We Keep Coding

Circle-like shape recognition

I am trying to build a program that can segment the area within these circle-like shapes. All the images will have a white background, and 5 experimental conditions that consist of either red or yellow color circular-like shaped liquids. For instance,
Yellow image - original
Red image - original
I want to extract the regions within the 5 round-ish circles to do further analysis, but I am stuck in this first step.
What I want is something like this
Yellow image - output
This is circled out manually, but I want a program that can do this automatically.
I have tried using cv2's find contours and hough circles, but for find contours, perhaps the colors of my experimental conditions are not too drastically different from white, so it cannot detect the contours accurately. And for hough circles, it can only detect perfect circles, so my circle-ish shapes cannot be detected.
Example -- red using find contours (not defined outline)
Example -- red using hough circles (can only detect perfect circles)
Here is the code for my find contours
image= cv2.imread('red.jpg')
gray= cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
edges= cv2.Canny(gray,30,200)
contours, hierarchy= cv2.findContours(edges, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
cv2.drawContours(image, contours, -1, (0,255,0),3)
cv2.imshow('All Contours', image)
cv2.waitKey(0)
cv2.destroyAllWindows()
Code for hough circles
experiment = cv2.imread('red.jpg')
gray = cv2.cvtColor(experiment, cv2.COLOR_BGR2GRAY)
img = cv2.medianBlur(gray, 5)
cimg = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
circles = cv2.HoughCircles(img, cv2.HOUGH_GRADIENT, 1, 120, param1 = 100, param2 = 30, minRadius = 0, maxRadius = 0)
circles = np.uint(np.around(circles))
for i in circles[0, :]:
cv2.circle(experiment, (i[0], i[1]), i[2], (0, 255, 0), 2)
cv2.imshow("Detection results", experiment)
cv2.waitKey(0)
cv2.destroyAllWindows()
What should I do? Perhaps, I am thinking I can just simply remove all the white background, but how can I do that and how do I save each cropped experimental condition into its own file subsequently?

Here is one possible way to do that in Python/OpenCV.
Read the input
Convert to grayscale
Blur
Divide the gray image by the blurred image to do division normalization (whiten the background)
Stretch the normalized image to full dynamic range
Do adaptive thresholding
Apply morphology close to connect the boundaries of the circles
Get the external contours
Loop over the contours filtering out those smaller than some area threshold, then fit the remaining contours to ellipses, print the ellipse parameters, draw the contours on a copy of the input and also draw the ellipses on a copy of the input
Save the results
Input:
import cv2
import numpy as np
import skimage.exposure
# read the input
img = cv2.imread('circles5.jpg')
# convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# blur
blur = cv2.GaussianBlur(gray, (0,0), sigmaX=99, sigmaY=99)
# do division normalization
normal = cv2.divide(gray, blur, scale=255)
# stretch to full dynamic range
stretch = skimage.exposure.rescale_intensity(normal, in_range='image', out_range=(0,255)).astype(np.uint8)
# adaptive threshold
thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 25, 6)
# apply morphology close
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (11,11))
morph = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
# get external contours
contours = cv2.findContours(morph, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
# filter out small contours and fit ellipse
# draw contour on copy of image and also draw ellipse
result1 = img.copy()
result2 = img.copy()
i = 1
for cntr in contours:
area = cv2.contourArea(cntr)
if area > 10000:
ellipse = cv2.fitEllipse(cntr)
(xc,yc),(d1,d2),angle = ellipse
print('ellipse #:', i)
print('center:', xc,yc)
print('diameters:', d1,d2)
print('angle:', angle)
print('')
cv2.drawContours(result1, [cntr], 0, (0,0,255), 1)
cv2.ellipse(result2, (int(xc),int(yc)), (int(d1/2),int(d2/2)), angle, 0, 360, (0,0,255), 1)
i = i + 1
# save results
cv2.imwrite('circles5_division_normalized.jpg', normal)
cv2.imwrite('circles5_stretched.jpg', stretch)
cv2.imwrite('circles5_thresholded.jpg', thresh)
cv2.imwrite('circles5_morph.jpg', morph)
cv2.imwrite('circles5_contours.jpg', result1)
cv2.imwrite('circles5_ellipses.jpg', result2)
# show results
cv2.imshow('gray', gray)
cv2.imshow('blur', blur)
cv2.imshow('normalized', normal)
cv2.imshow('stretched', stretch)
cv2.imshow('thresh', thresh)
cv2.imshow('morph', morph)
cv2.imshow('contours', result1)
cv2.imshow('ellipses', result2)
cv2.waitKey(0)
Normalized Image:
Stretched Image:
Adaptive Thresholded Image:
Morphology Closed Image:
Contours on Input:
Ellipses on Input:
Ellipse Parameters:
ellipse #: 1
center: 798.6463012695312 267.5460510253906
diameters: 145.31993103027344 159.45236206054688
angle: 91.30252075195312
ellipse #: 2
center: 1036.6676025390625 252.74435424804688
diameters: 141.51364135742188 151.05157470703125
angle: 138.46131896972656
ellipse #: 3
center: 344.023681640625 241.0209197998047
diameters: 137.6905517578125 141.6817626953125
angle: 143.69598388671875
ellipse #: 4
center: 99.26034545898438 250.16726684570312
diameters: 146.94293212890625 160.36122131347656
angle: 166.9673614501953
ellipse #: 5
center: 585.333251953125 231.5453338623047
diameters: 130.96046447753906 175.29257202148438
angle: 143.37709045410156

How to crop image to only text section with Python OpenCV?

I want to crop the image to only extract the text sections. There are thousands of them with different sizes so I can't hardcode coordinates. I'm trying to remove the unwanted lines on the left and on the bottom. How can I do this?
Original
Expected

Determine the least spanning bounding box by finding all the non-zero points in the image. Finally, crop your image using this bounding box. Finding the contours is time-consuming and unnecessary here, especially because your text is axis-aligned. You may accomplish your goal by combining cv2.findNonZero and cv2.boundingRect.
Hope this will work ! :
import numpy as np
import cv2
img = cv2.imread(r"W430Q.png")
# Read in the image and convert to grayscale
img = img[:-20, :-20] # Perform pre-cropping
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = 255*(gray < 50).astype(np.uint8) # To invert the text to white
gray = cv2.morphologyEx(gray, cv2.MORPH_OPEN, np.ones(
(2, 2), dtype=np.uint8)) # Perform noise filtering
coords = cv2.findNonZero(gray) # Find all non-zero points (text)
x, y, w, h = cv2.boundingRect(coords) # Find minimum spanning bounding box
# Crop the image - note we do this on the original image
rect = img[y:y+h, x:x+w]
cv2.imshow("Cropped", rect) # Show it
cv2.waitKey(0)
cv2.destroyAllWindows()
in above code from forth line of code is where I set the threshold below 50 to make the dark text white. However, because this outputs a binary image, I convert to uint8, then scale by 255. The text is effectively inverted.
Then, using cv2.findNonZero, we discover all of the non-zero locations for this image.We then passed this to cv2.boundingRect, which returns the top-left corner of the bounding box, as well as its width and height. Finally, we can utilise this to crop the image. This is done on the original image, not the inverted version.

Here's a simple approach:
Obtain binary image. Load the image, grayscale, Gaussian blur, then Otsu's threshold to obtain a binary black/white image.
Remove horizontal lines. Since we're trying to only extract text, we remove horizontal lines to aid us in our next step so incorrect contours will not merge together.
Merge text into a single contour. The idea is that characters which are adjacent to each other are part of the wall of text. So we can dilate individual contours together to obtain a single contour to extract.
Find contours and extract ROI. We find contours, sort contours by area, then extract the largest contour ROI using Numpy slicing.
Here's the visualization of each step:
Binary image -> Removed horizontal lines in green
1
2
Dilate to combine into a single contour -> Detected ROI to extract in green
3
4
Result
Code
import cv2
import numpy as np
# Load image, grayscale, Gaussian blur, Otsu's threshold
image = cv2.imread('1.png')
original = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (3, 3), 0)
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
# Remove horizontal lines
horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (25,1))
detected_lines = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, horizontal_kernel, iterations=1)
cnts = cv2.findContours(detected_lines, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
cv2.drawContours(thresh, [c], -1, 0, -1)
# Dilate to merge into a single contour
vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2,30))
dilate = cv2.dilate(thresh, vertical_kernel, iterations=3)
# Find contours, sort for largest contour and extract ROI
cnts, _ = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2:]
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:-1]
for c in cnts:
x,y,w,h = cv2.boundingRect(c)
cv2.rectangle(image, (x, y), (x + w, y + h), (36,255,12), 4)
ROI = original[y:y+h, x:x+w]
break
cv2.imshow('image', image)
cv2.imshow('dilate', dilate)
cv2.imshow('thresh', thresh)
cv2.imshow('ROI', ROI)
cv2.waitKey()

Using OpenCV to find a rotated rectangle of a certain aspect ratio?

I am not having difficulty transforming a found box, it is the fact that I am not able to detect the box in the first place when it is at an angle.
Here is a sample image I want the largest ~1230:123 rectangle in the image the problem is the rectangle can be rotated.
Here is a picture of a rotated barcode that I am unable to detect:
The function I have been using to process uses contour area just looks for the largest rectangle.
What methods should I use to look for a rotated rectangle so that even when rotated I can detect it?
#PYTHON 3.6 Snippet for Image Processing
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# compute the Scharr gradient magnitude representation of the images
# in both the x and y direction using OpenCV 2.4
ddepth = cv2.cv.CV_32F if imutils.is_cv2() else cv2.CV_32F
gradX = cv2.Sobel(gray, ddepth=ddepth, dx=1, dy=0, ksize=-1)
gradY = cv2.Sobel(gray, ddepth=ddepth, dx=0, dy=1, ksize=-1)
# subtract the y-gradient from the x-gradient
gradient = cv2.subtract(gradX, gradY)
gradient = cv2.convertScaleAbs(gradient)
# blur and threshold the image
blurred = cv2.blur(gradient, (8, 8))
(_, thresh) = cv2.threshold(blurred, 225, 255, cv2.THRESH_BINARY)
# construct a closing kernel and apply it to the thresholded image
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (21, 7))
closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
# perform a series of erosions and dilations
closed = cv2.erode(closed, None, iterations = 4)
closed = cv2.dilate(closed, None, iterations = 4)
# find the contours in the thresholded image, then sort the contours
# by their area, keeping only the largest one
cnts = cv2.findContours(closed.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
c = sorted(cnts, key = cv2.contourArea, reverse = True)[0]
# compute the rotated bounding box of the largest contour
rect = cv2.minAreaRect(c)

You don't need all the preprocessing (like Sobel, erode, dilate) for finding before executing findContours.
findContours works better when contours are full (filled with white color) instead of having just the edges.
I suppose you can keep the code from cv2.findContours to the end, and get the result you are looking for.
You may use the following stages:
Apply binary threshold using Otsu's thresholding (just in case image is not a binary image).
Execute cv2.findContours, and Find the contour with the maximum area.
Use cv2.minAreaRect for finding the minimum area bounding rectangle.
Here is a code sample:
import numpy as np
import cv2
img = cv2.imread('img.png', cv2.IMREAD_GRAYSCALE) # Read input image as gray-scale
ret, img = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU) # Apply threshold using Otsu's thresholding (just in case image is not a binary image).
# Find contours in img.
cnts = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[-2] # [-2] indexing takes return value before last (due to OpenCV compatibility issues).
# Find the contour with the maximum area.
c = max(cnts, key=cv2.contourArea)
# Find the minimum area bounding rectangle
# https://stackoverflow.com/questions/18207181/opencv-python-draw-minarearect-rotatedrect-not-implemented
rect = cv2.minAreaRect(c)
box = cv2.boxPoints(rect)
box = np.int0(box)
# Convert image to BGR (just for drawing a green rectangle on it).
bgr_img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
cv2.drawContours(bgr_img, [box], 0, (0, 255, 0), 2)
# Show images for debugging
cv2.imshow('bgr_img', bgr_img)
cv2.waitKey()
cv2.destroyAllWindows()
Result:
Note: The largest contour seems to be a parallelogram and not a perfect rectangle.

How to enclose the irregular figure contour and fill it with with 5px dots in opencv using python?

This is my
I want to get this
but the problem is I am not able to enclose the contour and how should I add these dots?
Does Open cv have any such function to handle this?
So basically,
The first problem is how to enclose this image
Second, how to add Dots.
Thank you

Here is one way to do that in Python/OpenCV. However, I cannot close your dotted outline without connecting separate regions. But it will give you some idea how to proceed with most of what you want to do.
If you manually add a few more dots to your input image where there are large gaps, then the morphology kernel can be made smaller such that it can connected the regions without merging separate parts that should remain isolated.
Read the input
Convert to grayscale
Threshold to binary
Apply morphology close to try to close the dotted outline. Unfortunately it connected separate regions.
Get the external contours
Draw white filled contours on a black background as a mask
Draw a single black circle on a white background
Tile out the circle image to the size of the input
Mask the tiled circle image with the filled contour image
Save results
Input:
import cv2
import numpy as np
import math
# read input image
img = cv2.imread('island.png')
hh, ww = img.shape[:2]
# convert img to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# threshold
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY)[1]
# use morphology to close figure
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (35,35))
morph = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel, )
# find contours and bounding boxes
mask = np.zeros_like(thresh)
contours = cv2.findContours(morph, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
for cntr in contours:
cv2.drawContours(mask, [cntr], 0, 255, -1)
# create a single tile as black circle on white background
circle = np.full((11,11), 255, dtype=np.uint8)
circle = cv2.circle(circle, (7,7), 3, 0, -1)
# tile out the tile pattern to the size of the input
numht = math.ceil(hh / 11)
numwd = math.ceil(ww / 11)
tiled_circle = np.tile(circle, (numht,numwd))
tiled_circle = tiled_circle[0:hh, 0:ww]
# composite tiled_circle with mask
result = cv2.bitwise_and(tiled_circle, tiled_circle, mask=mask)
# save result
cv2.imwrite("island_morph.jpg", morph)
cv2.imwrite("island_mask.jpg", mask)
cv2.imwrite("tiled_circle.jpg", tiled_circle)
cv2.imwrite("island_result.jpg", result)
# show images
cv2.imshow("morph", morph)
cv2.imshow("mask", mask)
cv2.imshow("tiled_circle", tiled_circle)
cv2.imshow("result", result)
cv2.waitKey(0)
Morphology connected image:
Contour Mask image:
Tiled circles:
Result:

Extract building edges from map image using Python

I got a map image here.
I need to extract the edges of buildings for further process, the result would be like step 2 for the post here.
Since I am not familiar with this field, can this be done by libraries such as OpenCV?

Seems you want to select individual buildings, so I used color separation. The walls are darker, which makes for good separation in the HSV colorspace. Note that the final result can be improved by zooming in more and/or by using an imagetype with less compression, such as PNG.
Select walls
First I determined good values for separation. For that I used this script. I found that the best result would be to separate the yellow and the gray separately and then combine the resulting masks. Not all walls closed perfectly, so I improved the result by closing the mask a bit. The result is a mask that displays all walls:
Left to right: Yellow mask, Gray mask, Combined and solidified mask
Find buildings
Next I used findCountours to separate out buildings. Since the wall contours will probably not be very useful (as walls are interconnected), I used the hierarchy to find the 'lowest' contours (that have no other contours inside of them). These are the buildings.
Result of findContours: the outline of all contours in green, the outline of individual buildings in red
Note that buildings on the edge are not detected. This is because using this technique they are not a separate contour, but part of the exterior of the image. This can be solve this by drawing a rectangle in gray on the border of the image. You may not want this in your final application, but I included it in case you do.
Code:
import cv2
import numpy as np
#load image and convert to hsv
img = cv2.imread("fLzI9.jpg")
# draw gray box around image to detect edge buildings
h,w = img.shape[:2]
cv2.rectangle(img,(0,0),(w-1,h-1), (50,50,50),1)
# convert image to HSV
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# define color ranges
low_yellow = (0,28,0)
high_yellow = (27,255,255)
low_gray = (0,0,0)
high_gray = (179,255,233)
# create masks
yellow_mask = cv2.inRange(hsv, low_yellow, high_yellow )
gray_mask = cv2.inRange(hsv, low_gray, high_gray)
# combine masks
combined_mask = cv2.bitwise_or(yellow_mask, gray_mask)
kernel = np.ones((3,3), dtype=np.uint8)
combined_mask = cv2.morphologyEx(combined_mask, cv2.MORPH_DILATE,kernel)
# findcontours
contours, hier = cv2.findContours(combined_mask,cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# find and draw buildings
for x in range(len(contours)):
# if a contour has not contours inside of it, draw the shape filled
c = hier[0][x][2]
if c == -1:
cv2.drawContours(img,[contours[x]],0,(0,0,255),-1)
# draw the outline of all contours
for cnt in contours:
cv2.drawContours(img,[cnt],0,(0,255,0),2)
# display result
cv2.imshow("Result", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
Result:
With buildings drawn solid red and all contours as green overlay

Here's a simple approach
Convert image to grayscale and Gaussian blur to smooth edges
Threshold image
Perform Canny edge detection
Find contours and draw contours
Threshold image using cv2.threshold()
Perform Canny edge detection with cv2.Canny()
Find contours using cv2.findContours() and cv2.drawContours()
import cv2
image = cv2.imread('1.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (3, 3), 0)
thresh = cv2.threshold(blurred, 240 ,255, cv2.THRESH_BINARY_INV)[1]
canny = cv2.Canny(thresh, 50, 255, 1)
cnts = cv2.findContours(canny, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
cv2.drawContours(image,[c], 0, (36,255,12), 2)
cv2.imshow('thresh', thresh)
cv2.imshow('canny', canny)
cv2.imshow('image', image)
cv2.imwrite('thresh.png', thresh)
cv2.imwrite('canny.png', canny)
cv2.imwrite('image.png', image)
cv2.waitKey(0)