알고리즘 완성본

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Lab_b[skin] 29.32\n",
+      "skin 의 warm 기준값과의 거리\n",
+      "8.702000000000005\n",
+      "skin 의 cool 기준값과의 거리\n",
+      "12.32\n",
+      "color_data\\warm_spring.png의 퍼스널 컬러는 웜톤(warm)입니다.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 평균 색상 추출\n",
+    "import cv2\n",
+    "import numpy as np\n",
+    "from sklearn.cluster import KMeans\n",
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "from skimage import io\n",
+    "from itertools import compress\n",
+    "\n",
+    "# tone 구별 \n",
+    "from scipy.spatial import distance\n",
+    "import copy\n",
+    "import math\n",
+    "import operator\n",
+    "\n",
+    "# main함수\n",
+    "from colormath.color_objects import LabColor, sRGBColor, HSVColor\n",
+    "from colormath.color_conversions import convert_color\n",
+    "\n",
+    "# color extract 클래스\n",
+    "class DominantColors:\n",
+    "\n",
+    "    CLUSTERS = None\n",
+    "    IMAGE = None\n",
+    "    COLORS = None\n",
+    "    LABELS = None\n",
+    "\n",
+    "    def __init__(self, image, clusters=3):\n",
+    "        self.CLUSTERS = clusters\n",
+    "        img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
+    "        self.IMAGE = img.reshape((img.shape[0] * img.shape[1], 3))\n",
+    "\n",
+    "        #using k-means to cluster pixels\n",
+    "        kmeans = KMeans(n_clusters = self.CLUSTERS)\n",
+    "        kmeans.fit(self.IMAGE)\n",
+    "\n",
+    "        #the cluster centers are our dominant colors.\n",
+    "        self.COLORS = kmeans.cluster_centers_\n",
+    "        self.LABELS = kmeans.labels_\n",
+    "\n",
+    "    def rgb_to_hex(self, rgb):\n",
+    "        return '#%02x%02x%02x' % (int(rgb[0]), int(rgb[1]), int(rgb[2]))\n",
+    "\n",
+    "    # Return a list in order of color that appeared most often.\n",
+    "    def getHistogram(self):\n",
+    "        numLabels = np.arange(0, self.CLUSTERS+1)\n",
+    "        #create frequency count tables\n",
+    "        (hist, _) = np.histogram(self.LABELS, bins = numLabels)\n",
+    "        hist = hist.astype(\"float\")\n",
+    "        hist /= hist.sum()\n",
+    "\n",
+    "        colors = self.COLORS\n",
+    "        #descending order sorting as per frequency count\n",
+    "        colors = colors[(-hist).argsort()]\n",
+    "        hist = hist[(-hist).argsort()]\n",
+    "        for i in range(self.CLUSTERS):\n",
+    "            colors[i] = colors[i].astype(int)\n",
+    "        # Blue mask 제거\n",
+    "        fil = [colors[i][2] < 250 and colors[i][0] > 10 for i in range(self.CLUSTERS)]\n",
+    "        colors = list(compress(colors, fil))\n",
+    "        return colors, hist\n",
+    "\n",
+    "    def plotHistogram(self):\n",
+    "        colors, hist = self.getHistogram()\n",
+    "        #creating empty chart\n",
+    "        chart = np.zeros((50, 500, 3), np.uint8)\n",
+    "        start = 0\n",
+    "\n",
+    "        #creating color rectangles\n",
+    "        for i in range(len(colors)):\n",
+    "            end = start + hist[i] * 500\n",
+    "            r,g,b = colors[i]\n",
+    "            #using cv2.rectangle to plot colors\n",
+    "            cv2.rectangle(chart, (int(start), 0), (int(end), 50), (r,g,b), -1)\n",
+    "            start = end\n",
+    "\n",
+    "        #display chart\n",
+    "        plt.figure()\n",
+    "        plt.axis(\"off\")\n",
+    "        plt.imshow(chart)\n",
+    "        plt.show()\n",
+    "\n",
+    "        return colors\n",
+    "    \n",
+    "   \n",
+    " #tone analysis 함수\n",
+    "def is_warm(lab_b, a):\n",
+    "    '''\n",
+    "    파라미터 lab_b = [skin_b, hair_b, eye_b]\n",
+    "    a = 가중치 [skin, hair, eye]\n",
+    "    질의색상 lab_b값에서 warm의 lab_b, cool의 lab_b값 간의 거리를\n",
+    "    각각 계산하여 warm이 가까우면 1, 반대 경우 0 리턴\n",
+    "    '''\n",
+    "    # standard of skin, eyebrow, eye (눈썹, 눈동자는 0으로) \n",
+    "    warm_b_std = [38.022000000000006, 0, 0]\n",
+    "    cool_b_std = [17, 0, 0]\n",
+    "\n",
+    "    warm_dist = 0\n",
+    "    cool_dist = 0\n",
+    "\n",
+    "    body_part = ['skin', 'eyebrow', 'eye']\n",
+    "    for i in range(1):\n",
+    "        warm_dist += abs(lab_b[i] - warm_b_std[i]) * a[i]\n",
+    "        \n",
+    "        print(body_part[i],\"의 warm 기준값과의 거리\")\n",
+    "        print(abs(lab_b[i] - warm_b_std[i]))\n",
+    "        \n",
+    "        cool_dist += abs(lab_b[i] - cool_b_std[i]) * a[i]\n",
+    "        \n",
+    "        print(body_part[i],\"의 cool 기준값과의 거리\")\n",
+    "        print(abs(lab_b[i] - cool_b_std[i]))\n",
+    "        \n",
+    "    if(warm_dist <= cool_dist):\n",
+    "        return 1 #warm\n",
+    "    else:\n",
+    "        return 0 #cool\n",
+    "\n",
+    "# 이미지 자르는 함수\n",
+    "def trimming (img): \n",
+    "    x = 100; \n",
+    "    y = 100; \n",
+    "    w = 100; \n",
+    "    h = 100; \n",
+    "    \n",
+    "    img_trim = img[y:y+h, x:x+w] \n",
+    "    return img_trim \n",
+    "\n",
+    "\n",
+    "# 원래 main\n",
+    "def analysis(imgpath):\n",
+    "    #######################################\n",
+    "    #           Face detection            #\n",
+    "    #######################################\n",
+    "    img=cv2.imread(imgpath)\n",
+    "    \n",
+    "    h,w,c=img.shape\n",
+    "    if((h>500) and (w>500)):\n",
+    "        img = trimming(img) # 이미지가 너무 크면 잘라서 확인\n",
+    "    \n",
+    "    face = [img, img,\n",
+    "            img, img,\n",
+    "            img, img]\n",
+    "\n",
+    "    \n",
+    "    #######################################\n",
+    "    #         Get Dominant Colors         #\n",
+    "    #######################################\n",
+    "    temp = []\n",
+    "    clusters = 4\n",
+    "    for f in face:\n",
+    "        dc = DominantColors(f, clusters)\n",
+    "        face_part_color, _ = dc.getHistogram()\n",
+    "        #dc.plotHistogram()\n",
+    "        temp.append(np.array(face_part_color[0]))\n",
+    "    cheek1 = np.mean([temp[0], temp[1]], axis=0)\n",
+    "    cheek2 = np.mean([temp[2], temp[3]], axis=0)\n",
+    "    cheek3 = np.mean([temp[4], temp[5]], axis=0)\n",
+    "\n",
+    "    Lab_b, hsv_s = [], []\n",
+    "    color = [cheek1, cheek2, cheek3]\n",
+    "    for i in range(3):\n",
+    "        rgb = sRGBColor(color[i][0], color[i][1], color[i][2], is_upscaled=True)\n",
+    "        lab = convert_color(rgb, LabColor, through_rgb_type=sRGBColor)\n",
+    "        hsv = convert_color(rgb, HSVColor, through_rgb_type=sRGBColor)\n",
+    "        Lab_b.append(float(format(lab.lab_b,\".2f\")))\n",
+    "        hsv_s.append(float(format(hsv.hsv_s,\".2f\"))*100)\n",
+    "\n",
+    "    Lab_b[1]=0\n",
+    "    Lab_b[2]=0\n",
+    "    \n",
+    "    print('Lab_b[skin]',Lab_b[0])\n",
+    "\n",
+    "    #######################################\n",
+    "    #      Personal color Analysis        #\n",
+    "    #######################################\n",
+    "    Lab_weight = [100, 0, 0]\n",
+    "    hsv_weight = [10, 0, 0]\n",
+    "    \n",
+    "    if(is_warm(Lab_b, Lab_weight)):\n",
+    "        tone = '웜톤(warm)'\n",
+    "    else:\n",
+    "        tone = '쿨톤(cool)'\n",
+    "        \n",
+    "    # Print Result\n",
+    "    print('{}의 퍼스널 컬러는 {}입니다.'.format(imgpath, tone))\n",
+    "\n",
+    "    \n",
+    "def main():    \n",
+    "    analysis('color_data\\warm_spring.png')\n",
+    "    \n",
+    "    \n",
+    "if __name__ == '__main__':\n",
+    "    main()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.6.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

@@ -15,8 +15,9 @@ class DetectFace:
 
 
         #face detection part
         #face detection part
         self.img = cv2.imread(image)
         self.img = cv2.imread(image)
-        #if self.img.shape[0]>500:
-        #    self.img = cv2.resize(self.img, dsize=(0,0), fx=0.8, fy=0.8)
+        
+        if self.img.shape[0]>500:
+            self.img = cv2.resize(self.img, dsize=(0,0), fx=0.8, fy=0.8)
 
 
         # init face parts
         # init face parts
         self.right_eyebrow = []
         self.right_eyebrow = []
@@ -32,7 +33,7 @@ class DetectFace:
 
 
     # return type : np.array
     # return type : np.array
     def detect_face_part(self):
     def detect_face_part(self):
-        face_parts = [[],[],[],[],[],[],[]]
+        face_parts = [[],[],[],[],[],[], []]
         # detect faces in the grayscale image
         # detect faces in the grayscale image
         rect = self.detector(cv2.cvtColor(self.img, cv2.COLOR_BGR2GRAY), 1)[0]
         rect = self.detector(cv2.cvtColor(self.img, cv2.COLOR_BGR2GRAY), 1)[0]