Initial pass at automatic hi-res NYPL Stereogranimator morphing

1 files changed, 108 insertions, 0 deletions

diff --git a/template_match_multiscale.py b/template_match_multiscale.py
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+#!/usr/bin/env python
+# USAGE
+# python template_match_multiscale.py --template template.png --image image.tif
+# Adapted from: http://www.pyimagesearch.com/2015/01/26/multi-scale-template-matching-using-python-opencv/
+
+# import the necessary packages
+import numpy as np
+import argparse
+import glob
+import cv2
+
+def resize(image, width = None, height = None, inter = cv2.INTER_AREA):
+	# initialize the dimensions of the image to be resized and
+	# grab the image size
+	dim = None
+	(h, w) = image.shape[:2]
+
+	# if both the width and height are None, then return the
+	# original image
+	if width is None and height is None:
+		return image
+
+	# check to see if the 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)
+
+	# otherwise, the height is None
+	else:
+		# calculate the ratio of the width and construct the
+		# dimensions
+		r = width / float(w)
+		dim = (width, int(h * r))
+
+	# resize the image
+	resized = cv2.resize(image, dim, interpolation = inter)
+
+	# return the resized image
+	return resized
+
+# construct the argument parser and parse the arguments
+ap = argparse.ArgumentParser()
+ap.add_argument("-t", "--template", required=True, help="Path to template image")
+ap.add_argument("-i", "--image", required=True,
+	help="Path to image where template will be matched")
+ap.add_argument("-v", "--visualize",
+	help="Flag indicating whether or not to visualize each iteration")
+args = vars(ap.parse_args())
+
+# load the image image, convert it to grayscale, and detect edges
+template = cv2.imread(args["template"])
+template = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)
+template = cv2.Canny(template, 50, 200)
+(tH, tW) = template.shape[:2]
+cv2.imshow("Template", template)
+
+imagePath = args["image"]
+# load the image, convert it to grayscale, and initialize the
+# bookkeeping variable to keep track of the matched region
+gray = cv2.imread(imagePath, 0)
+found = None
+
+# loop over the scales of the image
+for scale in np.linspace(0.2, 1.0, 20)[::-1]:
+  # resize the image according to the scale, and keep track
+  # of the ratio of the resizing
+  resized = resize(gray, width = int(gray.shape[1] * scale))
+  r = gray.shape[1] / float(resized.shape[1])
+
+  # if the resized image is smaller than the template, then break
+  # from the loop
+  if resized.shape[0] < tH or resized.shape[1] < tW:
+    break
+
+  # detect edges in the resized, grayscale image and apply template
+  # matching to find the template in the image
+  edged = cv2.Canny(resized, 50, 200)
+  result = cv2.matchTemplate(edged, template, cv2.TM_CCOEFF)
+  (_, maxVal, _, maxLoc) = cv2.minMaxLoc(result)
+
+  # check to see if the iteration should be visualized
+  if args.get("visualize", False):
+    # draw a bounding box around the detected region
+    clone = np.dstack([edged, edged, edged])
+    cv2.rectangle(clone, (maxLoc[0], maxLoc[1]),
+      (maxLoc[0] + tW, maxLoc[1] + tH), (0, 0, 255), 2)
+    cv2.imshow("Visualize", clone)
+    cv2.waitKey(0)
+
+  # if we have found a new maximum correlation value, then ipdate
+  # the bookkeeping variable
+  if found is None or maxVal > found[0]:
+    found = (maxVal, maxLoc, r)
+
+# unpack the bookkeeping varaible and compute the (x, y) coordinates
+# of the bounding box based on the resized ratio
+(_, maxLoc, r) = found
+(startX, startY) = (int(maxLoc[0] * r), int(maxLoc[1] * r))
+(endX, endY) = (int((maxLoc[0] + tW) * r), int((maxLoc[1] + tH) * r))
+
+print "%dx%d+%d+%d" % ((endX - startX), (endY - startY), startX, startY)
+
+# draw a bounding box around the detected result and display the image
+# cv2.rectangle(gray, (startX, startY), (endX, endY), (0, 0, 255), 2)
+# cv2.imshow("Image", gray)
+# cv2.waitKey(0)