Develop Reference

Converting graphs from a scanned document into data

I'm currently trying to write something that can extract data from some uncommon graphs in a book. I scanned the pages of the book, and by using opencv I would like to detect some features from the graphs in order to convert it into useable data. In the left graph I'm looking for the height of the "triangles" and in the right graph the distance from the center to the points where the dotted lines intersect with the gray area. In both cases I would like to convert these values into numeric data for further usage.
The first thing I thought of was detecting the lines of the charts, in the hopes I could somehow measure their length or position. For this I'm using the Hough Line Transform. The following snippet of code shows how far I've gotten already.
import numpy as np
import cv2
# Reading the image
img = cv2.imread('test2.jpg')
# Convert the image to grayscale
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# Apply edge detection
edges = cv2.Canny(gray,50,150,apertureSize = 3)
# Line detection
lines = cv2.HoughLinesP(edges,1,np.pi/180,100,minLineLength=50,maxLineGap=20)
for line in lines:
x1,y1,x2,y2 = line[0]
cv2.line(img,(x1,y1),(x2,y2),(0,0,255),2)
cv2.imwrite('linesDetected.jpg',img)
The only problem is that this detection algorithm is not accurate at all. At least not for me. And in order to extract some data from the charts, the detection of the lines should be somewhat accurate. Is their any way I could do this? Or is my strategy to detect lines just wrong in the first place? Should I maybe start with detecting something else, like circles,object sizes, contours or colors?

Using color segmentation is an easy way to convert this graph to data. This method does require some manual annotation. After the graph is segmented, count the pixels for each color. Check out the 'watershed' demo in the demo files that are included in the OpenCV library:
import numpy as np
import cv2 as cv
from common import Sketcher
class App:
def __init__(self, fn):
self.img = cv.imread(fn)
self.img = cv.resize(self.img, (654,654))
h, w = self.img.shape[:2]
self.markers = np.zeros((h, w), np.int32)
self.markers_vis = self.img.copy()
self.cur_marker = 1
self.colors = np.int32( list(np.ndindex(2, 2, 3)) ) * 123
self.auto_update = True
self.sketch = Sketcher('img', [self.markers_vis, self.markers], self.get_colors)
def get_colors(self):
return list(map(int, self.colors[self.cur_marker])), self.cur_marker
def watershed(self):
m = self.markers.copy()
cv.watershed(self.img, m)
cv.imshow('img', self.img)
overlay = self.colors[np.maximum(m, 0)]
vis = cv.addWeighted(self.img, 0.5, overlay, 0.5, 0.0, dtype=cv.CV_8UC3)
cv.imshow('overlay', np.array(overlay, np.uint8))
cv.imwrite('/home/stephen/Desktop/overlay.png', np.array(overlay, np.uint8))
cv.imshow('watershed', vis)
def run(self):
while cv.getWindowProperty('img', 0) != -1 or cv.getWindowProperty('watershed', 0) != -1:
ch = cv.waitKey(50)
if ch >= ord('1') and ch <= ord('9'):
self.cur_marker = ch - ord('0')
print('marker: ', self.cur_marker)
if self.sketch.dirty and self.auto_update:
self.watershed()
self.sketch.dirty = False
if ch == 27: break
cv.destroyAllWindows()
fn = '/home/stephen/Desktop/test.png'
App(cv.samples.findFile(fn)).run()
The output will be an image like this:
You can count the pixels for each color using this code:
# Extract the values from the image
vals = []
img = cv.imread('/home/stephen/Desktop/overlay.png')
# Get the colors in the image
flat = img.reshape(-1, img.shape[-1])
colors = np.unique(flat, axis=0)
# Iterate through the colors (ignore the first and last colors)
for color in colors[1:-1]:
a,b,c = color
lower = a-1, b-1, c-1
upper = a+1,b+1,c+1
lower = np.array(lower)
upper = np.array(upper)
mask = cv.inRange(img, lower, upper)
vals.append(sum(sum(mask)))
cv.imshow('mask', mask)
cv.waitKey(0)
cv.destroyAllWindows()
And print out the output data using this code:
names = ['alcohol', 'esters', 'biter', 'hoppy', 'acid', 'zoetheid', 'mout']
print(list(zip(names, vals)))
The output is:
[('alcohol', 22118), ('esters', 26000), ('biter', 16245), ('hoppy', 21170), ('acid', 19156), ('zoetheid', 11090), ('mout', 7167)]

Stitch two images using Homography transform - Transformed image cropped

Image stitching not work properly. The warped image is cropped and interpolation cannot be done because images do not intersect.
Hi,
I was assigned an homework in which I have to stitch togheter two images, shot by different cameras.
I should find the homography matrix and then warp the second image using this matrix. At the end I must interpolate the two images.
Unfortunately, the code I wrote seems not to work properly. During second image warp I lost most of the image information; a lot of pixels are black and not the whole transformed image is transformed.
I track in the two images four pixels each, in the same order. Below you can find the piece of code I wrote.
# Globals
points = []
def show_and_fetch(image, title):
cv2.namedWindow(title, cv2.WINDOW_NORMAL)
cv2.setMouseCallback(title, mouse_callback)
# Show the image
cv2.imshow(title, image)
# Wait for user input to continue
cv2.waitKey(0)
cv2.destroyAllWindows()
# mouse callback function
def mouse_callback(event,x,y,flags,param):
if event == cv2.EVENT_LBUTTONDOWN:
points.append([x, y])
def stitching():
"""
This procedure stiches two images
:return:
"""
print "Stitching starts..."
###########################################################################
# Get input information
in_file_1 = utils.get_input(
"Insert 0 to exit, the path to the first image to stitch "
"or empty input to use default image: ", "string",
constants.default_stitching1)
in_file_2 = utils.get_input(
"Insert 0 to exit, the path to the second image to stitch "
"or empty input to use default image: ", "string",
constants.default_stitching2)
image_1 = utils.read_image(in_file_1)
image_2 = utils.read_image(in_file_2)
global points
show_and_fetch(image_1, "Image 1 to Stitch")
image_1_points = np.asarray(points, dtype=np.float32)
points = []
show_and_fetch(image_2, "Image 2 to Stitch")
image_2_points = np.asarray(points, dtype=np.float32)
matrix, mask = cv2.findHomography(image_1_points, image_2_points, cv2.RANSAC, 5)
image_1_warped = cv2.warpPerspective(image_1, matrix, dsize=image_1.shape[0:2])
utils.show_image_and_wait(image_1_warped, 'Image 1 warped', wait=False)
utils.show_image_and_wait(image_1, 'Image 1', wait=False)
utils.show_image_and_wait(image_2, 'Image 2')
if __name__ == "__main__":
stitching()
I expect the warped image to be transformed, preserving the most of the information, in terms of pixels. Then interpolation should apply the intersection of the two images that overlap in a certain area.
For instance I want to interpolete these two images:

I've managed to stitch images based on this solution. Here is the stitching result:
Here is the full code:
import cv2
import imutils
import numpy as np
class Stitcher(object):
def __init__(self):
self.isv3 = imutils.is_cv3()
def stitch(self, images, ratio=0.75, reprojThresh=4.0, showMatches=False):
(imageB, imageA) = images
(kpsA, featuresA) = self.detectAndDescribe(imageA)
(kpsB, featuresB) = self.detectAndDescribe(imageB)
# match features between the two images
m = self.matchKeypoints(kpsA, kpsB, featuresA, featuresB, ratio, reprojThresh)
if not m:
return None
# otherwise, apply a perspective warp to stitch the images
# together
(matches, H, status) = m
result = cv2.warpPerspective(imageA, H,
(imageA.shape[1] + imageB.shape[1], imageA.shape[0]))
result[0:imageB.shape[0], 0:imageB.shape[1]] = imageB
# check to see if the keypoint matches should be visualized
if showMatches:
vis = self.drawMatches(imageA, imageB, kpsA, kpsB, matches,
status)
# return a tuple of the stitched image and the
# visualization
return result, vis
# return the stitched image
return result
def detectAndDescribe(self, image):
# convert the image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# check to see if we are using OpenCV 3.X
if self.isv3:
# detect and extract features from the image
descriptor = cv2.xfeatures2d.SIFT_create()
(kps, features) = descriptor.detectAndCompute(image, None)
# otherwise, we are using OpenCV 2.4.X
else:
# detect keypoints in the image
detector = cv2.xfeatures2d.SIFT_create()
kps = detector.detect(gray)
# extract features from the image
extractor = cv2.xfeatures2d.SIFT_create()
(kps, features) = extractor.compute(gray, kps)
# convert the keypoints from KeyPoint objects to NumPy
# arrays
kps = np.float32([kp.pt for kp in kps])
# return a tuple of keypoints and features
return kps, features
def matchKeypoints(self, kpsA, kpsB, featuresA, featuresB,
ratio, reprojThresh):
# compute the raw matches and initialize the list of actual
# matches
matcher = cv2.DescriptorMatcher_create("BruteForce")
rawMatches = matcher.knnMatch(featuresA, featuresB, 2)
matches = []
# loop over the raw matches
for m in rawMatches:
# ensure the distance is within a certain ratio of each
# other (i.e. Lowe's ratio test)
if len(m) == 2 and m[0].distance < m[1].distance * ratio:
matches.append((m[0].trainIdx, m[0].queryIdx))
# computing a homography requires at least 4 matches
if len(matches) > 4:
# construct the two sets of points
ptsA = np.float32([kpsA[i] for (_, i) in matches])
ptsB = np.float32([kpsB[i] for (i, _) in matches])
# compute the homography between the two sets of points
(H, status) = cv2.findHomography(ptsA, ptsB, cv2.RANSAC,
reprojThresh)
# return the matches along with the homograpy matrix
# and status of each matched point
return (matches, H, status)
# otherwise, no homograpy could be computed
return None
def drawMatches(self, imageA, imageB, kpsA, kpsB, matches, status):
# initialize the output visualization image
(hA, wA) = imageA.shape[:2]
(hB, wB) = imageB.shape[:2]
vis = np.zeros((max(hA, hB), wA + wB, 3), dtype="uint8")
vis[0:hA, 0:wA] = imageA
vis[0:hB, wA:] = imageB
# loop over the matches
for ((trainIdx, queryIdx), s) in zip(matches, status):
# only process the match if the keypoint was successfully
# matched
if s == 1:
# draw the match
ptA = (int(kpsA[queryIdx][0]), int(kpsA[queryIdx][1]))
ptB = (int(kpsB[trainIdx][0]) + wA, int(kpsB[trainIdx][1]))
cv2.line(vis, ptA, ptB, (0, 255, 0), 1)
# return the visualization
return vis
image1 = cv2.imread('image1.jpg')
image2 = cv2.imread('image2.jpg')
stitcher = Stitcher()
(result, vis) = stitcher.stitch([image1, image2], showMatches=True)
cv2.imwrite('result.jpg', result)

I faced with the same problem. It turns out that the order of my images was wrong.
I had two images for stitching. One needs to stitch from left to another. However, I was computing the transform as I want it to stitch from right.

Mask Issue With Python OpenCV ORB Image Alignment

I am trying to implement a Python (3.7) OpenCV (3.4.3) ORB image alignment. I normally do most of my processing with ImageMagick. But I need to do some image alignment and am trying to use Python OpenCV ORB. My script is based upon one from Satya Mallick's Learn OpenCV tutorial at https://www.learnopencv.com/image-alignment-feature-based-using-opencv-c-python/.
However, I am trying to modify it to use a rigid alignment rather than a perspective homology and to filter the points using a mask to limit the difference in y values, since the images are nearly aligned already.
The mask approach was taken from a FLANN alignment code in the last example at https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_feature2d/py_matcher/py_matcher.html.
My script works fine, if I remove the matchesMask, which should provide the point filtering. (I have two other working scripts. One is similar, but just filters the points and ignores the mask. The other is based upon the ECC algorithm.)
However, I would like to understand why my code below is not working.
Perhaps the structure of my mask is incorrect in current versions of Python Opencv?
The error that I get is:
Traceback (most recent call last):
File "warp_orb_rigid2_filter.py", line 92, in <module>
imReg, m = alignImages(im, imReference)
File "warp_orb_rigid2_filter.py", line 62, in alignImages
imMatches = cv2.drawMatches(im1, keypoints1, im2, keypoints2, matches, None, **draw_params)
SystemError: <built-in function drawMatches> returned NULL without setting an error
Here is my code. The first arrow shows where the mask is created. The second arrow shows the line I have to remove to get the script to work. But then it ignores my filtering of points.
#!/bin/python3.7
import cv2
import numpy as np
MAX_FEATURES = 500
GOOD_MATCH_PERCENT = 0.15
def alignImages(im1, im2):
# Convert images to grayscale
im1Gray = cv2.cvtColor(im1, cv2.COLOR_BGR2GRAY)
im2Gray = cv2.cvtColor(im2, cv2.COLOR_BGR2GRAY)
# Detect ORB features and compute descriptors.
orb = cv2.ORB_create(MAX_FEATURES)
keypoints1, descriptors1 = orb.detectAndCompute(im1Gray, None)
keypoints2, descriptors2 = orb.detectAndCompute(im2Gray, None)
# Match features.
matcher = cv2.DescriptorMatcher_create(cv2.DESCRIPTOR_MATCHER_BRUTEFORCE_HAMMING)
matches = matcher.match(descriptors1, descriptors2, None)
# Sort matches by score
matches.sort(key=lambda x: x.distance, reverse=False)
# Remove not so good matches
numGoodMatches = int(len(matches) * GOOD_MATCH_PERCENT)
matches = matches[:numGoodMatches]
# Extract location of good matches and filter by diffy
points1 = np.zeros((len(matches), 2), dtype=np.float32)
points2 = np.zeros((len(matches), 2), dtype=np.float32)
for i, match in enumerate(matches):
points1[i, :] = keypoints1[match.queryIdx].pt
points2[i, :] = keypoints2[match.trainIdx].pt
# initialize empty arrays for newpoints1 and newpoints2 and mask
newpoints1 = np.empty(shape=[0, 2])
newpoints2 = np.empty(shape=[0, 2])
matches_Mask = [0] * len(matches)
# filter points by using mask
for i in range(len(matches)):
pt1 = points1[i]
pt2 = points2[i]
pt1x, pt1y = zip(*[pt1])
pt2x, pt2y = zip(*[pt2])
diffy = np.float32( np.float32(pt2y) - np.float32(pt1y) )
print(diffy)
if abs(diffy) < 10.0:
newpoints1 = np.append(newpoints1, [pt1], axis=0)
newpoints2 = np.append(newpoints2, [pt2], axis=0)
matches_Mask[i]=[1,0] #<--- mask created
print(matches_Mask)
draw_params = dict(matchColor = (255,0,),
singlePointColor = (255,255,0),
matchesMask = matches_Mask, #<---- remove mask here
flags = 0)
# Draw top matches
imMatches = cv2.drawMatches(im1, keypoints1, im2, keypoints2, matches, None, **draw_params)
cv2.imwrite("/Users/fred/desktop/lena_matches.png", imMatches)
# Find Affine Transformation
# true means full affine, false means rigid (SRT)
m = cv2.estimateRigidTransform(newpoints1,newpoints2,False)
# Use affine transform to warp im1 to match im2
height, width, channels = im2.shape
im1Reg = cv2.warpAffine(im1, m, (width, height))
return im1Reg, m
if __name__ == '__main__':
# Read reference image
refFilename = "/Users/fred/desktop/lena.png"
print("Reading reference image : ", refFilename)
imReference = cv2.imread(refFilename, cv2.IMREAD_COLOR)
# Read image to be aligned
imFilename = "/Users/fred/desktop/lena_r1.png"
print("Reading image to align : ", imFilename);
im = cv2.imread(imFilename, cv2.IMREAD_COLOR)
print("Aligning images ...")
# Registered image will be stored in imReg.
# The estimated transform will be stored in m.
imReg, m = alignImages(im, imReference)
# Write aligned image to disk.
outFilename = "/Users/fred/desktop/lena_r1_aligned.jpg"
print("Saving aligned image : ", outFilename);
cv2.imwrite(outFilename, imReg)
# Print estimated homography
print("Estimated Affine Transform : \n", m)
Here are my two images: lena and lena rotated by 1 degree. Note that these are not my actual images. These image have no diffy values > 10, but my actual images do.
I am trying to align and warp the rotated image to match the original lena image.

The way you are creating the mask is incorrect. It only needs to be a list with single numbers, with each number telling you whether you want to use that particular feature match.
Therefore, replace this line:
matches_Mask = [[0,0] for i in range(len(matches))]
With this:
matches_Mask = [0] * len(matches)
... so:
# matches_Mask = [[0,0] for i in range(len(matches))]
matches_Mask = [0] * len(matches)
This creates a list of 0s that is as long as the number of matches. Finally, you need to change writing to the mask with a single value:
if abs(diffy) < 10.0:
#matches_Mask[i]=[1,0] #<--- mask created
matches_Mask[i] = 1
I finally get this:
Estimated Affine Transform :
[[ 1.00001187 0.01598318 -5.05963793]
[-0.01598318 1.00001187 -0.86121051]]
Take note that the format of the mask is different depending on what matcher you use. In this case, you use brute force matching so the mask needs to be in the format that I just described.
If you used FLANN's knnMatch for example, then it will be a nested list of lists, with each element being a list that is k long. For example, if you had k=3 and five keypoints, it will be a list of five elements long, with each element being a three element list. Each element in the sub-list delineates what match you want to use for drawing.

Detect the green lines in this image and calculate their lengths

Sample Images
The image can be more noisy at times where more objects intervene from the background. Right now I am using various techniques using the RGB colour space to detect the lines but it fails when there is change in the colour due to intervening obstacles from the background. I am using opencv and python.
I have read that HSV is better for colour detection and used but haven't been successful yet.
I am not able to find a generic solution to this problem. Any hints or clues in this direction would be of great help.

STILL IN PROGRESS
First of all, an RGB image consists of 3 grayscale images. Since you need the green color you will deal only with one channel. The green one. To do so, you can split the image, you can use b,g,r = cv2.split('Your Image'). You will get an output like that if you are showing the green channel:
After that you should threshold the image using your desired way. I prefer Otsu's thresholding in this case. The output after thresholding is:
It's obvious that the thresholded image is extremley noisy. So performing erosion will reduce the noise a little bit. The noise reduced image will be similar to the following:
I tried using closing instead of dilation, but closing preserves some unwanted noise. So I separately performed erosion followed by dilation. After dilation the output is:
Note that: You can do your own way in morphological operation. You can use opening instead of what I did. The results are subjective from
one person to another.
Now you can try one these two methods:
1. Blob Detection.
2. HoughLine Transform.
TODO
Try out these two methods and choose the best.

You should use the fact that you know you are trying to detect a line by using the line hough transform.
http://docs.opencv.org/2.4/doc/tutorials/imgproc/imgtrans/hough_lines/hough_lines.html
When the obstacle also look like a line use the fact that you know approximately what is the orientation of the green lines.
If you don't know the orientation of the line use hte fact that there are several green lines with the same orientation and only one line that is the obstacle
Here is a code for what i meant:
import cv2
import numpy as np
# Params
minLineCount = 300 # min number of point alogn line with the a specif orientation
minArea = 100
# Read img
img = cv2.imread('i.png')
greenChannel = img[:,:,1]
# Do noise reduction
iFilter = cv2.bilateralFilter(greenChannel,5,5,5)
# Threshold data
#ret,iThresh = cv2.threshold(iFilter,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
iThresh = (greenChannel > 4).astype(np.uint8)*255
# Remove small areas
se1 = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))
iThreshRemove = cv2.morphologyEx(iThresh, cv2.MORPH_OPEN, se1)
# Find edges
iEdge = cv2.Canny(iThreshRemove,50,100)
# Hough line transform
lines = cv2.HoughLines(iEdge, 1, 3.14/180,75)
# Find the theta with the most lines
thetaCounter = dict()
for line in lines:
theta = line[0, 1]
if theta in thetaCounter:
thetaCounter[theta] += 1
else:
thetaCounter[theta] = 1
maxThetaCount = 0
maxTheta = 0
for theta in thetaCounter:
if thetaCounter[theta] > maxThetaCount:
maxThetaCount = thetaCounter[theta]
maxTheta = theta
# Find the rhos that corresponds to max theta
rhoValues = []
for line in lines:
rho = line[0, 0]
theta = line[0, 1]
if theta == maxTheta:
rhoValues.append(rho)
# Go over all the lines with the specific orientation and count the number of pixels on that line
# if the number is bigger than minLineCount draw the pixels in finaImage
lineImage = np.zeros_like(iThresh, np.uint8)
for rho in range(min(rhoValues), max(rhoValues), 1):
a = np.cos(maxTheta)
b = np.sin(maxTheta)
x0 = round(a*rho)
y0 = round(b*rho)
lineCount = 0
pixelList = []
for jump in range(-1000, 1000, 1):
x1 = int(x0 + jump * (-b))
y1 = int(y0 + jump * (a))
if x1 < 0 or y1 < 0 or x1 >= lineImage.shape[1] or y1 >= lineImage.shape[0]:
continue
if iThreshRemove[y1, x1] == int(255):
pixelList.append((y1, x1))
lineCount += 1
if lineCount > minLineCount:
for y,x in pixelList:
lineImage[y, x] = int(255)
# Remove small areas
## Opencv 2.4
im2, contours, hierarchy = cv2.findContours(lineImage,cv2.RETR_CCOMP,cv2.CHAIN_APPROX_NONE )
finalImage = np.zeros_like(lineImage)
finalShapes = []
for contour in contours:
if contour.size > minArea:
finalShapes.append(contour)
cv2.fillPoly(finalImage, finalShapes, 255)
## Opencv 3.0
# output = cv2.connectedComponentsWithStats(lineImage, 8, cv2.CV_32S)
#
# finalImage = np.zeros_like(output[1])
# finalImage = output[1]
# stat = output[2]
# for label in range(output[0]):
# if label == 0:
# continue
# cc = stat[label,:]
# if cc[cv2.CC_STAT_AREA] < minArea:
# finalImage[finalImage == label] = 0
# else:
# finalImage[finalImage == label] = 255
# Show image
#cv2.imwrite('finalImage2.jpg',finalImage)
cv2.imshow('a', finalImage.astype(np.uint8))
cv2.waitKey(0)
and the result for the images:

Copy a part of an image in opencv and python

I'm trying to split an image into several sub-images with opencv by identifying templates of the original image and then copy the regions where I matched those templates. I'm a TOTAL newbie to opencv! I've identified the sub-images using:
result = cv2.matchTemplate(img, template, cv2.TM_CCORR_NORMED)
After some cleanup I get a list of tuples called points in which I iterate to show the rectangles. tw and th is the template width and height respectively.
for pt in points:
re = cv2.rectangle(img, pt, (pt[0] + tw, pt[1] + th), 0, 2)
print('%s, %s' % (str(pt[0]), str(pt[1])))
count+=1
What I would like to accomplish is to save the octagons (https://dl.dropbox.com/u/239592/region01.png) into separated files.
How can I do this? I've read something about contours but I'm not sure how to use it. Ideally I would like to contour the octagon.
Thanks a lot for your help!

If template matching is working for you, stick to it. For instance, I considered the following template:
Then, we can pre-process the input in order to make it a binary one and discard small components. After this step, the template matching is performed. Then it is a matter of filtering the matches by means of discarding close ones (I've used a dummy method for that, so if there are too many matches you could see it taking some time). After we decide which points are far apart (and thus identify different hexagons), we can do minor adjusts to them in the following manner:
Sort by y-coordinate;
If two adjacent items start at a y-coordinate that is too close, then set them both to the same y-coord.
Now you can sort this point list in an appropriate order such that the crops are done in raster order. The cropping part is easily achieved using slicing provided by numpy.
import sys
import cv2
import numpy
outbasename = 'hexagon_%02d.png'
img = cv2.imread(sys.argv[1])
template = cv2.cvtColor(cv2.imread(sys.argv[2]), cv2.COLOR_BGR2GRAY)
theight, twidth = template.shape[:2]
# Binarize the input based on the saturation and value.
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
saturation = hsv[:,:,1]
value = hsv[:,:,2]
value[saturation > 35] = 255
value = cv2.threshold(value, 0, 255, cv2.THRESH_OTSU)[1]
# Pad the image.
value = cv2.copyMakeBorder(255 - value, 3, 3, 3, 3, cv2.BORDER_CONSTANT, value=0)
# Discard small components.
img_clean = numpy.zeros(value.shape, dtype=numpy.uint8)
contours, _ = cv2.findContours(value, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
for i, c in enumerate(contours):
area = cv2.contourArea(c)
if area > 500:
cv2.drawContours(img_clean, contours, i, 255, 2)
def closest_pt(a, pt):
if not len(a):
return (float('inf'), float('inf'))
d = a - pt
return a[numpy.argmin((d * d).sum(1))]
match = cv2.matchTemplate(img_clean, template, cv2.TM_CCORR_NORMED)
# Filter matches.
threshold = 0.8
dist_threshold = twidth / 1.5
loc = numpy.where(match > threshold)
ptlist = numpy.zeros((len(loc[0]), 2), dtype=int)
count = 0
print "%d matches" % len(loc[0])
for pt in zip(*loc[::-1]):
cpt = closest_pt(ptlist[:count], pt)
dist = ((cpt[0] - pt[0]) ** 2 + (cpt[1] - pt[1]) ** 2) ** 0.5
if dist > dist_threshold:
ptlist[count] = pt
count += 1
# Adjust points (could do for the x coords too).
ptlist = ptlist[:count]
view = ptlist.ravel().view([('x', int), ('y', int)])
view.sort(order=['y', 'x'])
for i in xrange(1, ptlist.shape[0]):
prev, curr = ptlist[i - 1], ptlist[i]
if abs(curr[1] - prev[1]) < 5:
y = min(curr[1], prev[1])
curr[1], prev[1] = y, y
# Crop in raster order.
view.sort(order=['y', 'x'])
for i, pt in enumerate(ptlist, start=1):
cv2.imwrite(outbasename % i,
img[pt[1]-2:pt[1]+theight-2, pt[0]-2:pt[0]+twidth-2])
print 'Wrote %s' % (outbasename % i)
If you want only the contours of the hexagons, then crop on img_clean instead of img (but then it is pointless to sort the hexagons in raster order).
Here is a representation of the different regions that would be cut for your two examples without modifying the code above:

I am sorry, I didn't understand from your question on how do you relate matchTemplate and Contours.
Anyway, below is a small technique using contours. It is on the assumption that your other images are also like the one you provided. I am not sure if it works with your other images. But I think it would help to get a startup. Try this yourself and make necessary adjustments and modifications.
What I did :
1 - I needed the edge of octagons . So Thresholded Image using Otsu and apply dilation and erosion (or use any method you like that works well for all your images, beware of the edges in left edge of image).
2 - Then found contours (More about contours : http://goo.gl/r0ID0
3 - For each contours, find its convex hull, find its area(A) & perimeter(P)
4 - For a perfect octagon, P*P/A = 13.25 approximately. I used it here and cut it and saved it.
5 - You can see cropping it also removes some edges of octagon. If you want it, adjust the cropping dimension.
Code :
import cv2
import numpy as np
img = cv2.imread('region01.png')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret,thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
thresh = cv2.dilate(thresh,None,iterations = 2)
thresh = cv2.erode(thresh,None)
contours,hierarchy = cv2.findContours(thresh,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
number = 0
for cnt in contours:
hull = cv2.convexHull(cnt)
area = cv2.contourArea(hull)
P = cv2.arcLength(hull,True)
if ((area != 0) and (13<= P**2/area <= 14)):
#cv2.drawContours(img,[hull],0,255,3)
x,y,w,h = cv2.boundingRect(hull)
number = number + 1
roi = img[y:y+h,x:x+w]
cv2.imshow(str(number),roi)
cv2.imwrite("1"+str(number)+".jpg",roi)
cv2.imshow('img',img)
cv2.waitKey(0)
cv2.destroyAllWindows()
Those 6 octagons will be stored as separate files.
Hope it helps !!!