# How to isolate everything inside of a contour, scale it, and test the similarity to an image?

This situation is perfect for template matching. The idea is to search and find the location of a template image within a larger image. To perform this method, the template slides over the input image (similar to 2D convolution) where comparison methods are performed to determine pixel similarity. This is the basic idea behind template matching. Unfortunately, this basic method has flaws since it only works if the template image size is the same as the desired item to find in the input image. So if your template image was smaller than the desired region to find in the input image, this method would not work.

To get around this limitation, we can implement scale variant template matching by dynamically rescaling the image using `np.linspace()`. With each iteration, we resize the input image and keep track of the ratio. We continue resizing until the template image size is larger than the resized image while keeping track of the highest correlation value. A higher correlation value means a better match. Once we iterate through various scales, we find the ratio with the largest match and then compute the coordinates of the bounding box to determine the ROI.

Here’s the detected card highlighted in green. To visualize the process of dynamic template matching, uncomment the section in the code.

Code

``````import cv2
import numpy as np

# Resizes a image and maintains aspect ratio
def maintain_aspect_ratio_resize(image, width=None, height=None, inter=cv2.INTER_AREA):
# Grab the image size and initialize dimensions
dim = None
(h, w) = image.shape[:2]

# Return original image if no need to resize
if width is None and height is None:
return image

# We are resizing height if width is none
if width is None:
# Calculate the ratio of the height and construct the dimensions
r = height / float(h)
dim = (int(w * r), height)
# We are resizing width if height is none
else:
# Calculate the ratio of the 0idth and construct the dimensions
r = width / float(w)
dim = (width, int(h * r))

# Return the resized image
return cv2.resize(image, dim, interpolation=inter)

# Load template and convert to grayscale
template = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)
(tH, tW) = template.shape[:2]
cv2.imshow("template", template)

# Load original image, convert to grayscale
gray = cv2.cvtColor(original_image, cv2.COLOR_BGR2GRAY)
found = None

# Dynamically rescale image for better template matching
for scale in np.linspace(0.1, 3.0, 20)[::-1]:

# Resize image to scale and keep track of ratio
resized = maintain_aspect_ratio_resize(gray, width=int(gray.shape[1] * scale))
r = gray.shape[1] / float(resized.shape[1])

# Stop if template image size is larger than resized image
if resized.shape[0] < tH or resized.shape[1] < tW:
break

# Threshold resized image and apply template matching
thresh = cv2.threshold(resized, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
detected = cv2.matchTemplate(thresh, template, cv2.TM_CCOEFF)
(_, max_val, _, max_loc) = cv2.minMaxLoc(detected)

# Uncomment this section for visualization
'''
clone = np.dstack([thresh, thresh, thresh])
cv2.rectangle(clone, (max_loc[0], max_loc[1]), (max_loc[0] + tW, max_loc[1] + tH), (0,255,0), 2)
cv2.imshow('visualize', clone)
cv2.waitKey(50)
'''

# Keep track of correlation value
# Higher correlation means better match
if found is None or max_val > found[0]:
found = (max_val, max_loc, r)

# Compute coordinates of bounding box
(_, max_loc, r) = found
(start_x, start_y) = (int(max_loc[0] * r), int(max_loc[1] * r))
(end_x, end_y) = (int((max_loc[0] + tW) * r), int((max_loc[1] + tH) * r))

# Draw bounding box on ROI
cv2.rectangle(original_image, (start_x, start_y), (end_x, end_y), (0,255,0), 5)
cv2.imshow('detected', original_image)
cv2.imwrite('detected.png', original_image)
cv2.waitKey(0)
``````