Update app.py

app.py

@@ -16,13 +16,15 @@ def fourier_transform_drawing(input_image, frames, coefficients):
     img = cv2.cvtColor(input_image, cv2.COLOR_RGB2BGR)
 
     # processing
-    imgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-    blurred = cv2.GaussianBlur(imgray, (7, 7), 0)
-
-    # apply Otsu threshold
+    # Resize the image to a smaller size for faster processing
+    width = 224
+    height = 224
+    dim = (width, height)
+    resized = cv2.resize(img, dim, interpolation=cv2.INTER_AREA)
+    
+    imgray = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY)
+    blurred = cv2.GaussianBlur(imgray, (5, 5), 0)
     (_, thresh) = cv2.threshold(blurred, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)
-
-    # find contours of the binary image
     contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
     
     # take only the largest contour
@@ -47,7 +49,7 @@ def fourier_transform_drawing(input_image, frames, coefficients):
 
     t_list = np.linspace(0, tau, len(xs))
 
-    # Compute the Fourier coefficients
+    # compute the Fourier coefficients
     def f(t, t_list, xs, ys):
         return np.interp(t, t_list, xs + 1j*ys)
 
@@ -92,7 +94,11 @@ def fourier_transform_drawing(input_image, frames, coefficients):
         
         draw_x.append(center[0])
         draw_y.append(center[1])
-        drawing.set_data(draw_x, draw_y)
+
+        if i > 0:
+            drawing.set_data([draw_x[i-1], draw_x[i]], [draw_y[i-1], draw_y[i]])
+        else:
+            drawing.set_data(draw_x[i], draw_y[i])
 
     drawing_time = 1
     time = np.linspace(0, drawing_time, num=frames)