Develop Reference

Is there a function to get a grid detection of only solar panels in a solar image using openCV?

I'm working in a script using different OpenCV operations for processing an image with solar panels.
Original image:
Final image:
Can anybody help me with the code to detect only the grid lines in the image, removing the other area.
My code is the following:
image = cv2.imread("solar3.jpg")
cv2.imshow("Image", image)
image = cv2.resize(image, (482,406))
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.imshow("gray", gray)
blur = cv2.GaussianBlur(gray, (0,0), 2)
cv2.imshow("blur", blur)
lap = cv2.Laplacian(blur, cv2.CV_32F)
cv2.imshow("lap",lap)
_,thresh = cv2.threshold(lap, 0, 0, cv2.THRESH_TOZERO)
cv2.imshow("thresh", thresh)
thresh = thresh.astype(np.uint8)
contours, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
c = 0
for i in contours:
area = cv2.contourArea(i)
if area > 1000/2:
cv2.drawContours(image, contours, c, (0, 255, 0), 2)
c+=1
cv2.imshow("Final Image", image)
cv2.waitKey(0)
Can anybody help me with the code of detecting all the grids in the image.

How do I detect only the black rectangle that appears in the reference image with OpenCV

I need to detect only the black rectangle that appears there, but for some reason my code does not detect it but it does detect many other things.
import cv2
img=cv2.imread('vision.png') #read image
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
Blur=cv2.GaussianBlur(gray,(5,5),1) #apply blur to roi
Canny=cv2.Canny(Blur,10,50) #apply canny to roi
#Find my contours
contours =cv2.findContours(Canny,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)[0]
cntrRect = []
for i in contours:
epsilon = 0.05*cv2.arcLength(i,True)
approx = cv2.approxPolyDP(i,epsilon,True)
if len(approx) == 4:
cv2.drawContours(img,cntrRect,-1,(0,255,0),2)
cv2.imshow('Image Rect ONLY',img)
cntrRect.append(approx)
cv2.waitKey(0)
cv2.destroyAllWindows()
How do I detect only the black rectangle that appears in the image
But this code detect more rectangles and I don't want whis, but I only want detect the black countour rectangle

Here is one way to do that in Python/OpenCV.
Threshold the image. Then use morphology to fill out the rectangle. Then get the largest contour and draw on the input.
Input:
import cv2
import numpy as np
# load image
img = cv2.imread("black_rectangle_outline.png")
# convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# threshold
thresh = cv2.threshold(gray, 30, 255, cv2.THRESH_BINARY)[1]
# apply close morphology
kernel = np.ones((111,111), np.uint8)
morph = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
# invert so rectangle is white
morph = 255 - morph
# get largest contour and draw on copy of input
result = img.copy()
contours = cv2.findContours(morph, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
big_contour = max(contours, key=cv2.contourArea)
cv2.drawContours(result, [big_contour], 0, (255,255,255), 1)
# write result to disk
cv2.imwrite("black_rectangle_outline_thresh.png", thresh)
cv2.imwrite("black_rectangle_outline_morph.png", morph)
cv2.imwrite("black_rectangle_outline_result.png", result)
# display results
cv2.imshow("THRESH", thresh)
cv2.imshow("MORPH", morph)
cv2.imshow("RESULT", result)
cv2.waitKey(0)
cv2.destroyAllWindows()
Threshold Image:
Morphology Image:
Result:

colour contour on grayscale image

I made a simple code to find the contour of an object in an image.
img = cv2.imread(file_path, cv2.IMREAD_GRAYSCALE)
blur = cv2.GaussianBlur(img_enhanced,(25,25),0) # apply blur for contour
ret, binary = cv2.threshold(blur,1,255,cv2.THRESH_BINARY) # apply threshold to blur image
contours, hierarchy = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # find countour
obj_index = contours.index(max(contours, key=len)) # find index of largest object
contour_img = cv2.drawContours(img, contours, obj_index, (0,255,0), 3) # draw coutour on original image
plt.imshow(contour_img)
plt.show()
The original image is already grayscale but anyway I applied cv2.IMREAD_GRAYSCALE when I import the image.
And I thought if I apply the grayscale image when I 'Draw' contour, with below syntax,
contour_img = cv2.drawContours(img, contours, obj_index, (0,255,0), 3)
I can have a grayscale image with colored contour, but it shows
weird colored image.
How can I draw a colored contour line on the grayscale image?
Thanks in advance
image sample: black background and white object

Based on my comment:
Try something like this and tell me if it works or not
Edit: I have changed the threshold range. cv2.threshold(blur,25,255,cv2.THRESH_BINARY)
img = cv2.imread(file_path, 1)
print(img.shape) # this should give you (img_h, img_w, 3)
img2 = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(img2,(25,25),0) # apply blur for contour
ret, binary = cv2.threshold(blur,25,255,cv2.THRESH_BINARY) # apply threshold to blur image
contours, hierarchy = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # find countour
obj_index = contours.index(max(contours, key=len)) # find index of largest object
contour_img = cv2.drawContours(img, contours, obj_index, (0,255,0), 3) # draw coutour on original image
plt.imshow(contour_img, cmap='gray')
plt.show()
plt.hist(blur.ravel(), bins=50)

How to select child contour in OpenCV and increase accuracy using python

I'm really new to OpenCV. :) I have been working on this for almost an entire day. After hours of sleepless work I would like to know if I can further improve my code.
I have written some code to select only the black markings on the images. These black markings are child contours. Whilst my code is able to select some contours, it isn't accurate. You can see the code draws contours around the shadows along with black markings.
Code 1
At first I tried to use canny edge detection. But I was unable to overlay with the original image correctly.
import cv2
import numpy as np
image = cv2.imread('3.jpg')
image = cv2.resize(image, (500, 500))
image2 = image
cv2.waitKey(0)
# Grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Find Canny edges
edged = cv2.Canny(gray, 30, 200)
cv2.waitKey(0)
contours, hierarchy = cv2.findContours(edged, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
cv2.imshow('Canny', edged)
cv2.waitKey(0)
# print("Number of Contours found = " + str(len(contours)))
cv2.drawContours(image2, contours, -1, (0, 255, 0), 3)
cv2.imshow('Contours', image2)
cv2.waitKey(0)
cv2.destroyAllWindows()
Code 2
I was able to improve on Code 1 to be far more accurate. You should be able to see that it now only selects half of the thumb, none of the other fingers and it doesn't select the indent on the background.
Additionally changing the background of the image also increases the accuracy of the result.
import cv2
import numpy as np
image = cv2.imread('3.jpg', 0)
image2 = cv2.imread('3.jpg')
image = cv2.resize(image, (500, 500))
image2 = cv2.resize(image2, (500, 500))
cv2.waitKey(0)
ret, thresh_basic = cv2.threshold(image, 100, 255, cv2.THRESH_BINARY)
cv2.imshow("Thresh basic", thresh_basic)
# Taking a matrix of size 5 as the kernel
kernel = np.ones((5, 5), np.uint8)
img_erosion = cv2.erode(thresh_basic, kernel, iterations=1)
#####################
ret, thresh_inv = cv2.threshold(img_erosion, 100, 255, cv2.THRESH_BINARY_INV)
cv2.imshow("INV", thresh_inv)
#####################
# Find Canny edges
edged = cv2.Canny(img_erosion, 30, 200)
cv2.waitKey(0)
contours, hierarchy = cv2.findContours(edged, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
cv2.imshow('Canny', edged)
cv2.waitKey(0)
# print("Number of Contours found = " + str(len(contours)))
cv2.imshow('Original', image2)
cv2.drawContours(image2, contours, -1, (0, 255, 0), 3)
cv2.imshow('Contours', image2)
cv2.waitKey(0)
cv2.destroyAllWindows()
Can I improve my code further?

Why is drawing contours not working in image after erosion and dilation?

I have and portrait image. To which I:
Convert to Grayscale
Then convert to binary
Then I used morphological erosion operation.
Then I used morphological dilation operation.
Now, as last process, I am trying to find out the contours and drawing them. However, contours are getting found and drawn too, but, it is only getting drawn in white color, which made it looked like, contours is not drawn at all. What am I doing wrong??
Here is my code:
import numpy as np
import cv2
img = cv2.imread("Test1.jpg")
image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv2.imshow("GrayScaled", image)
cv2.waitKey(0)
ret, thresh = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY_INV)
cv2.imshow("Black&White", thresh)
cv2.waitKey(0)
kernel1 = np.ones((2,2),np.uint8)
erosion = cv2.erode(thresh,kernel1,iterations = 4)
cv2.imshow("AfterErosion", erosion)
cv2.waitKey(0)
kernel2 = np.ones((1,1),np.uint8)
dilation = cv2.dilate(erosion,kernel2,iterations = 5)
cv2.imshow("AfterDilation", dilation)
cv2.waitKey(0)
contours, hierarchy = cv2.findContours(dilation, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
cv2.drawContours(dilation, contours, -1, (255, 0, 0), 2)
cv2.imshow("Contours", dilation)
cv2.waitKey(0)
cv2.destroyAllWindows()
Here are images step by step:
Original Image:
GrayScaled Image:
Binary Image:
After Erosion:
After Dilation:
Contour:
I am defining the color of the contours boundary to be red in above code. So, why is it not showing red boundaries??