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Open-CV-Implementations
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‎README.md‎

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* [Thresholding](https://github.com/altruistcoder/Open-CV-Implementations/blob/master/blurring.ipynb):
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Python Code to perform different types of thresholding such as Simple, Inverse and Adaptive Thresholding on an image using OpenCV.
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* [Gradients and Edge Detection](https://github.com/altruistcoder/Open-CV-Implementations/blob/master/gradients_and_edge_detection.ipynb):
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Python Code to find Gradients of an image using Laplacian and Sobel method and also to perform Canny Edge Detection on an image using OpenCV.
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* [Contours](https://github.com/altruistcoder/Open-CV-Implementations/blob/master/contours.ipynb):
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Python Code to find & draw Contours present in an image using OpenCV.

‎blurring.ipynb‎

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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import cv2"
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]
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},

‎contours.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Finding Contours in an Image"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import cv2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"image = cv2.imread('sample_image.jpg')\n",
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"cv2.imshow(\"Original\", image)\n",
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"cv2.waitKey(0)\n",
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"cv2.destroyAllWindows()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
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"cv2.imshow(\"Grayscaled Image\", gray_image)\n",
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"cv2.waitKey(0)\n",
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"cv2.destroyAllWindows()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"blurred_image = cv2.GaussianBlur(gray_image, (11, 11), 0)\n",
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"cv2.imshow(\"Blurred Image\", blurred_image)\n",
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"cv2.waitKey(0)\n",
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"cv2.destroyAllWindows()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"No. of Contours in the image is 0\n"
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]
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}
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],
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"source": [
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"canny = cv2.Canny(blurred_image, 30, 150)\n",
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"cv2.imshow(\"Canny\", canny)\n",
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"\n",
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"(image_returned, counts, hierarchy) = cv2.findContours(canny.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
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"print(\"No. of Contours in the image is {}\".format(len(counts)))\n",
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"\n",
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"img = gray_image.copy()\n",
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"cv2.drawContours(img, counts, -1, (0, 0, 0), 5)\n",
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"cv2.imshow(\"Image with Contours\", img)\n",
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"\n",
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"cv2.waitKey(0)\n",
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"cv2.destroyAllWindows()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.5.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎gradients_and_edge_detection.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Gradients and Edge Detection"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Gradients of an Image"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import cv2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"image = cv2.imread('sample_image.jpg')\n",
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"cv2.imshow(\"Original\", image)\n",
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"cv2.waitKey(0)\n",
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"cv2.destroyAllWindows()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
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"cv2.imshow(\"Grayscaled Image\", gray_image)\n",
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"cv2.waitKey(0)\n",
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"cv2.destroyAllWindows()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Laplacian Gradient"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"lap_gradient = cv2.Laplacian(gray_image, cv2.CV_64F)\n",
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"lap_gradient = np.uint8(np.absolute(lap_gradient))\n",
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"cv2.imshow(\"Laplacian Gradient\", lap_gradient)\n",
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"cv2.waitKey(0)\n",
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"cv2.destroyAllWindows()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Sobel Gradient"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"sobelX = cv2.Sobel(gray_image, cv2.CV_64F, 1, 0) # For Vertical Edge Features\n",
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"sobelY = cv2.Sobel(gray_image, cv2.CV_64F, 0, 1) # For Horizontal Edge Features\n",
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"sobelX = np.uint8(np.absolute(sobelX))\n",
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"sobelY = np.uint8(np.absolute(sobelY))\n",
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"sobelCombined = cv2.bitwise_or(sobelX, sobelY)\n",
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"cv2.imshow(\"SobelX Gradient\", sobelX)\n",
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"cv2.imshow(\"SobelY Gradient\", sobelY)\n",
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"cv2.imshow(\"Sobel Combined Gradient\", sobelCombined)\n",
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"cv2.waitKey(0)\n",
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"cv2.destroyAllWindows()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Canny Edge Detection"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"blurred_image = cv2.GaussianBlur(gray_image, (5, 5), 0)\n",
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"cv2.imshow(\"Blurred Image\", blurred_image)\n",
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"\n",
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"canny_edge_image = cv2.Canny(blurred_image, 30, 150)\n",
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"cv2.imshow(\"Canny Edge Detection\", canny_edge_image)\n",
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"\n",
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"cv2.waitKey(0)\n",
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"cv2.destroyAllWindows()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.5.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎thresholding.ipynb‎

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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import cv2"
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]
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},

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