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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import cv2\n",
"from anime_face_detector import create_detector\n",
"from PIL import Image, ImageDraw, ImageFont\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import math"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def get_deg(arr):\n",
" rad = math.atan2(arr[3]-arr[1],arr[2]-arr[0])\n",
" PI = math.pi\n",
" deg = (rad*180)/PI\n",
" return deg"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"detector = create_detector('yolov3', device='cpu')"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [],
"source": [
"# gg = Image.open('gg.png')\n",
"# ggdraw = ImageDraw.Draw(gg)\n",
"# ggdraw.rectangle((5,5,gg.width-5,gg.height-5), outline=(255, 0,0), width=5)\n",
"gg = cv2.imread('gg.png', cv2.IMREAD_UNCHANGED)\n",
"gg = cv2.cvtColor(gg, cv2.COLOR_BGRA2RGBA)"
]
},
{
"cell_type": "code",
"execution_count": 168,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(604, 1074, 3)\n"
]
}
],
"source": [
"img = cv2.imread('test.png')\n",
"preds = detector(img)\n",
"img = cv2.cvtColor(img, cv2.COLOR_BGRA2RGB)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"# image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))\n",
"# draw = ImageDraw.Draw(image)\n",
"\n",
"# for face in preds:\n",
"# draw.rectangle((face['bbox'][0], face['bbox'][1], face['bbox'][2], face['bbox'][3]), outline=(255, 0, 0), width=5)\n",
"# x = face['bbox'][0]\n",
"# y = face['bbox'][1]\n",
"# for i, point in enumerate(face['keypoints']):\n",
"# # draw.ellipse((point[0]-2, point[1]-2, point[0]+2, point[1]+2), fill=(255, 0, 0))\n",
"# draw.text((point[0], point[1]), str(i), font=ImageFont.truetype('arial.ttf', 10), fill=(255, 0, 0))\n",
"\n",
"for face in preds:\n",
" points = face['keypoints']\n",
" color = img[int(points[27][1]), int(points[27][0])+10]\n",
" polygon = np.array([\n",
" [points[0][0], points[0][1]],\n",
" [points[1][0], points[1][1]],\n",
" [points[2][0], points[2][1]],\n",
" [points[3][0], points[4][1]],\n",
" [points[4][0], points[4][1]],\n",
" [points[10][0], points[10][1]],\n",
" [points[9][0], points[9][1]],\n",
" [points[8][0], points[8][1]],\n",
" [points[7][0], points[7][1]],\n",
" [points[6][0], points[6][1]],\n",
" [points[5][0], points[5][1]]\n",
" ], np.int32)\n",
" cv2.fillConvexPoly(img, polygon, color=(int(color[0]), int(color[1]), int(color[2]), 255))\n",
" deg = get_deg([points[0][0], points[0][1], points[4][0], points[4][1]])\n",
" rotated = gg.copy()\n",
" resize = math.sqrt((points[10][0] - points[5][0])**2 + (points[10][1] - points[5][1])**2)\n",
" rotated = cv2.resize(rotated, (int(resize), int(resize*1.12)))\n",
" matrix = cv2.getPerspectiveTransform(\n",
" np.float32([[0, 0], [rotated.shape[0],0], [0, rotated.shape[1]], [rotated.shape[0],rotated.shape[1]]]),\n",
" np.float32([[points[5][0], points[5][1]], [points[10][0], points[10][1]], [points[1][0], points[1][1]], [points[3][0], points[3][1]]]))\n",
" rotated = cv2.warpPerspective(rotated, matrix, (img.shape[1], img.shape[0]))\n",
"\n",
" alpha = rotated[:, :, 3] / 255.\n",
" for i in range(3):\n",
" w, h = img.shape[:2]\n",
" rw, rh = rotated.shape[:2]\n",
" pointx, pointy = points[5][:2]\n",
" pointx, pointy = int(pointx), int(pointy)\n",
" img[:, :, i] = (1. - alpha) * img[0:, 0:, i] + alpha * rotated[:, :, i]\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.imshow(np.asarray(img))\n",
"cv2.imwrite('result.png', cv2.cvtColor(img, cv2.COLOR_RGBA2BGR))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
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"language": "python",
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"language_info": {
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"file_extension": ".py",
"mimetype": "text/x-python",
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"nbconvert_exporter": "python",
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"orig_nbformat": 4,
"vscode": {
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