re-implement equipment with masks

vision.py

@@ -27,22 +27,32 @@ class Vision:
         # TM_CCOEFF, TM_CCOEFF_NORMED, TM_CCORR, TM_CCORR_NORMED, TM_SQDIFF, TM_SQDIFF_NORMED
         self.method = method
 
-    def find(self, haystack_img, needle_img, threshold=0.5, max_results=10):
+    def find(self, haystack_img, needle_img, threshold=0.5, max_results=10, normalize=False, mask=None):
         # run the OpenCV algorithm
         needle_w = needle_img.shape[1]
         needle_h = needle_img.shape[0]
-        result = cv.matchTemplate(haystack_img, needle_img, self.method)
+
+        if normalize:
+            result = cv.matchTemplate(haystack_img, needle_img, cv.TM_CCORR_NORMED, None, mask)
+            _minVal, _maxVal, minLoc, maxLoc = cv.minMaxLoc(result, None)
+            cv.normalize(result, result, 0, 1, cv.NORM_MINMAX, -1)
+        else:
+            result = cv.matchTemplate(haystack_img, needle_img, self.method)
 
         # Get the all the positions from the match result that exceed our threshold
         locations = np.where(result >= threshold)
         locations = list(zip(*locations[::-1]))
         # print(locations)
+        _minVal, _maxVal, minLoc, maxLoc = cv.minMaxLoc(result, None)
 
         # if we found no results, return now. this reshape of the empty array allows us to 
         # concatenate together results without causing an error
         if not locations:
             return np.array([], dtype=np.int32).reshape(0, 4)
 
+        if len(locations) > 5000:
+            return np.array([], dtype=np.int32).reshape(0, 4)
+
         # You'll notice a lot of overlapping rectangles get drawn. We can eliminate those redundant
         # locations by using groupRectangles().
         # First we need to create the list of [x, y, w, h] rectangles