significant speed improvement

detect_compo/lib_ip/block_division.py

@@ -68,17 +68,15 @@ def block_division(grey, org, grad_thresh,
 
                 # flood fill algorithm to get background (layout block)
                 mask_copy = mask.copy()
-                cv2.floodFill(grey, mask, (y,x), None, grad_thresh, grad_thresh, cv2.FLOODFILL_MASK_ONLY)
+                ff = cv2.floodFill(grey, mask, (y, x), None, grad_thresh, grad_thresh, cv2.FLOODFILL_MASK_ONLY)
+                # ignore small regions
+                if ff[0] < 500: continue
                 mask_copy = mask - mask_copy
-                region = np.nonzero(mask_copy[1:-1, 1:-1])
-                region = list(zip(region[0], region[1]))
+                region = np.reshape(cv2.findNonZero(mask_copy[1:-1, 1:-1]), (-1, 2))
+                region = [(p[1], p[0]) for p in region]
 
-                # ignore small regions
-                if len(region) < 500:
-                    continue
                 block = Block(region, grey.shape)
-
-                draw.draw_region(region, broad_all)
+                # draw.draw_region(region, broad_all)
                 # if block.height < 40 and block.width < 40:
                 #     continue
                 if block.height < 30:
@@ -100,7 +98,7 @@ def block_division(grey, org, grad_thresh,
                 # if block.height/row < min_block_height_ratio:
                 #     continue
                 blocks.append(block)
-                draw.draw_region(region, broad)
+                # draw.draw_region(region, broad)
     if show:
         cv2.imshow('flood-fill all', broad_all)
         cv2.imshow('block', broad)

detect_compo/lib_ip/ip_detection.py

@@ -335,15 +335,13 @@ def component_detection(binary, min_obj_area,
                 # region = util.boundary_bfs_connected_area(binary, i, j, mask)
 
                 mask_copy = mask.copy()
-                cv2.floodFill(binary, mask, (j, i), None, 0, 0, cv2.FLOODFILL_MASK_ONLY)
+                ff = cv2.floodFill(binary, mask, (j, i), None, 0, 0, cv2.FLOODFILL_MASK_ONLY)
+                if ff[0] < min_obj_area: continue
                 mask_copy = mask - mask_copy
-                region = np.nonzero(mask_copy[1:-1, 1:-1])
-                region = list(zip(region[0], region[1]))
+                region = np.reshape(cv2.findNonZero(mask_copy[1:-1, 1:-1]), (-1, 2))
+                region = [(p[1], p[0]) for p in region]
 
                 # filter out some compos
-                # ignore small area
-                if len(region) < min_obj_area:
-                    continue
                 component = Component(region, binary.shape)
                 # calculate the boundary of the connected area
                 # ignore small area

detect_compo/lib_ip/ip_preprocessing.py

@@ -33,17 +33,14 @@ def resize_by_height(org):
 def gray_to_gradient(img):
     if len(img.shape) == 3:
         img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-    row, column = img.shape[0], img.shape[1]
     img_f = np.copy(img)
     img_f = img_f.astype("float")
 
-    gradient = np.zeros((row, column))
-    for x in range(row - 1):
-        for y in range(column - 1):
-            gx = abs(img_f[x + 1, y] - img_f[x, y])
-            gy = abs(img_f[x, y + 1] - img_f[x, y])
-            gradient[x, y] = gx + gy
-    gradient = gradient.astype("uint8")
+    kernel_h = np.array([[0,0,0], [0,-1.,1.], [0,0,0]])
+    kernel_v = np.array([[0,0,0], [0,-1.,0], [0,1.,0]])
+    dst1 = abs(cv2.filter2D(img_f, -1, kernel_h))
+    dst2 = abs(cv2.filter2D(img_f, -1, kernel_v))
+    gradient = (dst1 + dst2).astype('uint8')
     return gradient
 
 

log.txt

@@ -12,4 +12,7 @@
 - Synchronized with Webapp.
 
 7/10/2020
-- Extract parameters as configurable
+- Extract parameters as configurable
+
+30/10/2020
+- Speed optimization (500% boost)

run_single.py

@@ -65,7 +65,7 @@ def resize_height_by_longest_edge(img_path, resize_length=800):
             classifier['Elements'] = CNN('Elements')
             # classifier['Noise'] = CNN('Noise')
         ip.compo_detection(input_path_img, output_root, key_params,
-                           classifier=classifier, resize_by_height=resized_height, show=True)
+                           classifier=classifier, resize_by_height=resized_height, show=False)
 
     if is_merge:
         import merge

speed-improvement.txt

@@ -0,0 +1,12 @@
+Optimization:
+1. ip_preprocessing.py / gray_to_gradient : 0.5s -> 0.02s
+
+2. ip_draw.py / draw_bounding_box : if not show and write_path is None: return : 0.005s -> 0s
+
+3. ip_detection.py / component_detection : if ff[0] < min_obj_area: continue : 2.5s -> 0.3s
+
+4. ip_detection.py / component_detection : cv2.findNonZero : 0.65s -> 0.33s
+
+5. block_division.py / block_division : if ff[0] < 500 : continue: 1.97s -> 1s
+
+6. block_division.py / block_division : Turn off draw : 1s -> 0.65s