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from doctest import Example |
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import gradio as gr |
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from transformers import DPTImageProcessor, DPTForDepthEstimation |
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import torch |
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import numpy as np |
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from PIL import Image, ImageOps |
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from pathlib import Path |
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import glob |
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from autostereogram.converter import StereogramConverter |
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from datetime import datetime |
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import time |
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import tempfile |
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feature_extractor = DPTImageProcessor.from_pretrained("Intel/dpt-large") |
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model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large") |
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stereo_converter = StereogramConverter() |
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def process_image(image_path): |
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print("\n\n\n") |
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print("Processing image:", image_path) |
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last_time = time.time() |
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image_raw = Image.open(Path(image_path)) |
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image = image_raw.resize( |
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(1280, int(1280 * image_raw.size[1] / image_raw.size[0])), |
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Image.Resampling.LANCZOS, |
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) |
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encoding = feature_extractor(image, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model(**encoding) |
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predicted_depth = outputs.predicted_depth |
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prediction = torch.nn.functional.interpolate( |
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predicted_depth.unsqueeze(1), |
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size=image.size[::-1], |
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mode="bicubic", |
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align_corners=False, |
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).squeeze() |
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output = prediction.cpu().numpy() |
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depth_image = (output * 255 / np.max(output)).astype("uint8") |
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depth_image_padded = np.array( |
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ImageOps.pad(Image.fromarray(depth_image), (1280, 720)) |
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) |
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stereo_image = stereo_converter.convert_depth_to_stereogram_with_thread_pool( |
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depth_image_padded, False |
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).astype(np.uint8) |
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stereo_image_pil = Image.fromarray(stereo_image).convert("RGB") |
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with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as f: |
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image_name = f.name |
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stereo_image_pil.save(image_name) |
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return [depth_image_padded, stereo_image, image_name] |
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examples_images = [[f] for f in sorted(glob.glob("examples/*.jpg"))] |
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with gr.Blocks() as blocks: |
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gr.Markdown( |
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""" |
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## Depth Image to Autostereogram (Magic Eye) |
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This demo is a variation from the original [DPT Demo](https://huggingface.co/spaces/nielsr/dpt-depth-estimation). |
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Zero-shot depth estimation from an image, then it uses [pystereogram](https://github.com/yxiao1996/pystereogram) |
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to generate the autostereogram (Magic Eye) |
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<base target="_blank"> |
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""" |
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) |
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with gr.Row(): |
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with gr.Column(): |
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input_image = gr.Image(type="filepath", label="Input Image") |
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button = gr.Button("Predict") |
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with gr.Column(): |
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predicted_depth = gr.Image(label="Predicted Depth", type="pil") |
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with gr.Row(): |
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autostereogram = gr.Image(label="Autostereogram", type="pil") |
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with gr.Row(): |
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with gr.Column(): |
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file_download = gr.File(label="Download Image") |
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with gr.Row(): |
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gr.Examples( |
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examples=examples_images, |
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fn=process_image, |
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inputs=[input_image], |
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outputs=[predicted_depth, autostereogram, file_download], |
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cache_examples=True, |
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) |
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button.click( |
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fn=process_image, |
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inputs=[input_image], |
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outputs=[predicted_depth, autostereogram, file_download], |
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) |
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blocks.launch(debug=True) |
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