add HOG feature extraction tutorial

Author: x4nth055

README.md

@@ -53,6 +53,7 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy
         - [How to Perform Malaria Cells Classification using TensorFlow 2 and Keras in Python](https://www.thepythoncode.com/article/malaria-cells-classification). ([code](machine-learning/malaria-classification))
         - [How to Make a Barcode Reader in Python](https://www.thepythoncode.com/article/making-a-barcode-scanner-in-python). ([code](general/barcode-reader))
         - [Image Transformations using OpenCV in Python](https://www.thepythoncode.com/article/image-transformations-using-opencv-in-python). ([code](machine-learning/image-transformation))
+        - [How to Apply HOG Feature Extraction in Python](https://www.thepythoncode.com/article/hog-feature-extraction-in-python). ([code](machine-learning/hog-feature-extraction))
     - [Building a Speech Emotion Recognizer using Scikit-learn](https://www.thepythoncode.com/article/building-a-speech-emotion-recognizer-using-sklearn). ([code](machine-learning/speech-emotion-recognition))
     - [How to Convert Speech to Text in Python](https://www.thepythoncode.com/article/using-speech-recognition-to-convert-speech-to-text-python). ([code](machine-learning/speech-recognition))
     - [Top 8 Python Libraries For Data Scientists and Machine Learning Engineers](https://www.thepythoncode.com/article/top-python-libraries-for-data-scientists).

machine-learning/hog-feature-extraction/README.md

@@ -0,0 +1 @@
+# [How to Apply HOG Feature Extraction in Python](https://www.thepythoncode.com/article/hog-feature-extraction-in-python)

machine-learning/hog-feature-extraction/cat.jpg

@@ -0,0 +1,97 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6-final"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python36664bitea6884f10f474b21a2a2f022451e0d09",
+   "display_name": "Python 3.6.6 64-bit"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#importing required libraries\n",
+    "from skimage.io import imread\n",
+    "from skimage.transform import resize\n",
+    "from skimage.feature import hog\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#reading the image\n",
+    "img = imread('cat.jpg')\n",
+    "plt.axis(\"off\")\n",
+    "plt.imshow(img)\n",
+    "print(img.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#resizing image\n",
+    "resized_img = resize(img, (128*4, 64*4))\n",
+    "plt.axis(\"off\")\n",
+    "plt.imshow(resized_img)\n",
+    "print(resized_img.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#creating hog features\n",
+    "fd, hog_image = hog(resized_img, orientations=9, pixels_per_cell=(8, 8),\n",
+    "                \tcells_per_block=(2, 2), visualize=True, multichannel=True)\n",
+    "print(fd.shape)\n",
+    "plt.axis(\"off\")\n",
+    "plt.imshow(hog_image, cmap=\"gray\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# save the images\n",
+    "plt.imsave(\"resized_img.jpg\", resized_img)\n",
+    "plt.imsave(\"hog_image.jpg\", hog_image, cmap=\"gray\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ]
+}

machine-learning/hog-feature-extraction/hog.ipynb

@@ -0,0 +1,31 @@
+#importing required libraries
+from skimage.io import imread
+from skimage.transform import resize
+from skimage.feature import hog
+import matplotlib.pyplot as plt
+
+#reading the image
+img = imread('cat.jpg')
+plt.axis("off")
+plt.imshow(img)
+print(img.shape)
+
+#resizing image
+resized_img = resize(img, (128*4, 64*4))
+plt.axis("off")
+plt.imshow(resized_img)
+plt.show()
+print(resized_img.shape)
+
+#creating hog features
+fd, hog_image = hog(resized_img, orientations=9, pixels_per_cell=(8, 8),
+                	cells_per_block=(2, 2), visualize=True, multichannel=True)
+print(fd.shape)
+print(hog_image.shape)
+plt.axis("off")
+plt.imshow(hog_image, cmap="gray")
+plt.show()
+
+# save the images
+plt.imsave("resized_img.jpg", resized_img)
+plt.imsave("hog_image.jpg", hog_image, cmap="gray")

machine-learning/hog-feature-extraction/hog.py

@@ -0,0 +1,2 @@
+scikit-image
+matplotlib