find and crop face implementation

main.py

@@ -8,13 +8,17 @@ import numpy as np
 
 from source.cartoonize import Cartoonizer
 
-
 def load_source(filename: str) -> np.ndarray:
   return cv2.imread(filename)[...,::-1]
 
 
 def find_and_crop_face(data: np.ndarray) -> np.ndarray:
-  # TODO
+  data_gray = cv2.cvtColor(data, cv2.COLOR_BGR2GRAY)
+  face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_default.xml')
+  face = face_cascade.detectMultiScale(data_gray, 1.3, 4)
+  face = max(face, key=len)
+  (x, y, w, h) = face
+  face = data[y:y + h, x:x + w]
   return data
 
 
@@ -23,7 +27,7 @@ def compare_with_anime_characters(data: np.ndarray) -> int:
   return 1
 
 
-def transfer_to_anime(ima: np.ndarray):
+def transfer_to_anime(img: np.ndarray):
   algo = Cartoonizer(dataroot='damo/cv_unet_person-image-cartoon_compound-models')
   return algo.cartoonize(img)
 

test.ipynb

@@ -1,76 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of detected faces: 1\n",
-      "face0.jpg is saved\n"
-     ]
-    }
-   ],
-   "source": [
-    "# import required libraries\n",
-    "import cv2\n",
-    "\n",
-    "# read the input image\n",
-    "img = cv2.imread('data/UAM-Andrzej-Wójtowicz.jpg')\n",
-    "\n",
-    "# convert to grayscale of each frames\n",
-    "gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
-    "\n",
-    "# read the haarcascade to detect the faces in an image\n",
-    "face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_default.xml')\n",
-    "\n",
-    "# detects faces in the input image\n",
-    "faces = face_cascade.detectMultiScale(gray, 1.3, 4)\n",
-    "print('Number of detected faces:', len(faces))\n",
-    "\n",
-    "# loop over all detected faces\n",
-    "if len(faces) > 0:\n",
-    "   for i, (x, y, w, h) in enumerate(faces):\n",
-    "      # To draw a rectangle in a face\n",
-    "      cv2.rectangle(img, (x, y), (x + w, y + h), (255, 255, 255), 2)\n",
-    "      face = img[y:y + h, x:x + w]\n",
-    "      cv2.imshow(\"Cropped Face\", face)\n",
-    "      cv2.imwrite(f'face{i}.jpg', face)\n",
-    "      print(f\"face{i}.jpg is saved\")\n",
-    " \n",
-    "# display the image with detected faces\n",
-    "cv2.imshow(\"image\", img)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.10.8 64-bit",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.8"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "767d51c1340bd893661ea55ea3124f6de3c7a262a8b4abca0554b478b1e2ff90"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}