Added application file

Dockerfile

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+# FROM python:3.9
+
+# ENV DEBIAN_FRONTEND=noninteractive
+
+# # Remove any third-party apt sources to avoid issues with expiring keys.
+# # Install some basic utilities
+# RUN rm -f /etc/apt/sources.list.d/*.list && \
+#     apt-get update && apt-get install -y --no-install-recommends \
+#     curl \
+#     ca-certificates \
+#     sudo \
+#     git \
+#     wget \
+#     procps \
+#     git-lfs \
+#     zip \
+#     unzip \
+#     htop \
+#     vim \
+#     nano \
+#     bzip2 \
+#     libx11-6 \
+#     build-essential \
+#     libsndfile-dev \
+#     software-properties-common && \
+#     rm -rf /var/lib/apt/lists/*
+
+# RUN git clone https://github.com/abc-valera/dotfiles.git
+# # && cd dotfiles && sudo ./install.sh
+
+# RUN useradd -m -u 1000 user
+# USER user
+# ENV PATH="/home/user/.local/bin:$PATH"
+
+# WORKDIR /app
+
+# COPY --chown=user ./requirements.txt requirements.txt
+# RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+# COPY --chown=user . /app
+# CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
+
+
+# read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
+# you will also find guides on how best to write your Dockerfile
+
+FROM python:3.9
+
+# The two following lines are requirements for the Dev Mode to be functional
+# Learn more about the Dev Mode at https://huggingface.co/dev-mode-explorers
+RUN useradd -m -u 1000 user
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user . /app
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

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+from fastapi import FastAPI
+
+
+app = FastAPI()
+
+
+@app.get("/")
+def read_root():
+    return {"Hello": "World!"}

requirements.txt

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+# requests
+# pillow
+# transformers[torch]
+# opencv-python
+
+fastapi==0.74.*
+requests==2.27.*
+sentencepiece==0.1.*
+torch==1.11.*
+transformers==4.*
+uvicorn[standard]==0.17.*

superpoint.py

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+from transformers import (
+    SuperPointImageProcessor,
+    SuperPointForKeypointDetection as SuperPointKeypointDetection,
+)
+import torch
+import cv2
+import numpy as np
+import os
+
+
+### ЗАВАНТАЖЕННЯ І ПОПЕРЕДНЯ ОБРОБКА ДАНИХ ###
+
+# Створюємо директорію для вихідних файлів
+os.makedirs("output", exist_ok=True)
+
+images = ["data/image0.jpg", "data/image1.jpg", "data/image2.jpg", "data/image3.jpg"]
+original_images = []
+resized_images = []
+
+for image_path in images:
+    image = cv2.imread(image_path)
+    original_images.append(image)
+    resized_image = cv2.resize(image, (640, 480))
+    resized_images.append(resized_image)
+
+
+### ЗАСТОСУВАННЯ ДЕТЕКТОРА ОЗНАК SUPERPOINT ###
+
+
+processor = SuperPointImageProcessor.from_pretrained("magic-leap-community/superpoint")
+model = SuperPointKeypointDetection.from_pretrained("magic-leap-community/superpoint")
+
+inputs = processor(resized_images, return_tensors="pt")
+outputs = model(**inputs)
+
+
+### ВІЗУАЛІЗАЦІЯ РЕЗУЛЬТАТІВ ###
+
+
+def draw_keypoints(image, keypoints, color=(0, 255, 0), radius=2):
+    for kp in keypoints:
+        x, y = int(kp[0]), int(kp[1])
+        cv2.circle(image, (x, y), radius, color, -1)
+    return image
+
+
+def create_blank_image(shape):
+    return np.zeros((shape[0], shape[1], 3), dtype=np.uint8)
+
+
+all_keypoints = []
+
+for i, (original_image, resized_image) in enumerate(
+    zip(original_images, resized_images)
+):
+    image_mask = outputs.mask[i]
+    image_indices = torch.nonzero(image_mask).squeeze()
+    image_keypoints = outputs.keypoints[i][image_indices]
+
+    # Масштабуємо ключові точки назад до оригінального розміру
+    scale_x = original_image.shape[1] / resized_image.shape[1]
+    scale_y = original_image.shape[0] / resized_image.shape[0]
+    scaled_keypoints = image_keypoints.clone()
+    scaled_keypoints[:, 0] *= scale_x
+    scaled_keypoints[:, 1] *= scale_y
+
+    all_keypoints.append(scaled_keypoints)
+
+    # Створюємо зображення з ключовими точками
+    keypoints_image = draw_keypoints(original_image.copy(), scaled_keypoints)
+    cv2.imwrite(f"output/image{i}.png", keypoints_image)
+
+    # Створюємо зображення тільки з ключовими точками
+    blank_image = create_blank_image(original_image.shape[:2])
+    just_keypoints_image = draw_keypoints(blank_image, scaled_keypoints)
+    cv2.imwrite(f"output/image{i}_just_keypoints.png", just_keypoints_image)
+
+
+### СПІВСТАВЛЕННЯ ОЗНАК ###
+
+
+def match_keypoints(img1, kp1, img2, kp2, method="flann"):
+    # Convert keypoints to cv2.KeyPoint objects
+    kp1 = [cv2.KeyPoint(x=float(kp[0]), y=float(kp[1]), size=1) for kp in kp1]
+    kp2 = [cv2.KeyPoint(x=float(kp[0]), y=float(kp[1]), size=1) for kp in kp2]
+
+    # Compute descriptors
+    sift = cv2.SIFT_create()
+    _, des1 = sift.compute(img1, kp1)
+    _, des2 = sift.compute(img2, kp2)
+
+    if method == "flann":
+        FLANN_INDEX_KDTREE = 1
+        index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
+        search_params = dict(checks=50)
+        flann = cv2.FlannBasedMatcher(index_params, search_params)
+        matches = flann.knnMatch(des1, des2, k=2)
+    else:  # BF Matcher
+        bf = cv2.BFMatcher()
+        matches = bf.knnMatch(des1, des2, k=2)
+
+    # Apply ratio test
+    good_matches = []
+    for m, n in matches:
+        if m.distance < 0.7 * n.distance:
+            good_matches.append(m)
+
+    # Draw matches
+    img_matches = cv2.drawMatches(
+        img1,
+        kp1,
+        img2,
+        kp2,
+        good_matches,
+        None,
+        flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS,
+    )
+    return img_matches
+
+
+# Застосовуємо алгоритми співставлення ознак, якщо є більше одного зображення
+if len(images) > 1:
+    for i in range(1, len(images)):
+        # FLANN matching
+        flann_matches = match_keypoints(
+            original_images[0],
+            all_keypoints[0],
+            original_images[i],
+            all_keypoints[i],
+            method="flann",
+        )
+        cv2.imwrite(f"output/image0_image{i}_flann.png", flann_matches)
+
+        # BF matching
+        bf_matches = match_keypoints(
+            original_images[0],
+            all_keypoints[0],
+            original_images[i],
+            all_keypoints[i],
+            method="bf",
+        )
+        cv2.imwrite(f"output/image0_image{i}_bf.png", bf_matches)