add Sign Detection with Gaussian Filter

+import cv2
+import numpy as np
+
+def stackImages(scale,imgArray): #calculate convolution
+    rows = len(imgArray)
+    cols = len(imgArray[0])
+    rowsAvailable = isinstance(imgArray[0], list)
+    width = imgArray[0][0].shape[1]
+    height = imgArray[0][0].shape[0]
+    if rowsAvailable:
+        for x in range ( 0, rows):
+            for y in range(0, cols):
+                if imgArray[x][y].shape[:2] == imgArray[0][0].shape [:2]:
+                    imgArray[x][y] = cv2.resize(imgArray[x][y], (0, 0), None, scale, scale)
+                else:
+                    imgArray[x][y] = cv2.resize(imgArray[x][y], (imgArray[0][0].shape[1], imgArray[0][0].shape[0]), None, scale, scale)
+                if len(imgArray[x][y].shape) == 2: imgArray[x][y]= cv2.cvtColor( imgArray[x][y], cv2.COLOR_GRAY2BGR)
+        imageBlank = np.zeros((height, width, 3), np.uint8)
+        hor = [imageBlank]*rows
+        hor_con = [imageBlank]*rows
+        for x in range(0, rows):
+            hor[x] = np.hstack(imgArray[x])
+        ver = np.vstack(hor)
+    else:
+        for x in range(0, rows):
+            if imgArray[x].shape[:2] == imgArray[0].shape[:2]:
+                imgArray[x] = cv2.resize(imgArray[x], (0, 0), None, scale, scale)
+            else:
+                imgArray[x] = cv2.resize(imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]), None,scale, scale)
+            if len(imgArray[x].shape) == 2: imgArray[x] = cv2.cvtColor(imgArray[x], cv2.COLOR_GRAY2BGR)
+        hor= np.hstack(imgArray)
+        ver = horx
+    return ver
+
+def getContours(img):
+    contours,hierarchy = cv2.findContours(img,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
+    for cnt in contours:
+        area = cv2.contourArea(cnt)
+        print(area)
+        if area>500:
+            cv2.drawContours(imgContour, cnt, -1, (255, 0, 0), 3)
+            peri = cv2.arcLength(cnt,True)
+            #print(peri)
+            approx = cv2.approxPolyDP(cnt,0.02*peri,True)
+            print(len(approx))
+            objCor = len(approx)
+            x, y, w, h = cv2.boundingRect(approx)
+
+            if objCor ==3: objectType ="Tri"
+            elif objCor == 4:
+                aspRatio = w/float(h)
+                if aspRatio >0.98 and aspRatio <1.03: objectType= "Square"
+                else:objectType="Rectangle"
+            elif objCor>4: objectType= "Circle"
+            else:objectType="None"
+
+
+            cv2.rectangle(imgContour,(x,y),(x+w,y+h),(0,255,0),2)
+            cv2.putText(imgContour,objectType,
+                        (x+(w//2)-10,y+(h//2)-10),cv2.FONT_HERSHEY_COMPLEX,0.7,
+                        (0,0,0),2)
+
+
+path = 'TestPhoto/test08.jpg'
+img = cv2.imread(path)
+imgContour = img.copy()
+
+imgGray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
+imgBlur = cv2.GaussianBlur(imgGray,(7,7),1) # Gaussian smoothing filter for noise(고주파) reduce
+imgCanny = cv2.Canny(imgBlur,50,50)
+getContours(imgCanny)
+
+imgBlank = np.zeros_like(img)
+imgStack = stackImages(0.6,([img,imgGray,imgBlur],
+                            [imgCanny,imgContour,imgBlank]))
+
+cv2.imshow("Stack", imgStack)
+
+cv2.waitKey(0)