PS|Added notebooks

notebooks/facial_age_gender.ipynb

@@ -0,0 +1,342 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cv2\n",
+    "import dlib\n",
+    "import os\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/Users/pareshar/Personal/Github/Facial-feature-detector'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "current_dir = os.getcwd()\n",
+    "parent_dir = os.path.dirname(current_dir)\n",
+    "os.chdir(parent_dir)\n",
+    "os.getcwd()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# static variables\n",
+    "path_to_images = \"data/images_age_gender/\"\n",
+    "image_files = os.listdir(path_to_images)\n",
+    "face_detector_weights = \"models/face_detection/res10_300x300_ssd_iter_140000.caffemodel\"\n",
+    "face_detector_config = \"models/face_detection/deploy.prototxt.txt\"\n",
+    "age_weights = \"models/face_age/age_net.caffemodel\"\n",
+    "age_config = \"models/face_age/age_deploy.prototxt\"\n",
+    "gender_weights = \"models/face_gender/gender_net.caffemodel\"\n",
+    "gender_config = \"models/face_gender/gender_deploy.prototxt\"\n",
+    "age_list = ['(0-2)', '(4-6)', '(8-12)', '(15-20)', '(25-32)', '(38-43)', '(48-53)', '(60-100)']\n",
+    "gender_list = ['Male', 'Female']\n",
+    "model_mean = (78.4263377603, 87.7689143744, 114.895847746) # taken from paper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(columns=[\"file_name\", \"model\", \"confidence_face_detected\", \"age_range\", \"age_confidence\", \"gender\", \"gender_confidence\"])\n",
+    "df_list = []\n",
+    "\n",
+    "\n",
+    "for image_file in image_files:\n",
+    "    image = cv2.imread(path_to_images + image_file)\n",
+    "    h, w = image.shape[:2]\n",
+    "    blob = cv2.dnn.blobFromImage(image=image, scalefactor=1.0, size=(300, 300))\n",
+    "    \n",
+    "    face_detector_net = cv2.dnn.readNetFromCaffe(face_detector_config, face_detector_weights)\n",
+    "    face_detector_net.setInput(blob)\n",
+    "    face_detections = face_detector_net.forward()  \n",
+    "    age_net = cv2.dnn.readNet(age_weights, age_config)\n",
+    "    gender_net = cv2.dnn.readNet(gender_weights, gender_config)\n",
+    "    \n",
+    "    d = None\n",
+    "    \n",
+    "    for i in range(0, face_detections.shape[2]):\n",
+    "        confidence = face_detections[0, 0, i, 2]\n",
+    "        if confidence > 0.97:\n",
+    "            box = face_detections[0, 0, i, 3:7] * np.array([w, h, w, h])\n",
+    "            (startX, startY, endX, endY) = box.astype(\"int\")\n",
+    "            face = image[startY:endY, startX:endX]\n",
+    "            \n",
+    "            blob = cv2.dnn.blobFromImage(face, 1.0, (227, 227), model_mean, swapRB=False)\n",
+    "            \n",
+    "            age_net.setInput(blob)\n",
+    "            age_preds = age_net.forward()\n",
+    "            i = age_preds[0].argmax()\n",
+    "            age = age_list[i]\n",
+    "            age_confidence_score = age_preds[0][i]\n",
+    "            \n",
+    "            gender_net.setInput(blob)\n",
+    "            gender_preds = gender_net.forward()\n",
+    "            i = gender_preds[0].argmax()\n",
+    "            gender = gender_list[i]\n",
+    "            gender_confidence_score = gender_preds[0][i]\n",
+    "            \n",
+    "            # plt.imshow(face)\n",
+    "            # plt.show()  \n",
+    "            \n",
+    "            d = {\n",
+    "            \"file_name\": image_file,\n",
+    "            \"model\": \"ageNet\",\n",
+    "            \"confidence_face_detected\": confidence,\n",
+    "            \"age_range\": age,\n",
+    "            \"age_confidence\": age_confidence_score,\n",
+    "            \"gender\": gender,\n",
+    "            \"gender_confidence\": gender_confidence_score \n",
+    "            }\n",
+    "            df_list.append(d)\n",
+    "            break\n",
+    "        \n",
+    "    if d is None or image_file != d[\"file_name\"]:\n",
+    "            \n",
+    "        d = {\n",
+    "            \"file_name\": image_file,\n",
+    "            \"model\": \"ageNet\",\n",
+    "            \"confidence_face_detected\": confidence,\n",
+    "            \"age_range\": \"NA\",\n",
+    "            \"age_confidence\": \"NA\",\n",
+    "            \"gender\": \"NA\",\n",
+    "            \"gender_confidence\": \"NA\" \n",
+    "            }\n",
+    "                \n",
+    "        df_list.append(d)\n",
+    "    \n",
+    "df = pd.concat([df, pd.DataFrame(df_list)], ignore_index=True).round(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>file_name</th>\n",
+       "      <th>model</th>\n",
+       "      <th>confidence_face_detected</th>\n",
+       "      <th>age_range</th>\n",
+       "      <th>age_confidence</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>gender_confidence</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>22_me.jpg</td>\n",
+       "      <td>ageNet</td>\n",
+       "      <td>0.98</td>\n",
+       "      <td>(25-32)</td>\n",
+       "      <td>0.67165</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>25_32_woman.jpg</td>\n",
+       "      <td>ageNet</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>(25-32)</td>\n",
+       "      <td>0.859894</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0.952863</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>38_43_man.jpg</td>\n",
+       "      <td>ageNet</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>(25-32)</td>\n",
+       "      <td>0.681306</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0.999431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>38_43_woman.jpg</td>\n",
+       "      <td>ageNet</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>(48-53)</td>\n",
+       "      <td>0.886763</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0.998737</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>4_6_boy.jpg</td>\n",
+       "      <td>ageNet</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>(4-6)</td>\n",
+       "      <td>0.639939</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0.999049</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4_6_girl.jpg</td>\n",
+       "      <td>ageNet</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>(4-6)</td>\n",
+       "      <td>0.319971</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0.998801</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>60_100_man.jpg</td>\n",
+       "      <td>ageNet</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>(4-6)</td>\n",
+       "      <td>0.548595</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0.999973</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>60_100_woman.jpg</td>\n",
+       "      <td>ageNet</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>(60-100)</td>\n",
+       "      <td>0.332936</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0.984078</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>60_100_woman_2.jpg</td>\n",
+       "      <td>ageNet</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>(38-43)</td>\n",
+       "      <td>0.414388</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0.518144</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>minion.jpg</td>\n",
+       "      <td>ageNet</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>NA</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            file_name   model  confidence_face_detected age_range  \\\n",
+       "1           22_me.jpg  ageNet                      0.98   (25-32)   \n",
+       "3     25_32_woman.jpg  ageNet                      1.00   (25-32)   \n",
+       "2       38_43_man.jpg  ageNet                      1.00   (25-32)   \n",
+       "8     38_43_woman.jpg  ageNet                      0.99   (48-53)   \n",
+       "7         4_6_boy.jpg  ageNet                      0.99     (4-6)   \n",
+       "4        4_6_girl.jpg  ageNet                      0.99     (4-6)   \n",
+       "6      60_100_man.jpg  ageNet                      0.99     (4-6)   \n",
+       "5    60_100_woman.jpg  ageNet                      1.00  (60-100)   \n",
+       "9  60_100_woman_2.jpg  ageNet                      1.00   (38-43)   \n",
+       "0          minion.jpg  ageNet                      0.00        NA   \n",
+       "\n",
+       "  age_confidence  gender gender_confidence  \n",
+       "1        0.67165    Male               1.0  \n",
+       "3       0.859894  Female          0.952863  \n",
+       "2       0.681306    Male          0.999431  \n",
+       "8       0.886763  Female          0.998737  \n",
+       "7       0.639939    Male          0.999049  \n",
+       "4       0.319971  Female          0.998801  \n",
+       "6       0.548595    Male          0.999973  \n",
+       "5       0.332936  Female          0.984078  \n",
+       "9       0.414388    Male          0.518144  \n",
+       "0             NA      NA                NA  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.sort_values(\"file_name\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Other\n",
+    "- Dataset used to train model: https://talhassner.github.io/home/projects/Adience/Adience-data.html#agegender"
+   ]
+  }
+ ],
+ "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": "3.8.10"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

requirements.txt

@@ -3,4 +3,5 @@ numpy==1.23.5
 scikit-image==0.21.0
 dlib==19.24.2
 imutils==0.5.4
-pillow==9.4.0
+pillow==9.4.0
+pyyaml==6.0

utils/face_symmetry.py

@@ -0,0 +1,5 @@
+#TODO: create YAML file to point towards static parameters
+#TODO: Test main output and app
+#TODO: Consider using other method for face detector - this one not as reliable
+#TODO: Text output showing other examples - celeb, child, gender
+#TODO: Move notebooks here

utils/face_texture.py

@@ -10,19 +10,12 @@ from utils.cv_utils import get_image
 from typing import Tuple
 
 
-#TODO: face texture class - face detector and output face
-#TODO: create YAML file to point towards static parameters
-#TODO: Test main output and app
-#TODO: Consider using other method for face detector - this one not as reliable
-#TODO: Text output showing other examples - celeb, child, gender
-
-
 class GetFaceTexture:
     def __init__(self) -> None:
         pass
     
     def preprocess_image(self, image) -> np.array:
-        image = imutils.resize(image, width=800)
+        image = imutils.resize(image, width=400)
         gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
         return gray_image