Revert "added more examples"

This reverts commit bb018e685e48b00bdb71b50254c58f294c1cea1a.

app.py

@@ -1,145 +0,0 @@
-import gradio as gr
-from matplotlib import pyplot as plt
-from mapper.utils.io import read_image
-from mapper.utils.exif import EXIF
-from mapper.utils.wrappers import Camera
-from mapper.data.image import rectify_image, pad_image, resize_image
-from mapper.utils.viz_2d import one_hot_argmax_to_rgb, plot_images
-from mapper.module import GenericModule
-from perspective2d import PerspectiveFields
-import torch
-import numpy as np
-from typing import Optional, Tuple
-from omegaconf import OmegaConf
-
-description = """
-<h1 align="center">
-  <ins>MapItAnywhere (MIA) </ins>
-  <br>
-  Empowering Bird’s Eye View Mapping using Large-scale Public Data
-  <br>
-<h3 align="center">
-    <a href="https://mapitanywhere.github.io" target="_blank">Project Page</a> |
-    <a href="https://arxiv.org/abs/2109.08203" target="_blank">Paper</a> |
-    <a href="https://github.com/MapItAnywhere/MapItAnywhere" target="_blank">Code</a>
-</h3>
-<p align="center">
-Mapper generates birds-eye-view maps from in-the-wild monocular first-person view images. You can try our demo by uploading your images or using the examples provided. Tip: You can also try out images across the world using <a href="https://www.mapillary.com/app" target="_blank">Mapillary</a> &#128521;
-</p>
-"""
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-cfg = OmegaConf.load("config.yaml")
-
-class ImageCalibrator(PerspectiveFields):
-    def __init__(self, version: str = "Paramnet-360Cities-edina-centered"):
-        super().__init__(version)
-        self.eval()
-
-    def run(
-        self,
-        image_rgb: np.ndarray,
-        focal_length: Optional[float] = None,
-        exif: Optional[EXIF] = None,
-    ) -> Tuple[Tuple[float, float], Camera]:
-        h, w, *_ = image_rgb.shape
-        if focal_length is None and exif is not None:
-            _, focal_ratio = exif.extract_focal()
-            if focal_ratio != 0:
-                focal_length = focal_ratio * max(h, w)
-        calib = self.inference(img_bgr=image_rgb[..., ::-1])
-        roll_pitch = (calib["pred_roll"].item(), calib["pred_pitch"].item())
-        if focal_length is None:
-            vfov = calib["pred_vfov"].item()
-            focal_length = h / 2 / np.tan(np.deg2rad(vfov) / 2)
-
-        camera = Camera.from_dict(
-            {
-                "model": "SIMPLE_PINHOLE",
-                "width": w,
-                "height": h,
-                "params": [focal_length, w / 2 + 0.5, h / 2 + 0.5],
-            }
-        )
-        return roll_pitch, camera
-
-def preprocess_pipeline(image, roll_pitch, camera):
-    image = torch.from_numpy(image).float() / 255
-    image = image.permute(2, 0, 1).to(device)
-    camera = camera.to(device)
-
-    image, valid = rectify_image(image, camera.float(), -roll_pitch[0], -roll_pitch[1])
-
-    roll_pitch *= 0
-
-    image, _, camera, valid = resize_image(
-        image=image,
-        size=512,
-        camera=camera,
-        fn=max,
-        valid=valid
-    )
-
-    image, valid, camera = pad_image(
-        image, 512, camera, valid
-    )
-
-    camera = torch.stack([camera])
-
-    return {
-        "image": image.unsqueeze(0).to(device),
-        "valid": valid.unsqueeze(0).to(device),
-        "camera": camera.float().to(device),
-    }
-
-
-calibrator = ImageCalibrator().to(device)
-model = GenericModule(cfg)
-model = model.load_from_checkpoint("trained_weights/mapper-excl-ood.ckpt", strict=False, cfg=cfg)
-model = model.to(device)
-model = model.eval()
-
-def run(input_img):
-    image_path = input_img.name
-
-    image = read_image(image_path)
-    with open(image_path, "rb") as fid:
-        exif = EXIF(fid, lambda: image.shape[:2])
-
-    gravity, camera = calibrator.run(image, exif=exif)
-
-    data = preprocess_pipeline(image, gravity, camera)
-    res = model(data)
-    
-    prediction = res['output']
-    rgb_prediction = one_hot_argmax_to_rgb(prediction, 6).squeeze(0).permute(1, 2, 0).cpu().long().numpy()
-    valid = res['valid_bev'].squeeze(0)[..., :-1]
-    rgb_prediction[~valid.cpu().numpy()] = 255
-    
-     # TODO: add legend here
-
-    plot_images([image, rgb_prediction], titles=["Input Image", "Prediction"], pad=2, adaptive=True)
-
-    return plt.gcf()
-
-
-examples = [
-    ["examples/left_crossing.jpg"],
-    ["examples/crossing.jpg"]
-    ["examples/two_roads.jpg"],
-    ["examples/night_road.jpg"],
-    ["examples/night_crossing.jpg"],
-]
-
-demo = gr.Interface(
-    fn=run,
-    inputs=[
-        gr.File(file_types=["image"], label="Input Image")
-    ], 
-    outputs=[
-        gr.Plot(label="Prediction", format="png"),
-    ],
-    description=description,
-    examples=examples)
-demo.launch(share=False, server_name="0.0.0.0")