added shape detection tutorial

Author: x4nth055

Diff for: README.md

@@ -29,6 +29,7 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy
     - [How to Make an Image Classifier in Python using Keras](https://www.thepythoncode.com/article/image-classification-keras-python). ([code](machine-learning/image-classifier))
     - [How to Use Transfer Learning for Image Classification using Keras in Python](https://www.thepythoncode.com/article/use-transfer-learning-for-image-flower-classification-keras-python). ([code](machine-learning/image-classifier-using-transfer-learning))
     - [How to Perform Edge Detection in Python using OpenCV](https://www.thepythoncode.com/article/canny-edge-detection-opencv-python). ([code](machine-learning/edge-detection))
+    - [How to Detect Shapes in Images in Python](https://www.thepythoncode.com/article/detect-shapes-hough-transform-opencv-python). ([code](machine-learning/shape-detection))
     - [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).
 

Diff for: machine-learning/shape-detection/README.md

@@ -0,0 +1,11 @@
+# [How to Detect Shapes in Images in Python](https://www.thepythoncode.com/article/detect-shapes-hough-transform-opencv-python)
+To run this:
+- `pip3 install -r requirements.txt`.
+- To detect lines of the image `monitor.jpg`:
+    ```
+    python shape_detector.py monitor.jpg
+    ```
+- To detect lines of live camera:
+    ```
+    python live_shape_detector.py
+    ```

Diff for: machine-learning/shape-detection/live_shape_detector.py

@@ -0,0 +1,27 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import cv2
+
+cap = cv2.VideoCapture(0)
+
+while True:
+    _, image = cap.read()
+    # convert to grayscale
+    grayscale = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    # perform edge detection
+    edges = cv2.Canny(grayscale, 30, 100)
+    # detect lines in the image using hough lines technique
+    lines = cv2.HoughLinesP(edges, 1, np.pi/180, 60, np.array([]), 50, 5)
+    # iterate over the output lines and draw them
+    for line in lines:
+        for x1, y1, x2, y2 in line:
+            cv2.line(image, (x1, y1), (x2, y2), (255, 0, 0), 3)
+            cv2.line(edges, (x1, y1), (x2, y2), (255, 0, 0), 3)
+    # show images
+    cv2.imshow("image", image)
+    cv2.imshow("edges", edges)
+    if cv2.waitKey(1) == ord("q"):
+        break
+
+cap.release()
+cv2.destroyAllWindows()

Diff for: machine-learning/shape-detection/monitor.jpg

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

Diff for: machine-learning/shape-detection/requirements.txt

@@ -0,0 +1,24 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import cv2
+import sys
+
+# read the image
+image = cv2.imread(sys.argv[1])
+
+# convert to grayscale
+grayscale = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+# perform edge detection
+edges = cv2.Canny(grayscale, 30, 100)
+
+# detect lines in the image using hough lines technique
+lines = cv2.HoughLinesP(edges, 1, np.pi/180, 60, np.array([]), 50, 5)
+# iterate over the output lines and draw them
+for line in lines:
+    for x1, y1, x2, y2 in line:
+        cv2.line(image, (x1, y1), (x2, y2), color=(20, 220, 20), thickness=3)
+
+# show the image
+plt.imshow(image)
+plt.show()