initial commit

.gitattributes

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+models/haarcascade_frontalface_alt2.xml filter=lfs diff=lfs merge=lfs -text
+models/lbfmodel.yaml filter=lfs diff=lfs merge=lfs -text
+imgs/03.webp filter=lfs diff=lfs merge=lfs -text
+imgs/11.jpg filter=lfs diff=lfs merge=lfs -text

.github/workflows/deploy-hf.yml

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+name: Deploy to Hugging Face spaces
+
+on:
+  push:
+    branches:
+      - main
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout Dev Repo
+        uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+          lfs: true
+
+      - name: Push to HF
+        env:
+          HFTOKEN: ${{ secrets.HFTOKEN }}
+
+        run: |
+          git remote add hf https://thiagohersan:[email protected]/spaces/5020A/5020-FaceAlign-Gradio
+          git push -f hf main

.gitignore

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+.DS_S*
+__pycache__/
+gradio_cached_examples/
+.ipynb_checkpoints/

README.md

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+---
+title: Face Align
+emoji: 🤨📐
+colorFrom: blue
+colorTo: gray
+sdk: gradio
+sdk_version: 4.42.0
+app_file: app.py
+pinned: false
+---

app.py

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+import cv2
+import gradio as gr
+import numpy as np
+import matplotlib.pyplot as plt
+
+from math import atan2
+from os import listdir, path
+from PIL import Image as PImage
+
+OUT_W = 130
+OUT_H = 170
+OUT_EYE_SPACE = 64
+OUT_NOSE_TOP = 72
+
+EYE_0_IDX = 36
+EYE_1_IDX = 45
+
+haarcascade = "./models/haarcascade_frontalface_alt2.xml"
+face_detector = cv2.CascadeClassifier(haarcascade)
+
+LBFmodel = "./models/lbfmodel.yaml"
+landmark_detector  = cv2.face.createFacemarkLBF()
+landmark_detector.loadModel(LBFmodel)
+
+NUM_OUTS = 16
+all_outputs = [gr.Image(format="jpeg", visible=False) for _ in range(NUM_OUTS)]
+
+def face(img_in):
+  out_pad = NUM_OUTS * [gr.Image(visible=False)]
+  if img_in is None:
+    return out_pad
+
+  pimg = img_in.convert("L")
+  pimg.thumbnail((1000,1000))
+  imgg = np.array(pimg).copy()
+
+  iw,ih = pimg.size
+  
+  faces = face_detector.detectMultiScale(imgg)
+
+  if len(faces) < 1:
+    return out_pad
+
+  biggest_faces = faces[np.argsort(-faces[:,2])]
+  _, landmarks = landmark_detector.fit(imgg, biggest_faces)
+
+  if len(landmarks) < 1:
+    return out_pad
+
+  out_images = []
+  for landmark in landmarks:
+    eye0 = np.array(landmark[0][EYE_0_IDX])
+    eye1 = np.array(landmark[0][EYE_1_IDX])
+    mid = np.mean([eye0, eye1], axis=0)
+
+    eye_line = eye1 - eye0
+    tilt = atan2(eye_line[1], eye_line[0])
+    tilt_deg = 180 * tilt / np.pi
+
+    scale = OUT_EYE_SPACE / abs(eye0[0] - eye1[0])
+    pimgs = pimg.resize((int(iw * scale), int(ih * scale)), resample=PImage.Resampling.LANCZOS)
+
+    # rotate around nose
+    new_mid = [int(c * scale) for c in mid]
+    crop_box = (new_mid[0] - (OUT_W // 2),
+                new_mid[1] - OUT_NOSE_TOP,
+                new_mid[0] + (OUT_W // 2),
+                new_mid[1] + (OUT_H - OUT_NOSE_TOP))
+
+    img_out = pimgs.rotate(tilt_deg, center=new_mid, resample=PImage.Resampling.BICUBIC).crop(crop_box)
+    out_images.append(gr.Image(img_out, visible=True))
+
+  out_images += out_pad
+  return out_images[:NUM_OUTS]
+
+
+with gr.Blocks() as demo:
+  gr.Markdown("""
+              # 9103H 2024F Face Alignment Tool.
+              ## Interface for face detection, alignment, cropping\
+              to help create dataset for [HW10](https://github.com/DM-GY-9103-2024F-H/HW10).
+              """)
+
+  gr.Interface(
+    face,
+    inputs=gr.Image(type="pil"),
+    outputs=all_outputs,
+    cache_examples=True,
+    examples=[["./imgs/03.webp"], ["./imgs/11.jpg"]],
+    allow_flagging="never",
+  )
+
+if __name__ == "__main__":
+   demo.launch()

requirements.txt

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+opencv-contrib-python
+opencv-python