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Commitf8817ad

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added kmeans image segmentation tutorial
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‎README.md

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@@ -31,6 +31,7 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy
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-[How to Detect Shapes in Images in Python](https://www.thepythoncode.com/article/detect-shapes-hough-transform-opencv-python). ([code](machine-learning/shape-detection))
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-[How to Detect Contours in Images using OpenCV in Python](https://www.thepythoncode.com/article/contour-detection-opencv-python). ([code](machine-learning/contour-detection))
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-[How to Recognize Optical Characters in Images in Python](https://www.thepythoncode.com/article/optical-character-recognition-pytesseract-python). ([code](machine-learning/optical-character-recognition))
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-[How to Use K-Means Clustering for Image Segmentation using OpenCV in Python](https://www.thepythoncode.com/article/kmeans-for-image-segmentation-opencv-python). ([code](machine-learning/kmeans-image-segmentation))
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-[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))
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-[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))
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-[Top 8 Python Libraries For Data Scientists and Machine Learning Engineers](https://www.thepythoncode.com/article/top-python-libraries-for-data-scientists).
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#[How to Use K-Means Clustering for Image Segmentation using OpenCV in Python](https://www.thepythoncode.com/article/kmeans-for-image-segmentation-opencv-python)
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To run this:
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-`pip3 install -r requirements.txt`
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- If you want to perform image segmentation on the image`image.jpg`:
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```
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python kmeans_segmentation.py image.jpg
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```
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- For live camera, consider using `live_kmeans_segmentation.py`.
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importcv2
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importnumpyasnp
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importmatplotlib.pyplotasplt
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importsys
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# read the image
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image=cv2.imread(sys.argv[1])
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# convert to RGB
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image=cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
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# reshape the image to a 2D array of pixels and 3 color values (RGB)
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pixel_values=image.reshape((-1,3))
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# convert to float
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pixel_values=np.float32(pixel_values)
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print(pixel_values.shape)
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# define stopping criteria
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criteria= (cv2.TERM_CRITERIA_EPS+cv2.TERM_CRITERIA_MAX_ITER,100,0.2)
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# number of clusters (K)
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k=3
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compactness,labels, (centers)=cv2.kmeans(pixel_values,k,None,criteria,10,cv2.KMEANS_RANDOM_CENTERS)
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print(labels)
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print(centers.shape)
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# convert back to 8 bit values
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centers=np.uint8(centers)
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# convert all pixels to the color of the centroids
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segmented_image=centers[labels.flatten()]
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# reshape back to the original image dimension
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segmented_image=segmented_image.reshape(image.shape)
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# show the image
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plt.imshow(segmented_image)
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plt.show()
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# disable only the cluster number 2
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masked_image=np.copy(image)
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masked_image[labels==2]= [0,0,0]
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# show the image
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plt.imshow(masked_image)
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plt.show()
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importcv2
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importnumpyasnp
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cap=cv2.VideoCapture(0)
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k=5
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# define stopping criteria
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criteria= (cv2.TERM_CRITERIA_EPS+cv2.TERM_CRITERIA_MAX_ITER,100,0.2)
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whileTrue:
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# read the image
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_,image=cap.read()
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# reshape the image to a 2D array of pixels and 3 color values (RGB)
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pixel_values=image.reshape((-1,3))
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# convert to float
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pixel_values=np.float32(pixel_values)
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# number of clusters (K)
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_,labels, (centers)=cv2.kmeans(pixel_values,k,None,criteria,10,cv2.KMEANS_RANDOM_CENTERS)
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# convert back to 8 bit values
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centers=np.uint8(centers)
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# convert all pixels to the color of the centroids
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segmented_image=centers[labels.flatten()]
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# reshape back to the original image dimension
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segmented_image=segmented_image.reshape(image.shape)
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# reshape labels too
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labels=labels.reshape(image.shape[0],image.shape[1])
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cv2.imshow("segmented_image",segmented_image)
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# visualize each segment
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ifcv2.waitKey(1)==ord("q"):
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break
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cap.release()
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cv2.destroyAllWindows()
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opencv-python
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numpy
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matplotlib

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