Merge branch 'main' of https://arnweb.nl/gitea/arne/EV5_Beeldherk_Bomen

src/helpers/algorithms/canny_map.py

@@ -0,0 +1,56 @@
+import cv2
+import timeit
+import seaborn as sns
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Read the original image
+img = cv2.imread("./res/trees/berk_sq1_original.png")
+
+# Convert to graycsale
+img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+# resize image
+resized = cv2.resize(img_gray, (720, 720), 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)
+
+def thing():
+    canny_max = 1000
+    canny_step = 20
+
+    results = [[] for x in range((int)(canny_max / canny_step))]
+
+    for th1 in range(0, canny_max, canny_step):
+        for th2 in range(0, canny_max, canny_step):
+            # Canny Edge Detection
+            edges = cv2.Canny(image=resized, threshold1=th1, threshold2=th2)
+            
+            w_res = cv2.countNonZero(edges)
+            y_ind = (int)(th1 / canny_step)
+            x_ind = (int)(th2 / canny_step)
+            
+            print(f"Result at thres {th1}, {th2}; \tIndex {y_ind}, {x_ind} \t= {w_res}")
+            
+            results[y_ind].append(w_res)
+            print(results[y_ind])
+            
+    
+    # Create a heatmap
+    sns.heatmap(results)
+
+    # Display the heatmap
+    plt.show()
+
+print(timeit.timeit(lambda: thing(), number=1))
+
+# # Display Canny Edge Detection Image
+# cv2.imshow("Canny Edge Detection", edges)
+
+# cv2.waitKey(0)
+
+# cv2.destroyAllWindows()