Update space for app

.pre-commit-config.yaml

@@ -0,0 +1,16 @@
+repos:
+-   repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v2.3.0
+    hooks:
+    -   id: check-yaml
+    -   id: end-of-file-fixer
+    -   id: trailing-whitespace
+- repo: https://github.com/astral-sh/ruff-pre-commit
+  # Ruff version.
+  rev: v0.5.2
+  hooks:
+    # Run the linter.
+    - id: ruff
+      args: [ --fix ]
+    # Run the formatter.
+    - id: ruff-format

Dockerfile

@@ -1,24 +0,0 @@
-# Stage 1: Build Dependencies
-FROM python:slim as builder
-
-RUN apt update && \
-    apt install --no-install-recommends -y build-essential gcc
-
-COPY requirements.txt /requirements.txt
-
-RUN pip install --upgrade pip && \
-    pip install --quiet --no-cache-dir --no-warn-script-location --user -r requirements.txt
-
-# Stage 2: Runtime
-FROM python:slim
-ENV GRADIO_SERVER_NAME=0.0.0.0
-
-RUN rm -rf /var/lib/apt/lists/*
-
-COPY --from=builder /root/.local/lib/python3.11/site-packages /root/.local/lib/python3.11/site-packages
-
-COPY raccoon_emoji.png raccoon_emoji.png
-COPY app.py app.py
-
-CMD [ "python" , "-u", "app.py" ]
-EXPOSE 7860

README.md

@@ -1,12 +0,0 @@
----
-title: Fawkes
-emoji: 🌖
-colorFrom: red
-colorTo: purple
-sdk: gradio
-sdk_version: 3.0.20
-app_file: app.py
-pinned: false
-license: gpl-3.0
-python_version: 3.8.13
----

app.py

@@ -5,111 +5,117 @@ from mtcnn_cv2 import MTCNN
 
 detector = MTCNN()
 
+
 def predict(img, selection):
-  faces = detector.detect_faces(img)
-
-  privacy_fn = None
-  if(selection == "Low"):
-    opts = (anonymize_face_pixelate, 20)
-  elif(selection == "Medium"):
-    opts = (anonymize_face_pixelate, 10)
-  elif(selection == "High"):
-    opts = (anonymize_face_pixelate, 4)
-  elif(selection == "Emoji"):
-    opts = (anonymize_face_emoji, "smiley")
-  else:
-    raise Exception("I don't know how you did it but you chose something else.")
-
-  if len(faces) > 0:
-    for features in faces:
-      img = opts[0](img, features, opts[1])
-  else:
-    raise Exception("No faces detected");
-  return img
+    faces = detector.detect_faces(img)
+
+    if selection == "Low":
+        opts = (anonymize_face_pixelate, 20)
+    elif selection == "Medium":
+        opts = (anonymize_face_pixelate, 10)
+    elif selection == "High":
+        opts = (anonymize_face_pixelate, 4)
+    elif selection == "Emoji":
+        opts = (anonymize_face_emoji, "smiley")
+    else:
+        raise Exception("I don't know how you did it but you chose something else.")
+
+    if len(faces) > 0:
+        for features in faces:
+            img = opts[0](img, features, opts[1])
+    else:
+        raise Exception("No faces detected")
+    return img
+
 
 def anonymize_face_pixelate(image, features, blocks=10):
-  bb = features['box']
-  face_crop = image[bb[1]:bb[1]+bb[3], bb[0]:bb[0]+bb[2]]
-  # Divide the input image into NxN blocks
-  (h,w) = face_crop.shape[:2]
-  xSteps = np.linspace(0, w, blocks + 1, dtype="int")
-  ySteps = np.linspace(0, h, blocks + 1, dtype="int")
-
-  # loop over the blocks in both x and y direction
-  for i in range(1, len(ySteps)):
-    for j in range(1, len(xSteps)):
-      # compute starting and ending (x, y)-coordinates
-      # for current block
-      startX = xSteps[j - 1]
-      startY = ySteps[i - 1]
-      endX = xSteps[j]
-      endY = ySteps[i]
-
-      # Extract the ROI using NumPy array slicing, compute the
-      # mean of the ROI, and then draw a rectangle with the
-      # mean RGB values over the ROI in teh original image
-      roi = face_crop[startY:endY, startX:endX]
-      (B, G, R) = [int(x) for x in cv2.mean(roi)[:3]]
-      cv2.rectangle(face_crop, (startX, startY), (endX, endY),
-                    (B,G,R), -1)
-
-  image[bb[1]:bb[1]+bb[3], bb[0]:bb[0]+bb[2]] = face_crop
-  return image
+    bb = features["box"]
+    face_crop = image[bb[1] : bb[1] + bb[3], bb[0] : bb[0] + bb[2]]
+    # Divide the input image into NxN blocks
+    (h, w) = face_crop.shape[:2]
+    xSteps = np.linspace(0, w, blocks + 1, dtype="int")
+    ySteps = np.linspace(0, h, blocks + 1, dtype="int")
+
+    # loop over the blocks in both x and y direction
+    for i in range(1, len(ySteps)):
+        for j in range(1, len(xSteps)):
+            # compute starting and ending (x, y)-coordinates
+            # for current block
+            startX = xSteps[j - 1]
+            startY = ySteps[i - 1]
+            endX = xSteps[j]
+            endY = ySteps[i]
+
+            # Extract the ROI using NumPy array slicing, compute the
+            # mean of the ROI, and then draw a rectangle with the
+            # mean RGB values over the ROI in teh original image
+            roi = face_crop[startY:endY, startX:endX]
+            (B, G, R) = [int(x) for x in cv2.mean(roi)[:3]]
+            cv2.rectangle(face_crop, (startX, startY), (endX, endY), (B, G, R), -1)
+
+    image[bb[1] : bb[1] + bb[3], bb[0] : bb[0] + bb[2]] = face_crop
+    return image
+
 
 def anonymize_face_emoji(img, features, name="smiley"):
-  bb = features['box']
-  (y, x) = (bb[1] + int(bb[3]/2), bb[0] + int(bb[2]/2))
-  (h,w) = (bb[3], bb[2])
-  # Get emoji with transparency
-  mask = cv2.imread('raccoon_emoji.png', -1)
+    bb = features["box"]
+    (y, x) = (bb[1] + int(bb[3] / 2), bb[0] + int(bb[2] / 2))
+    (h, w) = (bb[3], bb[2])
+    # Get emoji with transparency
+    mask = cv2.imread("raccoon_emoji.png", -1)
 
-  mshape = max(h,w)
-  offset = int(mshape/2)
+    mshape = max(h, w)
+    offset = int(mshape / 2)
+
+    return overlay_transparent(img, mask, x - offset, y - offset, (mshape, mshape))
 
-  return overlay_transparent(img, mask, 
-                            x - offset,
-                            y - offset, 
-                            (mshape, mshape))
 
 def overlay_transparent(background_img, img_to_overlay_t, x, y, overlay_size=None):
-	"""
-	@brief      Overlays a transparant PNG onto another image using CV2
-	
-	@param      background_img    The background image
-	@param      img_to_overlay_t  The transparent image to overlay (has alpha channel)
-	@param      x                 x location to place the top-left corner of our overlay
-	@param      y                 y location to place the top-left corner of our overlay
-	@param      overlay_size      The size to scale our overlay to (tuple), no scaling if None
-	
-	@return     Background image with overlay on top
-	"""
-	
-	bg_img = background_img.copy()
-	
-	if overlay_size is not None:
-		img_to_overlay_t = cv2.resize(img_to_overlay_t.copy(), overlay_size)
-
-	# Extract the alpha mask of the RGBA image, convert to RGB 
-	b,g,r,a = cv2.split(img_to_overlay_t)
-	overlay_color = cv2.merge((b,g,r))
-	
-	# Apply some simple filtering to remove edge noise
-	mask = cv2.medianBlur(a,5)
-
-	h, w, _ = overlay_color.shape
-	roi = bg_img[y:y+h, x:x+w]
-
-	# Black-out the area behind the logo in our original ROI
-	img1_bg = cv2.bitwise_and(roi.copy(),roi.copy(),mask = cv2.bitwise_not(mask))
-	
-	# Mask out the logo from the logo image.
-	img2_fg = cv2.bitwise_and(overlay_color,overlay_color,mask = mask)
-
-	# Update the original image with our new ROI
-	bg_img[y:y+h, x:x+w] = cv2.add(img1_bg, img2_fg)
-
-	return bg_img
-
-gr.Interface(fn=predict,
-             inputs=[gr.components.Image(type='numpy'), gr.components.Radio(["Low", "Medium", "High", "Emoji"], value="Medium")],
-             outputs=gr.components.Image(type="pil"), allow_flagging="never").launch(show_error=True, quiet=False)
+    """
+    @brief      Overlays a transparant PNG onto another image using CV2
+
+    @param      background_img    The background image
+    @param      img_to_overlay_t  The transparent image to overlay (has alpha channel)
+    @param      x                 x location to place the top-left corner of our overlay
+    @param      y                 y location to place the top-left corner of our overlay
+    @param      overlay_size      The size to scale our overlay to (tuple), no scaling if None
+
+    @return     Background image with overlay on top
+    """
+
+    bg_img = background_img.copy()
+
+    if overlay_size is not None:
+        img_to_overlay_t = cv2.resize(img_to_overlay_t.copy(), overlay_size)
+
+    # Extract the alpha mask of the RGBA image, convert to RGB
+    b, g, r, a = cv2.split(img_to_overlay_t)
+    overlay_color = cv2.merge((b, g, r))
+
+    # Apply some simple filtering to remove edge noise
+    mask = cv2.medianBlur(a, 5)
+
+    h, w, _ = overlay_color.shape
+    roi = bg_img[y : y + h, x : x + w]
+
+    # Black-out the area behind the logo in our original ROI
+    img1_bg = cv2.bitwise_and(roi.copy(), roi.copy(), mask=cv2.bitwise_not(mask))
+
+    # Mask out the logo from the logo image.
+    img2_fg = cv2.bitwise_and(overlay_color, overlay_color, mask=mask)
+
+    # Update the original image with our new ROI
+    bg_img[y : y + h, x : x + w] = cv2.add(img1_bg, img2_fg)
+
+    return bg_img
+
+
+gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.components.Image(type="numpy"),
+        gr.components.Radio(["Low", "Medium", "High", "Emoji"], value="Medium"),
+    ],
+    outputs=gr.components.Image(type="pil"),
+    allow_flagging="never",
+).launch(show_error=True, quiet=False)

requirements-dev.txt

@@ -0,0 +1,2 @@
+ruff
+pre-commit

requirements.txt

@@ -1,71 +1,4 @@
-aiofiles==23.1.0
-aiohttp==3.8.3
-aiosignal==1.3.1
-altair==4.2.2
-anyio==3.6.2
-async-timeout==4.0.2
-attrs==22.1.0
-bcrypt==4.0.1
-certifi==2022.12.7
-cffi==1.15.1
-charset-normalizer==2.1.1
-click==8.1.3
-contourpy==1.0.6
-cryptography==39.0.2
-cycler==0.11.0
-emoji==2.2.0
-entrypoints==0.4
-fastapi==0.86.0
-ffmpy==0.3.0
-filelock==3.9.1
-fonttools==4.38.0
-frozenlist==1.3.3
-fsspec==2022.10.0
-gradio==3.21.0
-h11==0.12.0
-httpcore==0.15.0
-httpx==0.23.0
-huggingface-hub==0.13.2
-idna==3.4
-Jinja2==3.1.2
-jsonschema==4.17.3
-kiwisolver==1.4.4
-linkify-it-py==1.0.3
-markdown-it-py==2.2.0
-MarkupSafe==2.1.1
-matplotlib==3.6.2
-mdit-py-plugins==0.3.1
-mdurl==0.1.2
-mtcnn-opencv==1.0.2
-multidict==6.0.2
-numpy==1.23.4
-opencv-contrib-python-headless==4.6.0.66
-orjson==3.8.1
-packaging==21.3
-pandas==1.5.1
-paramiko==2.12.0
-Pillow==9.3.0
-pycparser==2.21
-pycryptodome==3.15.0
-pydantic==1.10.2
-pydub==0.25.1
-PyNaCl==1.5.0
-pyparsing==3.0.9
-pyrsistent==0.19.3
-python-dateutil==2.8.2
-python-multipart==0.0.5
-pytz==2022.6
-PyYAML==6.0
-requests==2.28.1
-rfc3986==1.5.0
-six==1.16.0
-sniffio==1.3.0
-starlette==0.20.4
-toolz==0.12.0
-tqdm==4.65.0
-typing_extensions==4.4.0
-uc-micro-py==1.0.1
-urllib3==1.26.12
-uvicorn==0.19.0
-websockets==10.4
-yarl==1.8.1
+gradio>=4.38.1
+numpy>=2.0.0
+mtcnn-opencv>=1.0.2
+opencv-python>=4.10.0.84