If a $$ 4 \times 4 $$ base image is compared with a $$ 2 \times 2 $$ template image, the resulting output array will be $$ 3 \times 3 $$.

```python
# base image
[[255, 255, 255, 255], [255, 255, 255, 255], [255, 255, 255, 255], [255, 255, 255, 255]]
# temp image
[[255, 255], [255, 255]]
# output array
[[1, 1, 1], [1, 1, 1], [1, 1, 1]]
```

This uniform output occurs because all the values of the base image match perfectly with the template image in every position. In contrast, if there are mismatches, the output array reflects the localized similarities:

```python
# base image
[[255, 0, 255, 255], [255, 255, 0, 255], [255, 255, 255, 255], [0, 255, 255, 255]]
# temp image
[[255, 255], [255, 255]]
# output array
[[0, 0, 0], [1, 0, 0], [0, 1, 1]]
```

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Basic Template matching involves having an identical object file and then searching for the same pattern to appear in the base image. 
There are many methods to do template matching

```python
methods = [cv2.TM_CCOEFF, cv2.TMCCOEFF_NORMED, cv2.TM_CCORR, cv2.TM_CCORR_NORMED, cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]
```
OpenCV recommends that you utilize all the methods and take the best matching algorithm as your final approach.

```python
img = cv2.imread('path', 0)
template = cv2.imread('path'. 0)
height, width = template.shape

#methods list

for method in methods:
        img2 = img.copy()

        result = cv2.matchTemplate(img2, template, method)
        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)

        if method in [cv22.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:
                location = min_loc
        else:
                location = max_loc

        bottom_right = (location[0] + width, location[1] + height)
        cv2.rectangle(img2, location, bottom_right, 255, 5)
        cv2.imshow("Match", img2)
        cv2.waitKey(0)
        cv2.destroyAllWindows()
```
What this will do is to move around the template around the base image and return how good of a match it finds in each area. Results will return, (W_base - W_temp + 1, H_base - H_temp + 1) size array.

With the location information, we can draw a shape on the coordinate location. SQDIFF methods use min_loc. All other methods use max_loc.


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