I have this image:
(or this..)
How can I set to white all the area outside the boundingBox'es ?
I would like to obtain this result:
Thanks
As mentioned in the comments if you have the positions of the ROIs, you can use them to paste them on the an image with white background having the same shape as the original.
import cv2
import numpy as np
image = cv2.imread(r'C:\Users\Desktop\rus.png')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
white_bg = 255*np.ones_like(image)
ret, thresh = cv2.threshold(gray, 60, 255, cv2.THRESH_BINARY_INV)
blur = cv2.medianBlur(thresh, 1)
kernel = np.ones((10, 20), np.uint8)
img_dilation = cv2.dilate(blur, kernel, iterations=1)
im2, ctrs, hier = cv2.findContours(img_dilation.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
sorted_ctrs = sorted(ctrs, key=lambda ctr: cv2.boundingRect(ctr)[0])
for i, ctr in enumerate(sorted_ctrs):
# Get bounding box
x, y, w, h = cv2.boundingRect(ctr)
roi = image[y:y + h, x:x + w]
if (h > 50 and w > 50) and h < 200:
cv2.rectangle(image, (x, y), (x + w, y + h), (255, 255, 255), 1)
cv2.imshow('{}.png'.format(i), roi)
#--- paste ROIs on image with white background
white_bg[y:y+h, x:x+w] = roi
cv2.imshow('white_bg_new', white_bg)
cv2.waitKey(0)
cv2.destroyAllWindows()
The result:
Take a mask with the dimensions same as that of your image. The mask is an array with values 255 (white). I assume if you are drawing the bounding box, you definitely have the coordinates for each of them. For each of the bounding box you simply replace the region in the mask with the region bounded by the bounding box as below:
mask[y:y+h,x:x+w] = image[y:y+h,x:x+w], where mask is your final output with your desired result and image is your input image on which the processing is to take place. The values x,y,w,h is the same for both the image as we have made sure that the dimensions for the mask and input image are the same.
Related
I am trying to get the maximum area object in an image.
I applied a Blur Kernel 5x5 then I applied the Canny algo to get the edges. Then I used the findContours method and the max contourArea but it returns the wrong object.
Base Image:
Canny Image:
Image with all contours found:
Max area object:
As you can see it has to return the left box but it returns the right one.
I think the problem is that left and right boxes share a common edge but it seems that belongs only to the left one.
This is the code snippet:
img_rgb = cv.imread(img_path)
gray = cv.cvtColor(img_rgb, cv.COLOR_BGR2GRAY)
# blur with a kernel size of 5x5
blur = cv.GaussianBlur(gray, (5, 5), 0)
canny = cv.Canny(blur, 50, 50)
#saving canny image
cv.imwrite("canny.png", canny)
_, thresh = cv.threshold(canny, 127, 255, 0)
contours, _ = cv.findContours(thresh, cv.RETR_TREE, cv.CHAIN_APPROX_NONE)
for c in contours:
cv.drawContours(img_rgb, [c], 0, (randrange(255), randrange(255), randrange(255)), 3)
#saving image with contours
cv.imwrite("contours.png", img_rgb)
max_area_contour = max(contours, key=cv.contourArea)
x, y, w, h = cv.boundingRect(max_area_contour)
cv.rectangle(img_rgb, (x, y), (x + w, y + h), (0, 255, 0), 3)
#saving the image with the biggest contour
cv.imwrite("max_contour.png", img_rgb)
I made key function for max as a square of bounding rect and then it works fine:
img_rgb = cv.imread(img_path)
img_rgb_init = img_rgb.copy()
gray = cv.cvtColor(img_rgb, cv.COLOR_BGR2GRAY)
# blur with a kernel size of 5x5
blur = cv.GaussianBlur(gray, (5, 5), 0)
canny = cv.Canny(blur, 50, 50)
#saving canny image
# cv.imwrite("canny.png", canny)
_, thresh = cv.threshold(canny, 127, 255, 0)
contours, _ = cv.findContours(thresh, cv.RETR_TREE, cv.CHAIN_APPROX_NONE)
for c in contours:
cv.drawContours(img_rgb, [c], 0, (random.randrange(255), random.randrange(255), random.randrange(255)), 3)
#saving image with contours
# cv.imwrite("contours.png", img_rgb)
def bounding_rect_size(in_cnt):
x, y, w, h =cv.boundingRect(in_cnt)
return w*h
x, y, w, h = cv.boundingRect(max(contours,key=bounding_rect_size))
cv.rectangle(img_rgb_init, (x, y), (x + w, y + h), (0, 255, 0), 3)
cv.imshow("",img_rgb_init)
cv.waitKey()
The reason it was working incorrect that the contour of bigest rectangle was looking like train railways. In one direction from one side of rectangle line and in other direction from other side (the line somewhere is broken). That is why the area of this contour was small.
I am trying to develop a OCR system. I am trying to use MSER in order to extract character from an image and then passing the characters into a CNN to recognize those characters. Here is my code for character extraction:
import cv2
import numpy as np
# create MSER object
mser = cv2.MSER_create()
# read the image
img = cv2.imread('textArea01.png')
# convert to gray scale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# store copy of the image
vis = img.copy()
# detect regions in the image
regions,_ = mser.detectRegions(gray)
# find convex hulls of the regions and draw them onto the original image
hulls = [cv2.convexHull(p.reshape(-1, 1, 2)) for p in regions]
cv2.polylines(vis, hulls, 1, (0, 255, 0))
# create mask for the detected region
mask = np.zeros((img.shape[0], img.shape[1], 1), dtype=np.uint8)
mask = cv2.dilate(mask, np.ones((150, 150), np.uint8))
for contour in hulls:
cv2.drawContours(mask, [contour], -1, (255, 255, 255), -1)
#this is used to find only text regions, remaining are ignored
text_only = cv2.bitwise_and(img, img, mask=mask)
cv2.imshow('img', vis)
cv2.waitKey(0)
cv2.imshow('mask', mask)
cv2.waitKey(0)
cv2.imshow('text', text_only)
cv2.waitKey(0)
This is working fine for most images, but for some images like this:
The outer border is also detected as a region and the contour is drawn in the mask such that all area inside the border is detected as text region. So, the contours inside have no effect. How do I prevent this so that only the text is detected?
Hulls detected:
and the mask as a result:
My result using this code:
import cv2
import numpy as np
img = cv2.imread("img.png")
# grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv2.imshow('gray', gray)
# binary
# ret, thresh = cv2.threshold(gray, 250, 255, cv2.THRESH_BINARY_INV)
thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 35, 180)
cv2.imshow('threshold', thresh)
# dilation
kernel = np.ones((1, 1), np.uint8)
img_dilation = cv2.dilate(thresh, kernel, iterations=1)
cv2.imshow('dilated', img_dilation)
# find contours
# cv2.findCountours() function changed from OpenCV3 to OpenCV4: now it have only two parameters instead of 3
cv2MajorVersion = cv2.__version__.split(".")[0]
# check for contours on thresh
if int(cv2MajorVersion) >= 4:
ctrs, hier = cv2.findContours(img_dilation.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
else:
im2, ctrs, hier = cv2.findContours(img_dilation.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# sort contours
sorted_ctrs = sorted(ctrs, key=lambda ctr: cv2.boundingRect(ctr)[0])
for i, ctr in enumerate(sorted_ctrs):
# Get bounding box
x, y, w, h = cv2.boundingRect(ctr)
# Getting ROI
roi = img[y:y + h, x:x + w]
# show ROI
# cv2.imshow('segment no:'+str(i),roi)
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 1)
# if you want to save the letters without green bounding box, comment the line above
if w > 5:
cv2.imwrite('C:\\Users\\PC\\Desktop\\output\\{}.png'.format(i), roi)
cv2.imshow('marked areas', img)
cv2.waitKey(0)
You can have a threshold on the contour area so that it ignores all shapes that cover more than a certain area in the image.
for contour in hulls:
if cv.contourArea(contour) < ThresholdArea:
continue
cv2.drawContours(mask, [contour], -1, (255, 255, 255), -1)
#this is used to find only text regions, remaining are ignored
text_only = cv2.bitwise_and(img, img, mask=mask)
I'm looking to split number on image. My main problem is that the number may touch each other. so all technique with OpenCV contour found online doesn't work.
Here the steps i've done so far. It's working well with image that have number well separated, but not with number touching each other. One of the image i try to solve:
I'm trying to do that as part of the preprocessing step so i can after that use the number in my MNIST model.
My actual code:
import cv2
import numpy as np
# 3 - detect and extract ROI's
#image = cv2.imread('imgs/01.jpg')
#image = cv2.imread('imgs/03.jpg')
image = cv2.imread('imgs/01_tresh_210.jpg')
#cv2.imshow('i', image)
#cv2.waitKey(0)
# grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.imshow('gray', gray)
cv2.waitKey(0)
# binary
ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY_INV)
cv2.imshow('thresh', thresh)
cv2.waitKey(0)
# dilation
kernel = np.ones((10, 1), np.uint8) # values set for this image only - need to change for different images
img_dilation = cv2.dilate(thresh, kernel, iterations=1)
cv2.imshow('dilated', img_dilation)
cv2.waitKey(0)
# find contours
im2, ctrs, hier = cv2.findContours(img_dilation.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# sort contours
sorted_ctrs = sorted(ctrs, key=lambda ctr: cv2.boundingRect(ctr)[0])
for i, ctr in enumerate(sorted_ctrs):
# Get bounding box
x, y, w, h = cv2.boundingRect(ctr)
# Getting ROI
roi = image[y:y + h, x:x + w]
# show ROI
#cv2.imshow('segment no:'+str(i),roi)
cv2.imwrite('imgs\\roi\\{}.png'.format(i), roi)
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 0, 0), 1)
#cv2.waitKey(0)
# save only the ROI's which contain a valid information
if h > 20 and w > 75:
cv2.imwrite('roi\\{}.png'.format(i), roi)
cv2.imshow('marked areas', image)
cv2.waitKey(0)
I've written some code, to crop an object (in this case the Data Matrix Code) from an image:
import numpy as np
import cv2
image = cv2.imread("datamatrixc.png")
img_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
img_height, img_width = image.shape[:2]
WHITE = [255, 255, 255]
# Threshold filter
ret, thresh = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY_INV)
# Get Contours
_, contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# Get Last element of the contours object
max = len(contours) - 1
cnt = contours[max]
# Get coordinates for the bounding box
x, y, w, h = cv2.boundingRect(cnt)
image_region = image[ int(((img_height / 2) - h) / 2) : int(((img_height / 2) - h) / 2 + h), int(x): int(x + w) ]
dmc = cv2.copyMakeBorder(image_region, 10, 10, 10, 10, cv2.BORDER_CONSTANT, value = WHITE)
cv2.imshow("Test", dmc)
cv2.waitKey(0)
cv2.destroyAllWindows()
The code works fine and I received as result:
However, the next image is a little more complicated.
I receive the same result as in the previous image, but I have no idea how to detect the two other objects.
Is there an easier way every object showing in its window?
For this specific image take the biggest contours you have and check if the object is 4 sided shape.If the half-point between the bounding box's corners (see pairs below) is in the contour array then voila, problem solved.
Pairs : TopRight-TopLeft, TopRight-BottomRight, TopLeft-BottomLeft, BottomLeft-BottomRight
Or you could check if there pixels that are not black/white inside the bounding box ?
And for the ploting individualy just slap a for on what you allready have
How about this?
import numpy as np
import cv2
image = cv2.imread("datamatrixc.png")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
ret, bin_img = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
kernel = np.ones((3,3),np.uint8)
closing = cv2.morphologyEx(bin_img, cv2.MORPH_CLOSE, kernel, iterations=4)
n_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(bin_img)
size_thresh = 5000
for i in range(1, n_labels):
if stats[i, cv2.CC_STAT_AREA] >= size_thresh:
print(stats[i, cv2.CC_STAT_AREA])
x = stats[i, cv2.CC_STAT_LEFT]
y = stats[i, cv2.CC_STAT_TOP]
w = stats[i, cv2.CC_STAT_WIDTH]
h = stats[i, cv2.CC_STAT_HEIGHT]
cv2.imshow('img', image[y:y+h, x:x+w])
cv2.waitKey(0)
I have a big image of shape where i'm looking to overlap an image on a shape based on the contour
I have this image
I have this contur where it is detecting the shape and color of the image
I want to place an image on this shape
I want to custom resize the image
Out Put Desired:-
Code:-
if color == "blue" and shape == "pentagon":
A_img = cv2.imread("frame.png")
print(A_img)
x_offset=y_offset=50
B_img[y_offset:y_offset+A_img.shape[0], x_offset:x_offset+A_img.shape[1]] = A_img
That code is not working the monkey is getting printed on the top left
I have a working solution. Hope it is what you were looking for.
Code:
import cv2
import numpy as np
image = cv2.imread('C:/Users/524316/Desktop/shapes.png', 1)
monkey = cv2.imread('C:/Users/524316/Desktop/monkey.png', 1)
image2 = image.copy()
image3 = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
thresh = cv2.threshold(blurred, 60, 255, cv2.THRESH_BINARY)[1]
#cv2.imshow('thresh', thresh)
_, cnts, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for c in cnts:
#---- making sure to avoid small unwanted contours ---
if cv2.contourArea(c) > 150:
#--- selecting contours having 5 sides ---
if len(cv2.approxPolyDP(c, 0.04 * cv2.arcLength(c, True), True)) == 5:
cv2.drawContours(image2, [c], -1, (0, 255, 0), 2)
#--- finding bounding box dimensions of the contour ---
x, y, w, h = cv2.boundingRect(c)
print(x, y, w, h)
#--- overlaying the monkey in place of pentagons using the bounding box dimensions---
image3[y:y+h, x:x+w] = cv2.resize(monkey, (np.abs(x - (x+w)), np.abs(y - (y+h))))
cv2.imshow('image2', image2)
cv2.imshow('image3', image3)
cv2.waitKey(0)
cv2.destroyAllWindows()
Result: