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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)

Find contour of rectangle in object

I would like to find the contour of the rectangular photograph inside of the object. I've tried using the corner detection feature of OpenCV, but to no avail. I also tried to find all the contours using findContours, and filter out the contours with more (or less) than 4 edges, but this also didn't lead anywhere.
I have a sample scan here.

I have a solution for you, but it involves a lot of steps. Also, it may not generalize that well. It does work pretty good for your image though.
First a grayscale and threshold is made and findContours is used to create a mask of the paper area. That mask is inverted and combined with the original image, which makes the black edges white. A new grayscale and threshold is made on the resulting image, which is then inverted so findContours can find the dark pixels of the photo. A rotated box around the largest contours is selected, which is the area you seek.
I added a little extra, which you may not need, but could be convenient: perspectivewarp is applied to the box, so the area you want is made into a straight rectangle.
There is quite a lot happening, so I advise you to take some time a look at the intermediate steps, to understand what happens.
Result:
Code:
import numpy as np
import cv2
# load image
image = cv2.imread('photo.jpg')
# resize to easily view on screen, remove for final processing
image = cv2.resize(image,None,fx=0.2, fy=0.2, interpolation = cv2.INTER_CUBIC)
### remove outer black edge
# create grayscale
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# perform threshold
retr , mask = cv2.threshold(gray_image, 190, 255, cv2.THRESH_BINARY)
# remove noise
kernel = np.ones((5,5),np.uint8)
mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
# create emtpy mask
mask_2 = np.zeros(image.shape[:3], dtype=image.dtype)
# find contours
ret, contours, hier = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# draw the found shapes (white, filled in ) on the empty mask
for cnt in contours:
cv2.drawContours(mask_2, [cnt], 0, (255,255,255), -1)
# invert mask and combine with original image - this makes the black outer edge white
mask_inv_2 = cv2.bitwise_not(mask_2)
tmp = cv2.bitwise_or(image, mask_inv_2)
### Select photo - not inner edge
# create grayscale
gray_image2 = cv2.cvtColor(tmp, cv2.COLOR_BGR2GRAY)
# perform threshold
retr, mask3 = cv2.threshold(gray_image2, 190, 255, cv2.THRESH_BINARY)
# remove noise
maskX = cv2.morphologyEx(mask3, cv2.MORPH_CLOSE, kernel)
# invert mask, so photo area can be found with findcontours
maskX = cv2.bitwise_not(maskX)
# findcontours
ret, contours2, hier = cv2.findContours(maskX, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# select the largest contour
largest_area = 0
for cnt in contours2:
if cv2.contourArea(cnt) > largest_area:
cont = cnt
largest_area = cv2.contourArea(cnt)
# find the rectangle (and the cornerpoints of that rectangle) that surrounds the contours / photo
rect = cv2.minAreaRect(cont)
box = cv2.boxPoints(rect)
box = np.int0(box)
print(rect)
#### Warp image to square
# assign cornerpoints of the region of interest
pts1 = np.float32([box[1],box[0],box[2],box[3]])
# provide new coordinates of cornerpoints
pts2 = np.float32([[0,0],[0,450],[630,0],[630,450]])
# determine and apply transformationmatrix
M = cv2.getPerspectiveTransform(pts1,pts2)
result = cv2.warpPerspective(image,M,(630,450))
#draw rectangle on original image
cv2.drawContours(image, [box], 0, (255,0,0), 2)
#show image
cv2.imshow("Result", result)
cv2.imshow("Image", image)
cv2.waitKey(0)
cv2.destroyAllWindows()

How to discard the edges of an image using opencv?

I'm pre-processing some images in order to remove the background from my area of ​​interest. However, the images on my bench have rounded edges due to the focus of the camera. How do I discard these rounded edges and be able to remove only my object of interest from the image? The code below I can remove the background of the image, but it does not work right due to the edges around.
import numpy as np
import cv2
#Read the image and perform threshold and get its height and weight
img = cv2.imread('IMD408.bmp')
h, w = img.shape[:2]
# Transform to gray colorspace and blur the image.
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray,(5,5),0)
# Make a fake rectangle arround the image that will seperate the main contour.
cv2.rectangle(blur, (0,0), (w,h), (255,255,255), 10)
# Perform Otsu threshold.
_,thresh = cv2.threshold(blur,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
# Create a mask for bitwise operation
mask = np.zeros((h, w), np.uint8)
# Search for contours and iterate over contours. Make threshold for size to
# eliminate others.
contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
for i in contours:
cnt = cv2.contourArea(i)
if 1000000 >cnt > 100000:
cv2.drawContours(mask, [i],-1, 255, -1)
# Perform the bitwise operation.
res = cv2.bitwise_and(img, img, mask=mask)
# Display the result.
cv2.imwrite('IMD408.png', res)
cv2.imshow('img', res)
cv2.waitKey(0)
cv2.destroyAllWindows()
input image:
Exit:
Error:

Since you mentioned that all the images have the same hue, then this should work well for them. Steps is to do some white balancing which will increase the contrast a bit.
Get the greyscale.
Threshold the grayscale image. Values less than 127 are set to 255 (white). This will give you a binary image, which will become a mask for the original image.
Apply the mask
You might have to play around with the thresholding if you want better results, here is the link for that. But this should get you started. I'm using a different OpenCV version compared to you might have to tweak the code a bit.
import cv2
def equaliseWhiteBalance(image):
''' Return equilised WB of an image '''
wb = cv2.xphoto.createSimpleWB() #Create WB Object
imgWB = wb.balanceWhite(img) #Balance White on image
r,g,b = cv2.split(imgWB) #Get individual r,g,b channels
r_equ = cv2.equalizeHist(r) #Equalise RED channel
g_equ = cv2.equalizeHist(g) #Equalise GREEN channel
b_equ = cv2.equalizeHist(b) #Equalise BLUE channel
img_equ_WB = cv2.merge([r_equ,g_equ,b_equ]) #Merge equalised channels
return imgWB
#Read the image
img = cv2.imread('IMD408.bmp')
result = img.copy()
#Get whiteBalance of image
imgWB = equaliseWhiteBalance(img)
cv2.imshow('img', imgWB)
cv2.waitKey(0)
# Get gray image
gray = cv2.cvtColor(imgWB,cv2.COLOR_RGB2GRAY)
cv2.imshow('img', gray)
cv2.waitKey(0)
# Perform threshold
_, thresh = cv2.threshold(gray,127,255,cv2.THRESH_BINARY)
cv2.imshow('img', thresh)
cv2.waitKey(0)
# Apply mask
result[thresh!=0] = (255,255,255)
cv2.imshow('img', result)
cv2.waitKey(0)
If all the dark corner vignettes have different sizes per image, then I suggest looking for centroid of contours on the binary (mask) image. Centroids with a 'short' distance to any corner of your image will be the dark vignettes, so their value can be changed from black to white.

How to improve edge detection and remove background from an image?

I am using the code below to remove the backroung of the images and highlight only my region of interest (ROI), however, the algorithm behaves in a wrong way in some images, discarding the stain (ROI) and deleting along with the background.
import numpy as np
import cv2
#Read the image and perform threshold
img = cv2.imread('photo.bmp')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
blur = cv2.medianBlur(gray,5)
_,thresh = cv2.threshold(blur,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
#Search for contours and select the biggest one
contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
cnt = max(contours, key=cv2.contourArea)
#Create a new mask for the result image
h, w = img.shape[:2]
mask = np.zeros((h, w), np.uint8)
#Draw the contour on the new mask and perform the bitwise operation
cv2.drawContours(mask, [cnt],-1, 255, -1)
res = cv2.bitwise_and(img, img, mask=mask)
#Display the result
cv2.imwrite('photo.png', res)
#cv2.imshow('img', res)
cv2.waitKey(0)
cv2.destroyAllWindows()

I don't know if I understand correctly because when I run your code I do not get the output you posted (exit). If you would like to obtain only the mole it can't be done by simply thresholding because the mole is too near the border plus if you look at your image closley you will see that it has some sort of frame. However there is a simple way to do this for this image but it may not work in other cases. You can draw a fake border over your image and seperate the ROI from other noise area. Then make a threshold for which contour you wish to display. Cheers!
Example:
#Import all necessery libraries
import numpy as np
import cv2
#Read the image and perform threshold and get its height and weight
img = cv2.imread('moles.png')
h, w = img.shape[:2]
# Transform to gray colorspace and blur the image.
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray,(5,5),0)
# Make a fake rectangle arround the image that will seperate the main contour.
cv2.rectangle(blur, (0,0), (w,h), (255,255,255), 10)
# Perform Otsu threshold.
_,thresh = cv2.threshold(blur,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
# Create a mask for bitwise operation
mask = np.zeros((h, w), np.uint8)
# Search for contours and iterate over contours. Make threshold for size to
# eliminate others.
_, contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
for i in contours:
cnt = cv2.contourArea(i)
if 1000000 >cnt > 100000:
cv2.drawContours(mask, [i],-1, 255, -1)
# Perform the bitwise operation.
res = cv2.bitwise_and(img, img, mask=mask)
# Display the result.
cv2.imwrite('mole_res.jpg', res)
cv2.imshow('img', res)
cv2.waitKey(0)
cv2.destroyAllWindows()
Result:

How to crop the internal area of a contour?

I am working on Retinal fundus images.The image consists of a circular retina on a black background. With OpenCV, I have managed to get a contour which surrounds the whole circular Retina. What I need is to crop out the circular retina from the black background.

It is unclear in your question whether you want to actually crop out the information that is defined within the contour or mask out the information that isn't relevant to the contour chosen. I'll explore what to do in both situations.
Masking out the information
Assuming you ran cv2.findContours on your image, you will have received a structure that lists all of the contours available in your image. I'm also assuming that you know the index of the contour that was used to surround the object you want. Assuming this is stored in idx, first use cv2.drawContours to draw a filled version of this contour onto a blank image, then use this image to index into your image to extract out the object. This logic masks out any irrelevant information and only retain what is important - which is defined within the contour you have selected. The code to do this would look something like the following, assuming your image is a grayscale image stored in img:
import numpy as np
import cv2
img = cv2.imread('...', 0) # Read in your image
# contours, _ = cv2.findContours(...) # Your call to find the contours using OpenCV 2.4.x
_, contours, _ = cv2.findContours(...) # Your call to find the contours
idx = ... # The index of the contour that surrounds your object
mask = np.zeros_like(img) # Create mask where white is what we want, black otherwise
cv2.drawContours(mask, contours, idx, 255, -1) # Draw filled contour in mask
out = np.zeros_like(img) # Extract out the object and place into output image
out[mask == 255] = img[mask == 255]
# Show the output image
cv2.imshow('Output', out)
cv2.waitKey(0)
cv2.destroyAllWindows()
If you actually want to crop...
If you want to crop the image, you need to define the minimum spanning bounding box of the area defined by the contour. You can find the top left and lower right corner of the bounding box, then use indexing to crop out what you need. The code will be the same as before, but there will be an additional cropping step:
import numpy as np
import cv2
img = cv2.imread('...', 0) # Read in your image
# contours, _ = cv2.findContours(...) # Your call to find the contours using OpenCV 2.4.x
_, contours, _ = cv2.findContours(...) # Your call to find the contours
idx = ... # The index of the contour that surrounds your object
mask = np.zeros_like(img) # Create mask where white is what we want, black otherwise
cv2.drawContours(mask, contours, idx, 255, -1) # Draw filled contour in mask
out = np.zeros_like(img) # Extract out the object and place into output image
out[mask == 255] = img[mask == 255]
# Now crop
(y, x) = np.where(mask == 255)
(topy, topx) = (np.min(y), np.min(x))
(bottomy, bottomx) = (np.max(y), np.max(x))
out = out[topy:bottomy+1, topx:bottomx+1]
# Show the output image
cv2.imshow('Output', out)
cv2.waitKey(0)
cv2.destroyAllWindows()
The cropping code works such that when we define the mask to extract out the area defined by the contour, we additionally find the smallest horizontal and vertical coordinates which define the top left corner of the contour. We similarly find the largest horizontal and vertical coordinates that define the bottom left corner of the contour. We then use indexing with these coordinates to crop what we actually need. Note that this performs cropping on the masked image - that is the image that removes everything but the information contained within the largest contour.
Note with OpenCV 3.x
It should be noted that the above code assumes you are using OpenCV 2.4.x. Take note that in OpenCV 3.x, the definition of cv2.findContours has changed. Specifically, the output is a three element tuple output where the first image is the source image, while the other two parameters are the same as in OpenCV 2.4.x. Therefore, simply change the cv2.findContours statement in the above code to ignore the first output:
_, contours, _ = cv2.findContours(...) # Your call to find contours

Here's another approach to crop out a rectangular ROI. The main idea is to find the edges of the retina using Canny edge detection, find contours, and then extract the ROI using Numpy slicing. Assuming you have an input image like this:
Extracted ROI
import cv2
# Load image, convert to grayscale, and find edges
image = cv2.imread('1.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_OTSU + cv2.THRESH_BINARY)[1]
# Find contour and sort by contour area
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)
# Find bounding box and extract ROI
for c in cnts:
x,y,w,h = cv2.boundingRect(c)
ROI = image[y:y+h, x:x+w]
break
cv2.imshow('ROI',ROI)
cv2.imwrite('ROI.png',ROI)
cv2.waitKey()

This is a pretty simple way. Mask the image with transparency.
Read the image
Make a grayscale version.
Otsu Threshold
Apply morphology open and close to thresholded image as a mask
Put the mask into the alpha channel of the input
Save the output
Input
Code
import cv2
import numpy as np
# load image as grayscale
img = cv2.imread('retina.jpeg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# threshold input image using otsu thresholding as mask and refine with morphology
ret, mask = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
kernel = np.ones((9,9), np.uint8)
mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
# put mask into alpha channel of result
result = img.copy()
result = cv2.cvtColor(result, cv2.COLOR_BGR2BGRA)
result[:, :, 3] = mask
# save resulting masked image
cv2.imwrite('retina_masked.png', result)
Output