Duplicate from anon-SGXT/echocardiogram-video-diffusion

Co-authored-by: Anonymous <anon-SGXT@users.noreply.huggingface.co>

.gitattributes

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.gitignore

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+*.mp4
+*.ipynb
+*__pycache__*

README.md

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+---
+title: EchoNet Video Diffusion
+emoji: 🖤
+colorFrom: gray
+colorTo: purple
+sdk: gradio
+sdk_version: 3.17.0
+app_file: app.py
+pinned: false
+license: mit
+duplicated_from: anon-SGXT/echocardiogram-video-diffusion
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import os
+from omegaconf import OmegaConf
+from imagen_pytorch import Unet3D, ElucidatedImagen, ImagenTrainer, ElucidatedImagenConfig, NullUnet, Imagen
+import torch
+import numpy as np
+import cv2
+from PIL import Image
+import torchvision.transforms as T
+
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+exp_path = "model"
+
+class BetterCenterCrop(T.CenterCrop):
+    def __call__(self, img):
+        h = img.shape[-2]
+        w = img.shape[-1]
+        dim = min(h, w)
+
+        return T.functional.center_crop(img, dim)
+
+class ImageLoader:
+    def __init__(self, path) -> None:
+        self.path = path
+        self.all_files = os.listdir(path)
+        self.transform = T.Compose([
+            T.ToTensor(),
+            BetterCenterCrop((112, 112)),
+            T.Resize((112, 112)),
+        ])
+        
+    def get_image(self):
+        idx = np.random.randint(0, len(self.all_files))
+        img = Image.open(os.path.join(self.path, self.all_files[idx]))
+        return img
+
+class Context:
+    def __init__(self, path, device):
+        self.path = path
+        self.config_path = os.path.join(path, "config.yaml")
+        self.weight_path = os.path.join(path, "merged.pt")
+        
+        self.config = OmegaConf.load(self.config_path)
+
+        self.config.dataset.num_frames = int(self.config.dataset.fps * self.config.dataset.duration)
+
+        self.im_load = ImageLoader("echo_images")
+
+        unets = []
+        for i, (k, v) in enumerate(self.config.unets.items()):
+            unets.append(Unet3D(**v, lowres_cond=(i>0))) # type: ignore
+
+        imagen_klass = ElucidatedImagen if self.config.imagen.elucidated == True else Imagen
+        del self.config.imagen.elucidated
+        imagen = imagen_klass(
+            unets = unets,
+            **OmegaConf.to_container(self.config.imagen), # type: ignore
+        )
+
+        self.trainer = ImagenTrainer(
+            imagen = imagen,
+            **self.config.trainer
+        ).to(device)
+
+        print("Loading weights from", self.weight_path)
+        additional_data = self.trainer.load(self.weight_path)
+        print("Loaded weights from", self.weight_path)
+    
+    def reshape_image(self, image):
+        try:
+            image = self.im_load.transform(image).multiply(255).byte().permute(1,2,0).numpy()
+            return image
+        except:
+            return None
+    
+    def load_random_image(self):
+        print("Loading random image")        
+        image = self.im_load.get_image()
+        return image
+    
+    def generate_video(self, image, lvef, cond_scale):
+        print("Generating video")
+        print(f"lvef: {lvef}, cond_scale: {cond_scale}")
+
+        image = self.im_load.transform(image).unsqueeze(0)
+
+        sample_kwargs = {}
+        sample_kwargs = {
+            "text_embeds": torch.tensor([[[lvef/100.0]]]),
+            "cond_scale": cond_scale,
+            "cond_images": image,
+        }
+            
+        self.trainer.eval()
+        with torch.no_grad():
+            video = self.trainer.sample(
+                batch_size=1, 
+                video_frames=self.config.dataset.num_frames,
+                **sample_kwargs,
+                use_tqdm = True,
+            ).detach().cpu() # C x F x H x W
+        if video.shape[-3:] != (64, 112, 112):
+            video = torch.nn.functional.interpolate(video, size=(64, 112, 112), mode='trilinear', align_corners=False)
+        video = video.repeat((1,1,5,1,1)) # make the video loop 5 times - easier to see
+        uid = np.random.randint(0, 10) # prevent overwriting if multiple users are using the app
+        path = f"tmp/{uid}.mp4"
+        video = video.multiply(255).byte().squeeze(0).permute(1, 2, 3, 0).numpy()
+        out = cv2.VideoWriter(path, cv2.VideoWriter_fourcc(*'mp4v'), 32, (112, 112))
+        for i in video:
+            out.write(i)
+        out.release()
+        return path
+
+context = Context(exp_path, device)
+
+with gr.Blocks(css="style.css") as demo:
+
+    with gr.Row():
+        gr.Label("Cardiac Ultrasound Video Generation Demo (paper: 905)")
+
+    with gr.Row():
+        with gr.Column():
+            with gr.Row():
+                with gr.Column(scale=3, variant="panel"):
+                    text = gr.Markdown(value="This is a live demo of our work on cardiac ultrasound video generation. The model is trained on 4-chamber cardiac ultrasound videos and can generate realistic 4-chamber videos given a target Left Ventricle Ejection Fraction. Please, start by sampling a random frame from the pool of 100 images taken from the EchoNet-Dynamic dataset, which will act as the conditional image, representing the anatomy of the video. Then, set the target LVEF, and click the button to generate a video. The process takes 30s to 60s. The model running here corresponds to the 1SCM from the paper. **Click on the video to play it.** ")
+                with gr.Column(scale=1, min_width="226"):
+                    image = gr.Image(interactive=True)
+                with gr.Column(scale=1, min_width="226"):
+                    video = gr.Video(interactive=False)
+
+            slider_ef = gr.Slider(minimum=10, maximum=90, step=1, label="Target LVEF", value=60, interactive=True)
+            slider_cond = gr.Slider(minimum=0, maximum=20, step=1, label="Conditional scale (if set to more than 1, generation time is 60s)", value=1, interactive=True)
+
+            with gr.Row():
+                img_btn = gr.Button(value="❶ Get a random cardiac ultrasound image (4Ch)")
+                run_btn = gr.Button(value="❷ Generate a video (~30s) 🚀")
+
+    image.change(context.reshape_image, inputs=[image], outputs=[image])
+    img_btn.click(context.load_random_image, inputs=[], outputs=[image])
+    run_btn.click(context.generate_video, inputs=[image, slider_ef, slider_cond], outputs=[video])
+ 
+if __name__ == "__main__":
+    demo.queue()
+    demo.launch()