I am currently working on solar panel cropping from the images taken by the drone(attaching sample image). I have tried using contours but there wasn't a proper outcome. It was not detecting all solar panels in the image some of them were missing. I struck here itself. How do I proceed further? Please help me with this problem.
Thank you,
Sample Code:
import cv2
import numpy as np
img = cv2.imread('D:\\SolarPanel Images\\solarpanel.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray,(5,5),0)
edges = cv2.Canny(blur,100,200)
th3 = cv2.adaptiveThreshold(edges,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2)
im2, contours, hierarchy = cv2.findContours(th3, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
print("Len of contours",len(contours)
try: hierarchy = hierarchy[0]
except: hierarchy = []
height, width, = edges.shape
min_x, min_y = width, height
max_x = max_y = 0
# computes the bounding box for the contour, and draws it on the image,
for contour, hier in zip(contours, hierarchy):
area = cv2.contourArea(contour)
if area > 10000 and area < 250000:
(x,y,w,h) = cv2.boundingRect(contour)
min_x, max_x = min(x, min_x), max(x+w, max_x)
min_y, max_y = min(y, min_y), max(y+h, max_y)
if w > 80 and h > 80:
cv2.rectangle(img, (x,y), (x+w,y+h), (255, 0, 0), 2)
cv2.imshow('cont imge', img)
cv2.waitKey(0)
To find contours in images where the object of importance is clearly distinguishable from the background, you can always try converting the image to HSV format and then contour. I did the following:
import cv2
import numpy as np
img = cv2.imread('panel.jpg')
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
ret,thresh1 = cv2.threshold(hsv[:,:,0],100,255,cv2.THRESH_BINARY)
im2, contours, hierarchy = cv2.findContours(thresh1, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
try: hierarchy = hierarchy[0]
except: hierarchy = []
for contour, hier in zip(contours, hierarchy):
area = cv2.contourArea(contour)
if area > 10000 and area < 250000:
rect = cv2.minAreaRect(contour)
box = cv2.boxPoints(rect)
box = np.int0(box)
cv2.drawContours(img,[box],0,(0,0,255),2)
cv2.imshow('cont imge', img)
cv2.waitKey(0)
cv2.imwrite("result.jpg",img)
Result:
Related
I am trying to detect orange beats in below image
To detect these, I have first cropped the area from original image and then setting high and low hsv values to detect orange. This seems to be working fine. Below is the detected image:
Below is the code:
import cv2
import numpy as np
win_name = "Image"
cv2.namedWindow(win_name)
img = cv2.imread('image.png')
orangeImg = img[420:510, 457:953]
hsv = cv2.cvtColor(orangeImg, cv2.COLOR_BGR2HSV)
lower_bound = np.array([0, 80, 80])
upper_bound = np.array([20, 255, 255])
origMask = cv2.inRange(hsv, lower_bound, upper_bound)
contours, hierarchy = cv2.findContours(origMask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for _, c in enumerate(contours):
areas = [cv2.contourArea(c) for c in contours]
for area in areas:
if area >= 20.0:
boundRect = cv2.boundingRect(c)
rectX = boundRect[0]
rectY = boundRect[1]
rectWidth = boundRect[2]
rectHeight = boundRect[3]
color = (0, 0, 255)
cv2.rectangle(orangeImg, (int(rectX), int(rectY)), (int(rectX + rectWidth), int(rectY + rectHeight)), color, 2)
cv2.imshow(win_name, img)
cv2.waitKey(0)
cv2.destroyAllWindows()
In the output image, you can notice that it still has some noise around the bbox created. Is there a better way to reduce the noise in it. Also is there a way to count the detected contours in the image?
Hello everybody before you close this question, i have already searched here, here too and also here
I am using python code to detect the Leaf in an image, using contours finding and then find out the largest contour, the part works best, but then i want only the leaf part of the image and skip the rest of the image to avoid unnecessary content in the resultant output, some of the methods in the link suggests bounding box but this still includes extra content in the image as the shape is not rectangular it's irregular, sample is attached
The code is following
import cv2
import numpy as np
img = cv2.imread("blob.jpg", -1)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 101, 3)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5))
blob = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (9,9))
blob = 255 - cv2.morphologyEx(blob, cv2.MORPH_CLOSE, kernel)
cnts = cv2.findContours(blob, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if len(cnts) == 2:
cnts = cnts[0]
else:
cnts = cnts[1]
big_contour = max(cnts, key = cv2.contourArea)
blob_area_thresh = 1000
blob_area = cv2.contourArea(big_contour)
if blob_area < blob_area_thresh:
print("Leaf is Too Small")
else:
#problem starts from here . i tested big_contour is just perfect by drawing on actual image
mask = np.zeros_like(img)
cv2.drawContours(mask, big_contour, -1, 255, -1)
out = np.zeros_like(img)
out[mask == 255] = img[mask == 255]
cv2.imwrite('output.jpg', out)
Now the problem is i am getting the resultant image as black nothing cropped all black pixels
There is a problem with your contour because it is not circulating the leaf as the end of the leaf on the right is out of the image.
You can see this when I try to fill the contour to create a mask using
cv2.fillPoly(mask, pts =[big_contour], color=(255,255,255))
it doesn't fill the leaf.
However I tried something although not perfect and has some background left but it crops the leaf to some extend.
import cv2
import numpy as np
img = cv2.imread("96Klg.jpg", -1)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 101, 3)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5))
blob = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
#kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (9,9))
#blob = 255 - cv2.morphologyEx(blob, cv2.MORPH_CLOSE, kernel)
cnts = cv2.findContours(blob, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if len(cnts) == 2:
cnts = cnts[0]
else:
cnts = cnts[1]
big_contour = max(cnts, key = cv2.contourArea)
blob_area_thresh = 1000
blob_area = cv2.contourArea(big_contour)
if blob_area < blob_area_thresh:
print("Leaf is Too Small")
else:
#problem starts from here . i tested big_contour is just perfect by drawing on actual image
mask = np.ones_like(img)
mask.fill(255)
cv2.fillPoly(mask, pts =[big_contour], color=(0,0,0))
#cv2.drawContours(mask, big_contour, -1, (255,255,255), 1)
out = np.zeros_like(img)
out[mask == 255] = img[mask == 255]
width = int(gray.shape[1] * 0.25)
height = int(gray.shape[0] * 0.25)
dim = (width, height)
# resize image
resized = cv2.resize(out, dim, interpolation = cv2.INTER_AREA)
resizedmask = cv2.resize(mask, dim, interpolation = cv2.INTER_AREA)
cv2.imshow('gray',resized)
cv2.imshow('out',resizedmask)
Output
I am using the same code that is provided by the OpenCv tutorial, it was working few weeks ago, today I was trying to run it is says that gray name is not defined!! can some one find me the error?
import numpy as np
#import matplotlib.pyplot as plt
import cv2
import glob
import os
def draw(img, corners, imgpts):
corner = tuple(corners[0].ravel())
img = cv2.line(img, corner, tuple(imgpts[0].ravel()), (255,0,0), 5)
img = cv2.line(img, corner, tuple(imgpts[1].ravel()), (0,255,0), 5)
img = cv2.line(img, corner, tuple(imgpts[2].ravel()), (0,0,255), 5)
return img
# termination criteria
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((7*7,3), np.float32)
objp[:,:2] = np.mgrid[0:7,0:7].T.reshape(-1,2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d point in real world space
imgpoints = [] # 2d points in image plane.
img_dir = "C:\\Hungary\\Biblography\\Rotating Solitary Wave\\My Work\\Final Work\\Experiment1111 \\Camera Calibration\\Image Processing\\chess"
data_path = os.path.join(img_dir,'*bmp')
images = glob.glob(data_path)
for fname in images:
img = cv2.imread(fname)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# Find the chess board corners
ret, corners = cv2.findChessboardCorners(gray, (7,7),None)
# If found, add object points, image points (after refining them)
if ret == True:
objpoints.append(objp)
corners2 = cv2.cornerSubPix(gray,corners,(11,11),(-1,-1),criteria)
imgpoints.append(corners2)
# Draw and display the corners
img = cv2.drawChessboardCorners(img, (7,7), corners2,ret)
cv2.imshow('img',img)
cv2.waitKey(500)
cv2.destroyAllWindows()
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape [::-1],None,None)
print('Rotation Vector, or the Angles For Each Photo: ', rvecs, '\n')
R = cv2.Rodrigues(rvecs[0])
print('The Rotation Matrix is: ', R)
print('Translation Vector: ', tvecs, '\n')
print(mtx, '\n')
print('Distortion Coefficients ', dist, '\n')
img = cv2.imread('00000274.bmp')
h, w = img.shape[:2]
newcameramtx, roi=cv2.getOptimalNewCameraMatrix(mtx,dist,(w,h),1,(w,h))
print('Camera Matrix', newcameramtx, '\n')
# undistort
dst = cv2.undistort(img, mtx, dist) #, None, newcameramtx)
p = np.ones_like(dst)
# crop the image
x,y,w,h = roi
dst = dst[y:y+h, x:x+w]
# undistort
mapx,mapy = cv2.initUndistortRectifyMap(mtx,dist,None,newcameramtx,(w,h),5)
dst = cv2.remap(img,mapx,mapy,cv2.INTER_LINEAR)
# crop the image
x,y,w,h = roi
dst = dst[y:y+h, x:x+w]
cv2.imwrite('calibresult.png',dst)
mean_error = 0
for i in range(len(objpoints)):
imgpoints2, _ = cv2.projectPoints(objpoints[i], rvecs[i], tvecs[i], mtx, dist)
error = cv2.norm(imgpoints[i],imgpoints2, cv2.NORM_L2)/len(imgpoints2)
mean_error += error
print("total error: ", mean_error/len(objpoints))
If you read the opencv document you will find that I did little changes on the code and it was working but today it is raising this error about the gray name is not defined!
Check your path once, and see if images is an empty list. In that case, for loop will not be executed where the gray variable is defined.
I encountered such a problem: I can not draw lines on the image where the color was determined, and also find out the distance to this place. Help to make it as in the image below:
My code:
import cv2
import numpy as np
from PIL import ImageGrab
while True:
screen = np.array(ImageGrab.grab(bbox=(0,40,800,640)))
rgb_screen = cv2.cvtColor(screen, cv2.COLOR_BGR2RGB)
lower = np.array([72, 160, 160])
upper = np.array([112, 249, 249])
mask = cv2.inRange(rgb_screen, lower, upper)
output = cv2.bitwise_and(rgb_screen, rgb_screen, mask=mask)
cv2.imshow('window', output)
if cv2.waitKey(25) & 0xFF == ord('q'):
cv2.destroyAllWindows()
break
I can't help much because I do not have your original image. But you could read my code and maybe get an idea. For distance I do not know what u mean, so I made an example on how to get distance of top left corner to bottom left. You can apply other points or apply it as ratio depanding on your demands.
import cv2
import numpy as np
img = cv2.imread('untitled.png')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, threshold = cv2.threshold(gray,150,255,cv2.THRESH_BINARY)
im, contours, hierarchy = cv2.findContours(threshold,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
area = sorted(contours, key=cv2.contourArea, reverse=True)
c = area[0]
rect = cv2.minAreaRect(c)
box = cv2.boxPoints(rect)
print(box)
box = np.int0(box)
cv2.drawContours(img,[box],0,(0,0,255),2)
extreme_left = tuple(c[c[:, :, 0].argmin()][0])
extreme_top = tuple(c[c[:, :, 1].argmin()][0])
x1 = box[1,0]
y1 = box[1,1]
x2 = box[0,0]
y2 = box[0,1]
distance = np.sqrt( (x1 - x2)**2 + (y1 - y2)**2 )
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(img,'Distance: '+str(distance),(1,300), font, 0.5,(255,255,255),2,cv2.LINE_AA)
cv2.circle(img, (x2,y2), 5, (255, 0, 0), -1)
cv2.circle(img, (x1,y1), 5, (255, 0, 0), -1)
cv2.imshow('image', img)
Image used --
My code:
# multiple programs
import cv2
import numpy as np
img = cv2.imread('Dodo.jpg', 0)
ret, thresh = cv2.threshold(img, 127, 255, 0)
img2, contours, hierarchy = cv2.findContours(thresh, 1, 2)
cnt = contours[0]
M = cv2.moments(cnt)
print(M)
cx = int(M['m10']/ M['m00'])
cy = int(M['m01']/ M['m00'])
print("Cx:", cx, "Cy:", cy)
area = cv2.contourArea(cnt)
print("Area:", area)
perimeter = cv2.arcLength(cnt, True)
print("Perimeter:", perimeter)
epsilon = 0.1*cv2.arcLength(cnt,True)
approx = cv2.approxPolyDP(cnt,epsilon,True)
imgapprox = cv2.drawContours(img,[approx],0,(0,0,255),2)
hull = cv2.convexHull(cnt)
imghull =cv2.drawContours(img,[hull],0,(0,0,255),2)
k = cv2.isContourConvex(cnt)
print(k)
x,y,w,h = cv2.boundingRect(cnt)
rectst = cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)
rect = cv2.minAreaRect(cnt)
box = cv2.boxPoints(rect)
box = np.int0(box)
rectrt =cv2.drawContours(img,[box],0,(0,0,255),2)
cv2.imshow('StraightRect', rectst)
cv2.imshow('RotatedRect', rectrt)
cv2.imshow('Approx', imgapprox)
cv2.imshow('hull', imghull)
cv2.waitKey()
cv2.destroyAllWindows()
OpenCV-Python version 3.4.1
So I am trying to learn the contour section in OpenCV (Link below)
Link : https://docs.opencv.org/3.4.1/dd/d49/tutorial_py_contour_features.html
Now the output is the same for all the features. i.e. same output for every cv2.imshow here.
Why? What is the error?
If it is overwriting the previous feature, then how do I display every feature?
Please help. Thanks :)
You are making the change in the same image each time.
Use image.copy() in cv2.drawContours(img.copy ,.......) , cv2.rectangle(img.copy(),.....)
.Because of that it seems they are showing the same features but it isn't .
Also since the background is black you are not able to see the rectangles and contour properly
Try this:
import cv2
import numpy as np
img = cv2.imread('Dodo.jpg')
f1 = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
f1 = cv2.threshold(f1, 120,255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1]
img2, contours, hierarchy = cv2.findContours(f1, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
#ret, thresh = cv2.threshold(img, 127, 255, 0)
#img2, contours, hierarchy = cv2.findContours(thresh, 1, 2)
cnt = contours[0]
M = cv2.moments(cnt)
print(M)
cx = int(M['m10']/ M['m00'])
cy = int(M['m01']/ M['m00'])
print("Cx:", cx, "Cy:", cy)
area = cv2.contourArea(cnt)
print("Area:", area)
perimeter = cv2.arcLength(cnt, True)
print("Perimeter:", perimeter)
epsilon = 0.1*cv2.arcLength(cnt,True)
approx = cv2.approxPolyDP(cnt,epsilon,True)
imgapprox = cv2.drawContours(img.copy(),[approx],0,(0,0,255),2)
hull = cv2.convexHull(cnt)
imghull =cv2.drawContours(img.copy(),[hull],0,(0,0,255),2)
k = cv2.isContourConvex(cnt)
print(k)
x,y,w,h = cv2.boundingRect(cnt)
rectst = cv2.rectangle(img.copy(),(x,y),(x+w,y+h),(0,255,0),2)
rect = cv2.minAreaRect(cnt)
box = cv2.boxPoints(rect)
box = np.int0(box)
rectrt =cv2.drawContours(img.copy(),[box],0,(0,0,255),2)
cv2.imshow('StraightRect', rectst)
cv2.imshow('RotatedRect', rectrt)
cv2.imshow('Approx', imgapprox)
cv2.imshow('hull', imghull)
cv2.waitKey()
cv2.destroyAllWindows()
This is the result i get after executing the above code.