I want to detect a rectangle shape in the digital meter, to detect the shape contour approximation, but not able to find the exact contour of rectangle .I don't know where is the mistake .please have a look and suggest
digitalMeter.jpg
required-Output-digitalMeter-contour
import imutils
import cv2
image = cv2.imread('C:\\digitalMeter.jpg')
image = imutils.resize(image, height=500)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
edged = cv2.Canny(blurred, 50, 200, 255)
cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)
displayCnt = None
for c in (cnts):
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.02 * peri, True)
if len(approx) == 4:
print(displayCnt)[enter image description here][2]
displayCnt = approx
break
cv2.drawContours(image, [displayCnt], -1, (0, 230, 255), 6)
cv2.imshow('cnts', image)
cv2.waitKey(0)
Here is one way to do that in Python/OpenCV.
Read the input
Convert to gray
Threshold
Apply morphology to clean up the threshold image
Invert so that the meter is white on a black background
Find the contours and extract the largest (actually only) contour
Draw the contour on the input image
Save the results
Input:
import cv2
import numpy as np
# read image
img = cv2.imread('digital_meter.jpg')
hh, ww = img.shape[:2]
# convert to grayscale
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# threshold
thresh = cv2.threshold(gray,30,255,cv2.THRESH_BINARY)[1]
# apply close and open morphology
kernel = np.ones((3,3), np.uint8)
mask = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
kernel = np.ones((11,11), np.uint8)
mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
# invert
mask = 255 - mask
# get largest contour
contours = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
big_contour = max(contours, key=cv2.contourArea)
# draw green contour on input
contour_img = img.copy()
cv2.drawContours(contour_img,[big_contour],0,(0,255,0),2)
# save cropped image
cv2.imwrite('digital_meter_thresh.png',thresh)
cv2.imwrite('digital_meter_mask.png',mask)
cv2.imwrite('digital_meter_contour.png',contour_img)
# show the images
cv2.imshow("THRESH", thresh)
cv2.imshow("MASK", mask)
cv2.imshow("CONTOUR", contour_img)
cv2.waitKey(0)
cv2.destroyAllWindows()
Threshold Image:
Morphology cleaned and inverted image:
Resulting contour on input:
Related
Just used the following code a OCR application. The OCR to be read is on a metal milled surface with a unique font. The below code work well for embossed surfaces but not engraved surfaces. I have tried tweaking the blur and dilate iterations, still no results.
How i can add new fonts into tesseract , if the unique font is an issue ?
Any tips on how i can get better results ?
import cv2
import numpy as np
import imutils
import pytesseract
# read image from disk
image = cv2.imread('test.jpg')
# make it gray
img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# blur it to remove noise
img = cv2.GaussianBlur(img, (7,7), 0)
# perform edge detection, then perform a dilation + erosion to
# close gaps in between object edges
edged = cv2.Canny(img, 40, 90)
dilate = cv2.dilate(edged, None, iterations=1)
# perform erosion if necessay, it completely depends on the image
#erode = cv2.erode(dilate, None, iterations=1)
# create an empty masks
mask = np.ones(img.shape[:2], dtype="uint8") * 255
# find contours
cnts = cv2.findContours(dilate.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[1] if imutils.is_cv2() else cnts[0]
orig = img.copy()
for c in cnts:
# if the contour is not sufficiently large, ignore it
if cv2.contourArea(c) < 300:
cv2.drawContours(mask, [c], -1, 0, -1)
x,y,w,h = cv2.boundingRect(c)
# filter more contours if nessesary
if(w>h):
cv2.drawContours(mask, [c], -1, 0, -1)
newimage = cv2.bitwise_and(dilate.copy(), dilate.copy(), mask=mask)
img2 = cv2.dilate(newimage, None, iterations=5)
ret2,th1 = cv2.threshold(img2 ,0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)
pytesseract.pytesseract.tesseract_cmd = r'root\folder'
# Tesseract OCR on the image
temp = pytesseract.image_to_string(th1)
# Write results on the image
cv2.putText(image, temp, (100,100), cv2.FONT_HERSHEY_SIMPLEX, 1.8, (0,255,255), 3)
# show the outputs
cv2.imshow('Original image', cv2.resize(image,(640,480)))
cv2.imshow('Dilated', cv2.resize(dilate,(640,480)))
cv2.imshow('New Image', cv2.resize(newimage,(640,480)))
cv2.imshow('Inverted Threshold', cv2.resize(th1,(640,480)))
cv2.waitKey(0)
cv2.destroyAllWindows()
I am using this code to remove this yellow stamp from an image :
import cv2
import numpy as np
# read image
img = cv2.imread('input.jpg')
# threshold on yellow
lower = (0, 200, 200)
upper = (100, 255, 255)
thresh = cv2.inRange(img, lower, upper)
# apply dilate morphology
kernel = np.ones((9, 9), np.uint8)
mask = cv2.morphologyEx(thresh, cv2.MORPH_DILATE, kernel)
# get largest contour
contours = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
big_contour = max(contours, key=cv2.contourArea)
x, y, w, h = cv2.boundingRect(big_contour)
# draw filled white contour on input
result = img.copy()
cv2.drawContours(result, [big_contour], 0, (255, 255, 255), -1)
cv2.imwrite('yellow_removed.png', result)
# show the images
cv2.imshow("RESULT", result)
cv2.waitKey(0)
cv2.destroyAllWindows()
I get the following error:
big_contour = max(contours, key=cv2.contourArea) ValueError: max() arg
is an empty sequence
Obviously, it is not detecting any contours, and the contours array is empty, but I could not figure out why that is or how to fix it.
Help is appreciated!
Check your lower thresholds. It worked for me for both images when I changed the lower threshold to lower = (0, 120, 120).
The thresholds is the reason due to the second image being darker. Lowering these thresholds captures more of the yellow area, but will still leave some holes when drawing the contour.
lower = (0, 130, 130)
You can fix this by drawing the bounding rectangle instead.
cv2.rectangle(result,(x,y),(x+w,y+h),(255,255,255),-1)
Using HSV color space is great for figuring out a particular shade/tone of color. When you have dominant colors to isolate, you can opt for the LAB color space. I have explained as to why this is better in this answer.
Code:
img = cv2.imread('bill.jpg')
# create another copy for the result
img2 = img.copy()
# convert to LAB space and store b-channel
lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
b_channel = lab[:,:,-1]
Notice how bright the yellow region is above.
# Perform Otsu threshold
th = cv2.threshold(b_channel, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1]
# Find the contour with largest area
contours, hierarchy = cv2.findContours(th, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
c = max(contours, key = cv2.contourArea)
# draw the contour on plain black image of same shape as original
mask = np.zeros((img.shape[0], img.shape[1]), np.uint8)
mask = cv2.drawContours(mask,[c],0,255, -1)
# dilation to avoid border effects
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
dilate = cv2.dilate(mask, kernel, iterations=1)
img2[dilate == 255] = (255, 255, 255)
Another example:
Input:
Result:
Original Image
Click here for the image
For this, I am trying to detect the underlines first. But as the underlines might be tilted, this code:
import time
from google.colab.patches import cv2_imshow
from collections import OrderedDict
# Let's load a simple image with 3 black squares
image = cv2.imread("line_detected.png")
cv2.waitKey(0)
# Grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Find Canny edges
font = cv2.FONT_HERSHEY_COMPLEX
edged = cv2.Canny(gray, 30, 200)
cv2.waitKey(0)
# Finding Contours
# Use a copy of the image e.g. edged.copy()
# since findContours alters the image
contours, hierarchy = cv2.findContours(edged, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cv2_imshow(edged)
cv2.waitKey(0)
print("Number of Contours found = " + str(len(contours)))
# Draw all contours
# -1 signifies drawing all contours
# cv2.drawContours(image, contours, -1, (0, 255, 0), 3)
mask = np.ones(image.shape[:2], dtype="uint8") * 255
d=OrderedDict()
coords=[]
nuclei = []
l=[]
heading=[]
images=[]
lvalue=0
line=[]
h=[]
contours = contours[::-1]
for cnt in (contours):
peri = cv2.arcLength(cnt, True)
approx = cv2.approxPolyDP(cnt, 0.04 * peri, True)
if (len(approx==2)):
x, y, w, h = cv2.boundingRect(cnt)
# print(h)
cv2.rectangle(img,(x, y), (x+w, y+h),(0, 0, 255), 2)
cv2_imshow(img)
is not able to detect the slanting underlines very properly. Also, I want this code to extend to detecting only the gray underlines. "minor differences" has a single underline as it is slanted/tilted, it reads it as two straight lines. Also, it is reading the images in the left which it should not read(tesseract giving weird outputs).
For the gray shade only I found this mask thing online:
lower_range = np.array([110,50,50])
upper_range = np.array([130,255,255])
mask = cv2.inRange(hsv, lower_range, upper_range)
But Don't know how to incorporate in code... I'm a beginner, any help is much appreciated!
I am doing this university project where i try to detect UI elements on screenshots of Android applications using openCV. I am not expecting a 100 percent accuracy for this detection of UI elements.
This is my code below. I convert the image to gray scale, apply Gaussian blur and then use adaptive threshold to convert the image to binary. After which i use the find contours method.
ap = argparse.ArgumentParser()
ap.add_argument("-i","--image", help = "path to an image", required =
True)
args = vars(ap.parse_args())
image = cv2.imread(args["image"])
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.imshow("gray",gray)
cv2.waitKey(0)
blurred = cv2.GaussianBlur(gray, (5,5), 0)
thresh = cv2.adaptiveThreshold(blurred, 255,
cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 11, 4)
cv2.imshow("thresh",thresh)
cv2.waitKey(0)
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
cv2.drawContours(image, cnts, -1, (0,255,0), 1)
cv2.imshow("contours", image)
cv2.waitKey(0)
for c in cnts:
area = cv2.contourArea(c)
print(area)
if area > 50:
M = cv2.moments(c)
cX = int(M['m10'] / M['m00'])
cY = int(M['m01'] / M['m00'])
#cv2.drawContours(image, [c], -1, (0,255,0), 2) # draw contours on image
(x,y,w,h) = cv2.boundingRect(c) # for each contour get a
bounding rectangle
mask = np.zeros(image.shape[:2], dtype = "uint8") # find
shape of the image dimensions and set up a mask
mask[y: y + h, x: x + w] = 255 # convert region of
interest into white
to_display = cv2.bitwise_and(image,image, mask = mask) # carry
out bitwise and
#cv2.putText(image, 'center', (c))
cv2.imshow("Image", to_display)
cv2.waitKey(0)
this is the screenshot that i am running my code on.
The leftmost screenshot represents the image after applying a threshold to it.
The middle image represents the image i get after drawing the contours.
The last image shows when i am examining each individual contour. The contour covers the line but does not encapsulate the rectangle.
I have a few questions.
1) Is it possible to sieve out the contours for the white rectangles. What alteration do i have to make to my code to be able to achieve this?
2) I am trying to sieve out the unimportant contours eg. the words and I was thinking if i could use the getArea() function to help me with it. The idea is that i would set a minimum contour size to filter out the smaller contours that account for the words.
This is another image that i have tried to identify the "objects" in this screenshots.
I face the same issue here where i cant identify the white rectangles. I am only identifying the borders of the rectangle.
Would appreciate any form of help as I am still new to openCv
Original images before processing:
There is no need to blur. In fact I makes it harder. Simple thresholding works best with hard transitions. The second image is easiest. There are white items on a grayish background. By selecting only very white values the items are selected.
Result:
Code:
# load image
img = cv2.imread("app.png")
# convert to gray
img2 = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# crate a mask that hold only white values (above 250)
ret,thresh1 = cv2.threshold(img2,250,255,cv2.THRESH_BINARY)
# find contours in mask
im2, contours, hierarchy = cv2.findContours(thresh1, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# select large contours (menu items only)
for cnt in contours:
print(cv2.contourArea(cnt))
if cv2.contourArea(cnt) > 5000:
# draw a rectangle around the items
x,y,w,h = cv2.boundingRect(cnt)
cv2.rectangle(img, (x,y), (x+w,y+h), (0,255,0),3)
#cv2.drawContours(img, [cnt], 0, (0,255,0), 3) #also works, but has issues with letters at the last item
#show image
cv2.imshow("img", img)
#cv2.imshow("mask", thresh) # shows mask
cv2.waitKey(0)
cv2.destroyAllWindows()
The first image is more complex, because it is divided in by a very thin red line. Selecting colors is easier in HSV colorspace. Next red values are used to create a mask, some noise is removed and then contours are detected.
Result:
# load image
img = cv2.imread("app2.png")
# convert to HSV
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# set lower and upper color limits
lower_val = np.array([0,0,0])
upper_val = np.array([20,50,255])
# Threshold the HSV image
mask = cv2.inRange(hsv, lower_val, upper_val)
# remove noise
kernel = np.ones((1,2),np.uint8)
mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
kernel = np.ones((1,5),np.uint8)
mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
# find contours in mask
im2, contours, hierarchy = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# select large contours (menu items only)
for cnt in contours:
print(cv2.contourArea(cnt))
if cv2.contourArea(cnt) > 1000:
# draw a rectangle around the items
x,y,w,h = cv2.boundingRect(cnt)
cv2.rectangle(img, (x,y), (x+w,y+h), (0,255,0),3)
#show image
cv2.imshow("img", img)
cv2.imshow("mask", mask)
cv2.waitKey(0)
cv2.destroyAllWindows()
I am trying to find the contours of an animal from a picture. Let's assume it is a chicken. From the picture I could find its contours but they aren't closed. Also, I am getting a lot of noise from the background which is white ( same as the chicken).
I am using a simple code found on stackoverflow.
import numpy as np
import cv2
img = cv2.imread('lateral.jpg')
imgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# blurred = cv2.GaussianBlur(imgray, (5, 5), 0)
# edged = cv2.Canny(blurred, 10, 11) # 10 and 40 to be more perceptive
# contours_canny= cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[-2]
edges = cv2.Canny(imgray, 10,30)
cv2.imshow('edges', edges)
k = cv2.waitKey()
Is there a way to find just the contour of this chicken?
Thanks in advance.
Finding contour is quite easy. The problem is that your image has low contrast between the chicken and the background. So, your idea of using canny edges was not bad, it just needed some post processing.
I guess this is what you are looking for:
import cv2
import numpy as np
image = cv2.imread("./chicken.jpg", cv2.IMREAD_COLOR)
image = cv2.resize(image, (0,0), fx=0.5, fy=0.5)
imgray = cv2.cvtColor(image, cv2.COLOR_BGR2LAB)[...,0]
edges = cv2.Canny(imgray, 10,30)
blurred = cv2.GaussianBlur(edges, (9, 9), 0)
clahe = cv2.createCLAHE(clipLimit=5.0, tileGridSize=(32,32))
contrast = clahe.apply(blurred)
ret, thresh = cv2.threshold(contrast, 20, 255, cv2.THRESH_BINARY|cv2.THRESH_OTSU)
_, contours, _ = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
maxArea = 0
best = None
for contour in contours:
area = cv2.contourArea(contour)
print (area)
if area > maxArea :
maxArea = area
best = contour
cv2.drawContours(image, [best], 0, (0, 0, 255), -1)
while True:
cv2.imshow("result", image)
k = cv2.waitKey(30) & 0xff
if k == 27:
break