add image transformations tutorial

Author: x4nth055

README.md

@@ -52,6 +52,7 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy
         - [Skin Cancer Detection using TensorFlow in Python](https://www.thepythoncode.com/article/skin-cancer-detection-using-tensorflow-in-python). ([code](machine-learning/skin-cancer-detection))
         - [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))
     - [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/image-transformation/README.md

@@ -0,0 +1,3 @@
+# [Image Transformations using OpenCV in Python](https://www.thepythoncode.com/article/image-transformations-using-opencv-in-python)
+To run all the scripts, you need to install the requirements:
+- `pip3 install -r requirements.txt`

machine-learning/image-transformation/city.jpg

@@ -0,0 +1,24 @@
+import numpy as np
+import cv2
+import matplotlib.pyplot as plt
+
+# read the input image
+img = cv2.imread("city.jpg")
+# convert from BGR to RGB so we can plot using matplotlib
+img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+# disable x & y axis
+plt.axis('off')
+# show the image
+plt.imshow(img)
+plt.show()
+
+# get 200 pixels from 100 to 300 on both x-axis & y-axis
+# change that if you will, just make sure you don't exceed cols & rows
+cropped_img = img[100:300, 100:300]
+# disable x & y axis
+plt.axis('off')
+# show the resulting image
+plt.imshow(cropped_img)
+plt.show()
+# save the resulting image to disk
+plt.imsave("city_cropped.jpg", cropped_img)

machine-learning/image-transformation/cropping.py

@@ -0,0 +1,34 @@
+import numpy as np
+import cv2
+import matplotlib.pyplot as plt
+
+# read the input image
+img = cv2.imread("city.jpg")
+# convert from BGR to RGB so we can plot using matplotlib
+img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+# disable x & y axis
+plt.axis('off')
+# show the image
+plt.imshow(img)
+plt.show()
+
+# get the image shape
+rows, cols, dim = img.shape
+
+# transformation matrix for x-axis reflection 
+M = np.float32([[1,  0, 0   ],
+                [0, -1, rows],
+                [0,  0, 1   ]])
+# transformation matrix for y-axis reflection
+# M = np.float32([[-1, 0, cols],
+#                 [ 0, 1, 0   ],
+#                 [ 0, 0, 1   ]])
+# apply a perspective transformation to the image
+reflected_img = cv2.warpPerspective(img,M,(int(cols),int(rows)))
+# disable x & y axis
+plt.axis('off')
+# show the resulting image
+plt.imshow(reflected_img)
+plt.show()
+# save the resulting image to disk
+plt.imsave("city_reflected.jpg", reflected_img)

machine-learning/image-transformation/reflection.py

@@ -0,0 +1,3 @@
+matplotlib
+opencv-python
+numpy

machine-learning/image-transformation/requirements.txt

@@ -0,0 +1,32 @@
+import numpy as np
+import cv2
+import matplotlib.pyplot as plt
+
+# read the input image
+img = cv2.imread("city.jpg")
+# convert from BGR to RGB so we can plot using matplotlib
+img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+# disable x & y axis
+plt.axis('off')
+# show the image
+plt.imshow(img)
+plt.show()
+
+# get the image shape
+rows, cols, dim = img.shape
+
+#angle from degree to radian
+angle = np.radians(10)
+#transformation matrix for Rotation
+M = np.float32([[np.cos(angle), -(np.sin(angle)), 0],
+            	[np.sin(angle), np.cos(angle), 0],
+            	[0, 0, 1]])
+# apply a perspective transformation to the image
+rotated_img = cv2.warpPerspective(img, M, (int(cols),int(rows)))
+# disable x & y axis
+plt.axis('off')
+# show the resulting image
+plt.imshow(rotated_img)
+plt.show()
+# save the resulting image to disk
+plt.imsave("city_rotated.jpg", rotated_img)

machine-learning/image-transformation/rotation.py

@@ -0,0 +1,30 @@
+import numpy as np
+import cv2
+import matplotlib.pyplot as plt
+
+# read the input image
+img = cv2.imread("city.jpg")
+# convert from BGR to RGB so we can plot using matplotlib
+img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+# disable x & y axis
+plt.axis('off')
+# show the image
+plt.imshow(img)
+plt.show()
+
+# get the image shape
+rows, cols, dim = img.shape
+
+#transformation matrix for Scaling
+M = np.float32([[1.5, 0  , 0],
+            	[0,   1.8, 0],
+            	[0,   0,   1]])
+# apply a perspective transformation to the image
+scaled_img = cv2.warpPerspective(img,M,(cols*2,rows*2))
+# disable x & y axis
+plt.axis('off')
+# show the resulting image
+plt.imshow(scaled_img)
+plt.show()
+# save the resulting image to disk
+plt.imsave("city_scaled.jpg", scaled_img)

machine-learning/image-transformation/scaling.py

@@ -0,0 +1,37 @@
+import numpy as np
+import cv2
+import matplotlib.pyplot as plt
+
+# read the input image
+img = cv2.imread("city.jpg")
+# convert from BGR to RGB so we can plot using matplotlib
+img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+# disable x & y axis
+plt.axis('off')
+# show the image
+plt.imshow(img)
+plt.show()
+
+# get the image shape
+rows, cols, dim = img.shape
+
+# transformation matrix for Shearing
+# shearing applied to x-axis
+M = np.float32([[1, 0.5, 0],
+             	[0, 1  , 0],
+            	[0, 0  , 1]])
+# shearing applied to y-axis
+# M = np.float32([[1,   0, 0],
+#             	  [0.5, 1, 0],
+#             	  [0,   0, 1]])
+
+
+# apply a perspective transformation to the image                
+sheared_img = cv2.warpPerspective(img,M,(int(cols*1.5),int(rows*1.5)))
+# disable x & y axis
+plt.axis('off')
+# show the resulting image
+plt.imshow(sheared_img)
+plt.show()
+# save the resulting image to disk
+plt.imsave("city_sheared.jpg", sheared_img)

machine-learning/image-transformation/shearing.py

@@ -0,0 +1,30 @@
+import numpy as np
+import cv2
+import matplotlib.pyplot as plt
+
+# read the input image
+img = cv2.imread("city.jpg")
+# convert from BGR to RGB so we can plot using matplotlib
+img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+# disable x & y axis
+plt.axis('off')
+# show the image
+plt.imshow(img)
+plt.show()
+
+# get the image shape
+rows, cols, dim = img.shape
+
+# transformation matrix for translation
+M = np.float32([[1, 0, 50],
+                [0, 1, 50],
+                [0, 0, 1]])
+# apply a perspective transformation to the image
+translated_img = cv2.warpPerspective(img, M, (cols, rows))
+# disable x & y axis
+plt.axis('off')
+# show the resulting image
+plt.imshow(translated_img)
+plt.show()
+# save the resulting image to disk
+plt.imsave("city_translated.jpg", translated_img)