I've got images like this one:
I need to detect the center of this circular element:
(More precisely - I'm looking for the midpoint of the circular element)
Currently my code detect the mold (the plastic circle that holds the circular element) and select the rectangle ROI to focus the image into the relevant area:
import cv2
import imutils
import numpy as np
if __name__ == "__main__":
image = cv2.imread('Dart - Overview Image - with Film.bmp')
img = imutils.resize(image, width=700)
image = img
output = image.copy()
roi = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# cv2.imshow("Gray", gray)
# cv2.waitKey(0)
# detect circles in the image
circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1.2, 100)
# ensure at least some circles were found
if circles is not None:
# convert the (x, y) coordinates and radius of the circles to integers
circles = np.round(circles[0, :]).astype("int")
# loop over the (x, y) coordinates and radius of the circles
for (x, y, r) in circles:
# draw the circle in the output image, then draw a rectangle
# corresponding to the center of the circle
cv2.circle(output, (x, y), r, (0, 255, 0), 2)
cv2.rectangle(output, (x - 2, y - 2), (x + 2, y + 2), (0, 128, 255), -1)
roi = roi[y - r: y + r, x - r: x + r]
cv2.imshow("img", roi)
cv2.waitKey(0)
This will show this image:
Now, I'm trying to find the midpoint of the circular element inside this ROI image by detecting the circular element but It's challenged for me, I've try this methods:
gray_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray_roi, (3, 3), 0)
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_OTSU + cv2.THRESH_BINARY_INV)[1]
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)
for c in cnts:
(x, y), radius = cv2.minEnclosingCircle(c)
cv2.circle(roi, (int(x), int(y)), int(radius), (35, 255, 12), 3)
cv2.circle(roi, (int(x), int(y)), 1, (35, 255, 12), 2)
print(x, y)
break
# Find Canny edges
edged = cv2.Canny(roi, 30, 121, apertureSize=3, L2gradient=True)
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_NONE)
cv2.imshow('Canny Edges After Contouring', edged)
cv2.waitKey(0)
And got this output:
I've also tried to change the threshold parameters in the cv2.Canny function but it's didn't give me better results.
Thanks a lot!
Related
I'm am trying to get all white areas in the below image (first / second image), and draw a bounding box.
After thresholding, canny etc I get a decent mask, although needs improving. However not sure how to get all white areas once isolate. As you can see the current scrip finds the major contour (third image)
img = cv2.imread("img.png")
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(img_gray, 128, 255, cv2.THRESH_BINARY)
img_blur = cv2.GaussianBlur(thresh, (5, 5), 2)
img_canny = cv2.Canny(img_blur, 0, 0)
cnts = cv2.findContours(img_canny, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
r1, r2 = sorted(cnts, key=cv2.contourArea)[-3:-1]
x, y, w, h = cv2.boundingRect(np.r_[r1, r2])
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 2)
cv2.imshow("img", img)
cv2.waitKey(0)
I have tried grabbing all contours using..., but still only adds a single box. This should sort big to small?
cnts = imutils.grab_contours(cnts)
(cnts, _) = contours.sort_contours(cnts)
for C in cnts:
if cv2.contourArea(C) < 200:
continue
box = cv2.minAreaRect(C)
box = cv2.boxPoints(box) if imutils.is_cv2() else cv2.boxPoints(box)
box = perspective.order_points(box)
orig = img.copy()
cv2.drawContours(orig, [box.astype("int")], -1, (0, 255, 0), 1)
cv2.imshow("img", orig)
- Desired
To conclude, I'm trying to isolate the white spaces (yes needs some work), then add a box to all isolated white areas, eventually get box area etc.
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 currently doing a project on painting recognition using opencv-python.
Right now I am able to detect most of the paintings decently however the bounding boxes are rectangles that include a lot of background.
This is because the cv2.boundingRect() function finds the bounding rectangle with a perpendicular projection (afaik). However I want to find the best bounding box without detecting any background.
The main part of my code:
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
hue, saturation, value = cv2.split(hsv)
blurred_sat = cv2.GaussianBlur(saturation, (5, 5), 0)
edges = cv2.Canny(blurred_sat, 45, 100)
kernel = np.ones((3, 3), np.uint8)
dilate = cv2.dilate(edges, kernel, iterations=6)
erode = cv2.erode(dilate, kernel, iterations=2)
contours, hierarchy = cv2.findContours(erode, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = sorted(contours, key=cv2.contourArea, reverse=True)[:10]
# cv2.drawContours(frame, contours, -1, (255, 255, 0), 3)
for contour in contours:
area = cv2.contourArea(contour)
if area >= 3000:
x, y, w, h = cv2.boundingRect(contour)
subImg = frame[y:y+h, x:x+w]
cv2.rectangle(frame,
(x, y), (x + w, y + h),
(0, 255, 0),
2)
Current output image (video)
Desired output image with desired bounding box in red
After finding and sorting the contours, you need to use cv2.minAreaRect() function. This draws a rectangle enclosing each contour with the least area:
import numpy as np
# portion of code to find and sort contours
for contour in contours:
area = cv2.contourArea(contour)
if area >= 3000:
rect = cv2.minAreaRect(cnt)
box = cv2.boxPoints(rect)
box = np.int0(box)
cv2.drawContours(frame, [box], 0, (0, 255, 0), 2)
Note: cv2.minAreaRect() returns properties of the rotated rectangle (center, dimensions and angle of rotation). cv2.boxPoints() is used to obtain the vertices of the rectangle. This is then passed into cv2.drawContours() to draw them.
Have a look at the documentation
For details on the algorithm:
Link 1
Link 2
Update:
The following code approximates a quadrilateral around a contour and draws it on frame. The variable value determines how strong you want your approximations to be:
# use a value between 0 and 1
value = 0.02
for c in contours:
perimeter = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, value*perimeter, True)
if len(approx) == 4:
cv2.drawContours(frame, [approx], 0, (0, 0, 255), 5)
I need to detect the points along these curves, in particular I need the position on the image of one of the two points starting from the left:
I tried to detect Hough Points like that:
import cv2
import numpy as np
# detect circles in the image
output = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1.2, 100)
# ensure at least some circles were found
if circles is not None:
# convert the (x, y) coordinates and radius of the circles to integers
circles = np.round(circles[0, :]).astype("int")
# loop over the (x, y) coordinates and radius of the circles
for (x, y, r) in circles:
# draw the circle in the output image, then draw a rectangle
# corresponding to the center of the circle
cv2.circle(output, (x, y), r, (0, 255, 0), 4)
cv2.rectangle(output, (x - 5, y - 5), (x + 5, y + 5), (0, 128, 255), -1)
# show the output image
cv2.imshow("output", np.hstack([image, output]))
cv2.waitKey(0)
But it doesn't get the right points and I suppose that this is because the points are positioned along the two curves. Is there a way to detect points along a curve?
Just found the solution following the comment of #HansHirse. I followed these steps:
Color threshold
Erosion
Finding contours
This is the code:
import cv2
import numpy as np
hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv_image, lowerb=(85, 0, 0), upperb=(95, 255, 255))
# Masking with green
imask = mask > 0
green = np.zeros_like(hsv_image, np.uint8)
green[imask] = hsv_image[imask]
kernel = np.ones((8, 8), np.uint8)
erosion = cv2.erode(green, kernel, iterations=1)
gray = cv2.cvtColor(erosion, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
# Filter out large non-connecting objects
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
area = cv2.contourArea(c)
if area < 500:
cv2.drawContours(thresh,[c],0,0,-1)
# Morph open using elliptical shaped kernel
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3,3))
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=3)
# Find circles
cnts = cv2.findContours(opening, cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)[-2]
for c in cnts:
area = cv2.contourArea(c)
if area > 0 and area < 50:
((x, y), r) = cv2.minEnclosingCircle(c)
cv2.circle(image, (int(x), int(y)), int(r), (36, 255, 12), 2)
cv2.imshow('image', image)
cv2.waitKey()
The result is the following image where the dots are represented by the green circles
I inserted an ellipse into the image, I know its center and I would like to add a line that divides the ellipse into two halves. This is my actuall code:
import cv2
import imutils
img = cv2.imread('labrador.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 100 , 255, cv2.THRESH_BINARY)[1]
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
cnt = max(cnts, key=cv2.contourArea)
ellipse = cv2.fitEllipse(cnt)
cv2.ellipse(img, ellipse, (0, 255, 0), 3)
x, y = ellipse[0]
cv2.circle(img, (int(x), int(y)), 10, (255, 255, 255), -1)
cv2.imshow("dog_with_ellipse", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
Original image:
Original image with an ellipse
And result should look like:
How could I do that? And how to calculate the two points that could be used to divide an ellipse by length?
Add rotated labrador image:
Here is one way in Python/OpenCV. I note that the findEllipse() function returns the center, minor and major axes diameters and the angle.
Input:
import cv2
import numpy as np
import math
# read input
img = cv2.imread('labrador.jpg')
# convert to gray
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# threshold
thresh = cv2.threshold(gray, 100 , 255, cv2.THRESH_BINARY)[1]
# find largest contour
contours = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
big_contour = max(contours, key=cv2.contourArea)
# fit contour to ellipse and get ellipse center, minor and major diameters and angle in degree
ellipse = cv2.fitEllipse(big_contour)
(xc,yc),(d1,d2),angle = ellipse
print(xc,yc,d1,d1,angle)
# draw ellipse
result = img.copy()
cv2.ellipse(result, ellipse, (0, 255, 0), 3)
# draw circle at center
xc, yc = ellipse[0]
cv2.circle(result, (int(xc),int(yc)), 10, (255, 255, 255), -1)
# draw vertical line
# compute major radius
rmajor = max(d1,d2)/2
if angle > 90:
angle = angle - 90
else:
angle = angle + 90
print(angle)
xtop = xc + math.cos(math.radians(angle))*rmajor
ytop = yc + math.sin(math.radians(angle))*rmajor
xbot = xc + math.cos(math.radians(angle+180))*rmajor
ybot = yc + math.sin(math.radians(angle+180))*rmajor
cv2.line(result, (int(xtop),int(ytop)), (int(xbot),int(ybot)), (0, 0, 255), 3)
# save results
cv2.imwrite("labrador_ellipse.jpg", result)
cv2.imshow("labrador_thresh", thresh)
cv2.imshow("labrador_ellipse", result)
cv2.waitKey(0)
cv2.destroyAllWindows()
Result:
ADDITION:
For this image:
the code generates this result: