adds app and dockerfile files

.gitignore

@@ -0,0 +1,2 @@
+.DS_Store
+

Dockerfile

@@ -0,0 +1,12 @@
+FROM python:3.8.15
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+COPY app.py /code/app.py
+COPY examples /code/examples
+
+CMD ["python", "app.py"]

README.md

@@ -1,6 +1,6 @@
 ---
 title: Maskformer Satellite Trees Gradio Docker
-emoji: 📈
+emoji: 🛰🐳
 colorFrom: pink
 colorTo: green
 sdk: docker

app.py

@@ -0,0 +1,100 @@
+import glob
+import gradio as gr
+import numpy as np
+from os import environ
+from PIL import Image
+from torchvision import transforms as T
+from transformers import MaskFormerForInstanceSegmentation, MaskFormerImageProcessor
+
+
+example_images = sorted(glob.glob('examples/map*.jpg'))
+
+ade_mean=[0.485, 0.456, 0.406]
+ade_std=[0.229, 0.224, 0.225]
+
+test_transform = T.Compose([
+    T.ToTensor(),
+    T.Normalize(mean=ade_mean, std=ade_std)
+])
+
+palette = [
+    [120, 120, 120], [4, 200, 4], [4, 4, 250], [6, 230, 230],
+    [80, 50, 50], [120, 120, 80], [140, 140, 140], [204, 5, 255]
+]
+
+model_id = f"thiagohersan/maskformer-satellite-trees"
+vegetation_labels = ["vegetation"]
+
+# preprocessor = MaskFormerImageProcessor.from_pretrained(model_id)
+preprocessor = MaskFormerImageProcessor(
+    do_resize=False,
+    do_normalize=False,
+    do_rescale=False,
+    ignore_index=255,
+    reduce_labels=False
+)
+
+hf_token = environ.get('HFTOKEN')
+model = MaskFormerForInstanceSegmentation.from_pretrained(model_id, use_auth_token=hf_token)
+
+
+def visualize_instance_seg_mask(img_in, mask, id2label, included_labels):
+    img_out = np.zeros((mask.shape[0], mask.shape[1], 3))
+    image_total_pixels = mask.shape[0] * mask.shape[1]
+    label_ids = np.unique(mask)
+
+    id2color = {id: palette[id] for id in label_ids}
+    id2count = {id: 0 for id in label_ids}
+
+    for i in range(img_out.shape[0]):
+      for j in range(img_out.shape[1]):
+        img_out[i, j, :] = id2color[mask[i, j]]
+        id2count[mask[i, j]] = id2count[mask[i, j]] + 1
+
+    image_res = (0.5 * img_in + 0.5 * img_out).astype(np.uint8)
+
+    dataframe = [[
+        f"{id2label[id]}",
+        f"{(100 * id2count[id] / image_total_pixels):.2f} %",
+        f"{np.sqrt(id2count[id] / image_total_pixels):.2f} m"
+        ] for id in label_ids if id2label[id] in included_labels]
+
+    if len(dataframe) < 1:
+        dataframe = [[
+            f"",
+            f"{(0):.2f} %",
+            f"{(0):.2f} m"
+        ]]
+
+    return image_res, dataframe
+
+
+def query_image(image_path):
+    img = np.array(Image.open(image_path))
+    img_size = (img.shape[0], img.shape[1])
+    inputs = preprocessor(images=test_transform(img), return_tensors="pt")
+    outputs = model(**inputs)
+    results = preprocessor.post_process_semantic_segmentation(outputs=outputs, target_sizes=[img_size])[0]
+    mask_img, dataframe = visualize_instance_seg_mask(img, results.numpy(), model.config.id2label, vegetation_labels)
+    return mask_img, dataframe
+
+
+demo = gr.Interface(
+    title="Maskformer Satellite+Trees",
+    description="Using a finetuned version of the [facebook/maskformer-swin-base-ade](https://huggingface.co/facebook/maskformer-swin-base-ade) model (created specifically to work with satellite images) to calculate percentage of pixels in an image that belong to vegetation.",
+
+    fn=query_image,
+    inputs=[gr.Image(type="filepath", label="Input Image")],
+    outputs=[
+        gr.Image(label="Vegetation"),
+        gr.DataFrame(label="Info", headers=["Object Label", "Pixel Percent", "Square Length"])
+    ],
+
+    examples=example_images,
+    cache_examples=True,
+
+    allow_flagging="never",
+    analytics_enabled=None
+)
+
+demo.launch(show_api=False)

requirements.txt

@@ -0,0 +1,6 @@
+gradio==3.16.2
+Pillow==9.4.0
+scipy==1.9.3
+torch==1.13.1
+torchvision==0.14.1
+transformers==4.25.1