reconstruct the repo. and add some new code files

Author: manthan89-py

Readme.md

@@ -1,3 +1,7 @@
+<p align="center">
+    <img src="test_images/opencv-logo.png" height=450 width=350>
+</p>
+
 # OpenCV Tutorials⭐:
 
 ## Follow Tutorial Files in Below Sequence⭐:
@@ -15,6 +19,9 @@
 * simple_thresholding_matplotlib_with_python.py
 * morphological_transformation_opencv_python.py
 * smoothing_images_and_blurring_images.py
+* image_gradients.py
+* canny_edge_detection.py
+* image_pyramids_opencv.py
 
 ## Helpful Documentations⭐:
 * OpenCV : https://docs.opencv.org/master/

adaptive_thresholding_opencv.py

@@ -1,7 +1,7 @@
 import cv2
 import numpy as np
 
-img = cv2.imread('sudoku.png' , 0)
+img = cv2.imread('test_images/sudoku.png' , 0)
 
 cv2.imshow('Main Image' , img)
 

basic_operations_opencv.py

@@ -1,8 +1,8 @@
 import numpy as np
 import cv2
 
-img = cv2.imread('messi5.jpg')
-img2 = cv2.imread('opencv-logo.png')
+img = cv2.imread('test_images/messi5.jpg')
+img2 = cv2.imread('test_images/opencv-logo.png')
 
 print(img.shape) # row , column , channles
 print(img.size) # total number of pixels

bitwise_operation_opencv.py

@@ -4,7 +4,7 @@
 # height width channls
 img1 = np.zeros((250,500,3) , np.uint8)
 img1 = cv2.rectangle(img1,(200,0),(300,100) , (255,255,255) , -1)
-img2 = cv2.imread('messi5.jpg')
+img2 = cv2.imread('test_images/messi5.jpg')
 
 cv2.imshow('Main image1' , img1)
 cv2.imshow('Main image2' , img2)

canny_edge_detection.py

@@ -0,0 +1,56 @@
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+
+img = cv2.imread('test_images/smarties.png', 0)
+
+## Canny Edge Detection : It is multi-stage algorithm useful for wide range of edge detection.
+## Total 5 Steps : 
+# (1) Noise Reduction
+# (2) Gradient Calculation
+# (3) Non-maximum supression
+# (4) Double threshold
+# (5) Edge Tracking by hysteresis
+## All things will be done by using one simple function frmo OpenCV
+
+
+### Using Matplotlib ###
+
+canny = cv2.Canny(img ,100 ,200)
+
+
+titles = ['Main image', 'Canny']
+images = [img , canny]
+
+
+for i in range(len(images)):
+    plt.subplot(1,2,i+1)
+    plt.imshow(images[i] , 'gray')
+    plt.title(titles[i])
+    plt.xticks([]) , plt.yticks([])
+    
+plt.show()
+
+
+### Using Trackbar ###
+
+# def nothing(x):
+#     pass
+# cv2.namedWindow('threshold meter')
+# cv2.createTrackbar('th1' , 'threshold meter' , 0 ,255 , nothing)
+# cv2.createTrackbar('th2' , 'threshold meter', 0 , 255 , nothing)
+
+# cv2.imshow('Image' , img)
+
+# while True:
+#     th1 = cv2.getTrackbarPos('th1','threshold meter')
+#     th2 = cv2.getTrackbarPos('th2','threshold meter')
+#     canny = cv2.Canny(img , th1 , th2 )
+#     cv2.imshow('canny' , canny)
+
+#     key = cv2.waitKey(1)
+#     if key == 27:
+#         break
+    
+
+# cv2.destroyAllWindows()

draw_shapes.py

@@ -1,6 +1,6 @@
 import cv2
 import numpy as np
-# img = cv2.imread('lena.jpg', 1)
+# img = cv2.imread('test_images/lena.jpg', 1)
 
 # img with numpy
 img = np.zeros([600,600,3] , np.uint8 )

first_tut.py

@@ -1,6 +1,6 @@
 import cv2
 
-# img = cv2.imread('lena.jpg',-1)
+# img = cv2.imread('test_images/lena.jpg',-1)
 
 # cv2.imshow('image',img)
 
@@ -9,7 +9,7 @@
 # if k == 27:
 #     cv2.destroyAllWindows()
 # elif k == ord('s'):
-#     cv2.imwrite('lena_copy_2.png' , img)
+#     cv2.imwrite('test_images/lena_copy_2.png' , img)
 #     cv2.destroyAllWindows()
 
 

image_gradients.py

@@ -0,0 +1,43 @@
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Image Gradient : directional change in intensity or color in image.
+# img = cv2.imread('test_images/messi5.jpg' , cv2.IMREAD_GRAYSCALE)
+img = cv2.imread('test_images/lena.jpg' , cv2.IMREAD_GRAYSCALE)
+
+##### Laplacian Gradient : detect edges in images. ######
+
+# 64 bit float type. It supports negative number
+# negative slop due to induced by transformaing the image white to black.
+# NOTE : kernelsize must be odd
+
+lap = cv2.Laplacian(img,cv2.CV_64F , ksize=1)
+
+# take absolute value and convert it into unsigned interger 8 bit.
+lap = np.uint8(np.absolute(lap))
+
+
+
+                                ##### Sobel Gradient ######
+
+sobelX = cv2.Sobel(img , cv2.CV_64F , 1, 0 )
+sobelY = cv2.Sobel(img , cv2.CV_64F , 0, 1 )
+
+sobelX = np.uint8(np.absolute(sobelX))
+sobelY = np.uint8(np.absolute(sobelY))
+
+sobelCombined = cv2.bitwise_or(sobelX , sobelY)
+
+titles = ['image' , 'Laplacian', 'sobelX' , 'sobelY', 'Sobel Combined']
+images = [img , lap , sobelX , sobelY , sobelCombined]
+
+
+for i in range(len(images)):
+    plt.subplot(2,3, i+1)
+    plt.imshow(images[i] , 'gray')
+    plt.title(titles[i])
+    plt.xticks([]) , plt.yticks([])
+
+
+plt.show()

image_pyramids_opencv.py

@@ -0,0 +1,40 @@
+import cv2
+import numpy as np
+
+## Pyramid : Using Blur and subsample we downscale the image in low size.
+## Use for Reduce the size of images.
+
+img = cv2.imread('test_images/lena.jpg')
+layer = img.copy()
+
+# 1. Gaussian Pyramid 
+
+# pyrDown : reduce the size
+# pyrUp   : increase the size
+
+# applying pyrUp on pyrdowned image will not be same as original.
+# pyrdown image lose data at the time of compression.
+gp = [layer]
+
+for i in range(6):
+    layer = cv2.pyrDown(layer)
+    gp.append(layer)
+    # cv2.imshow(str(i) , layer)
+
+
+# 2. Laplacian Pyramid
+# Difference between that level in gaussian pyramid and expanded version of its upper level
+# in gaussian pyramid
+# Its like edge detection
+
+layer = gp[5]
+cv2.imshow('final upper layer',layer)
+lp = [layer]
+
+for i in range(5,0,-1):
+    gaussian_expaned = cv2.pyrUp(gp[i])
+    laplacian = cv2.subtract(gp[i-1],gaussian_expaned)
+    cv2.imshow(str(i) , laplacian)
+
+cv2.waitKey(0)
+cv2.destroyAllWindows()

matplotlib_with_opencv.py

@@ -2,7 +2,7 @@
 
 from matplotlib import pyplot as plt
 
-img = cv2.imread('lena.jpg' , -1)
+img = cv2.imread('test_images/lena.jpg' , -1)
 
 plt.imshow(img)
 plt.title('BGR Format Image')