batch = torch.stack([frames[0]])
    #img2_batch = torch.stack([frames[1]])

    img1_batch = torch.stack([input_frame_1])
    img2_batch = torch.stack([input_frame_2])
    
    
    weights = Raft_Large_Weights.DEFAULT
    transforms = weights.transforms()


    def preprocess(img1_batch, img2_batch):
        img1_batch = F.resize(img1_batch, size=[512, 512])
        img2_batch = F.resize(img2_batch, size=[512, 512])
        return transforms(img1_batch, img2_batch)


    img1_batch, img2_batch = preprocess(img1_batch, img2_batch)


    list_of_flows = model(img1_batch.to(device), img2_batch.to(device))

    predicted_flows = list_of_flows[-1]


    #flow_imgs = flow_to_image(predicted_flows)

    #print(flow_imgs)

    predicted_flow = list_of_flows[-1][0]
    opitcal_flow_path = os.path.join(output_folder, f"flow_{i}.png")
    flow_img = flow_to_image(predicted_flow).to("cpu")
    write_jpeg(flow_img,opitcal_flow_path)
    
    
    return opitcal_flow_path

output_images = []
output_paths = []

# Initialize with the first image path

result = x_path
output_image_path = os.path.join(output_folder, f"output_image_0.png")

#with open(output_image_path, "wb") as f:
   # f.write(result)
    
last_image_path = x_path
for i in range(1, len(y_paths)):
    # Use the last image path and optical flow map to generate the next input
    optical_flow = infer(y_paths[i - 1], y_paths[i])
    
    # Modify your send_request to use the last_image_path
    result = send_request(last_image_path, optical_flow, y_paths[i])
    data = json.loads(result)
    encoded_image = data["images"][0]
    output_image_path = os.path.join(output_folder, f"output_image_{i}.png")
    last_image_path = output_image_path
    with open(output_image_path, "wb") as f:
       f.write(base64.b64decode(encoded_image))
    print(f"Written data for frame {i}:")