Import

README.md

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+This repository contains all files for the Image recognition course of HU Electrical Engineering year 3.
+
+---
+
+Arne van Iterson
+Tom Selier

md/nuts.md

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+### Jullie gaan noten herkennen
+
+Hoe de neuk? Nut. 
+1. Hoe gaan we de noten herkennen (beeld, smaak, geluid, text)?
+    - Beeldherkenning lol
+2. Wat zijn de visuele kenmerken van de noten?
+    - Vorm, kleur, textuur, grootte
+3. Hoeveel noten willen we herkennen
+    - Qua type, hoeveel verschillende noten
+    - Qua tijd, hoeveel noten tegelijkertijd
+4. Wat is de achtergrond
+5. Met wat voor camera worden de noten bekeken
+6. Is de noot verwerkt
+
+De deelvragen moeten sequentieel zijn
+
+
+Wij moeten presenteren volgende week

src/adj.py

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+import cv2
+import numpy as np
+
+blur = 3
+canny_low = 255
+canny_high = 50
+
+def on_change(value):
+    pass
+
+img = cv2.imread("./res/trees/berk_sq1_original.png")
+img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+width = int(img.shape[1] * 40 / 100)
+height = int(img.shape[0] * 40 / 100)
+dim = (width, height)
+
+# resize image
+img = cv2.resize(img, dim, interpolation = cv2.INTER_AREA)
+
+cv2.imshow('frame', img)
+
+cv2.createTrackbar('Contrast', 'frame', 0, 25, on_change)
+cv2.createTrackbar('Canny low', 'frame', 0, 1000, on_change)
+cv2.createTrackbar('Canny high', 'frame', 0, 1000, on_change)
+
+while(1):
+    # get data from trackbars
+    blur = cv2.getTrackbarPos('Blur rate', 'frame')
+    canny_low = cv2.getTrackbarPos('Canny low', 'frame')
+    canny_high = cv2.getTrackbarPos('Canny high', 'frame')
+
+    # Canny Edge Detection
+    edges = cv2.Canny(image=img, threshold1=canny_low, threshold2=canny_high)  
+    
+    # Display Canny Edge Detection Image
+    cv2.imshow("Canny Edge Detection", edges)
+
+    # wait for escape
+    k = cv2.waitKey(5) & 0xFF
+    if k == 27:
+        break
+
+cv2.destroyAllWindows()

src/tree_edge.py

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+import cv2
+
+# Read the original image
+img = cv2.imread("./res/trees/berk_sq1_original.png")
+
+# Convert to graycsale
+img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+width = int(img_gray.shape[1] * 40 / 100)
+height = int(img_gray.shape[0] * 40 / 100)
+dim = (width, height)
+  
+# resize image
+resized = cv2.resize(img_gray, dim, interpolation = cv2.INTER_AREA)
+
+# Display original image
+cv2.imshow("Original", resized)
+
+# Blur the image for better edge detection
+img_blur = cv2.GaussianBlur(resized, (3, 3), 0)
+
+# Sobel Edge Detection
+sobelx = cv2.Sobel(
+    src=img_blur, ddepth=cv2.CV_64F, dx=1, dy=0, ksize=5
+)  # Sobel Edge Detection on the X axis
+sobely = cv2.Sobel(
+    src=img_blur, ddepth=cv2.CV_64F, dx=0, dy=1, ksize=5
+)  # Sobel Edge Detection on the Y axis
+sobelxy = cv2.Sobel(
+    src=img_blur, ddepth=cv2.CV_64F, dx=1, dy=1, ksize=5
+)  # Combined X and Y Sobel Edge Detection
+# Display Sobel Edge Detection Images
+cv2.imshow("Sobel X", sobelx)
+cv2.imshow("Sobel Y", sobely)
+cv2.imshow("Sobel X Y using Sobel() function", sobelxy)
+
+# Canny Edge Detection
+edges = cv2.Canny(
+    image=resized, threshold1=150, threshold2=250
+)  # Canny Edge Detection
+# Display Canny Edge Detection Image
+cv2.imshow("Canny Edge Detection", edges)
+cv2.waitKey(0)
+
+cv2.destroyAllWindows()
+
+## Sift