Im using following code to draw circle around face.
for (x, y, w, h) in faces:
cv2.circle(img, ( int((x + x + w )/2), int((y + y + h)/2 )), int (h / 2), (0, 255, 0), 5)
however the thickness of drawn circle is entirely green. Is there any ways to make few percentage (say 30% )of the circle to be pink?
As I suggested in the comments, you could use cv2.ellipse() to draw the two arcs individually. For example:
import numpy as np
import cv2
img = np.ones((400,400,3), np.uint8) * 255
# See:
# http://docs.opencv.org/2.4/modules/core/doc/drawing_functions.html#cv2.ellipse
# http://docs.opencv.org/3.1.0/dc/da5/tutorial_py_drawing_functions.html
circ_center = (200,200)
circ_radius = 150
circ_thick = 12
circ_axes = (circ_radius,circ_radius)
# cv2.ellipse(img, center, axes, angle, startAngle, endAngle, color[, thickness[, lineType[, shift]]])
cv2.ellipse(img, circ_center, circ_axes, 0, 0, 90, (255,0,0), circ_thick, cv2.LINE_AA)
cv2.ellipse(img, circ_center, circ_axes, 0, 90, 360, (0,255,0), circ_thick, cv2.LINE_AA)
cv2.imshow("Image", img)
cv2.imwrite("circ1.png", img)
cv2.waitKey()
Produces:
Now, the arcs have rounded edges. This may or may not be an issue for you. I'm not sure if there's a better way in OpenCV, but one way that I've created thick lines with flat edges is to build the thick lines out of many thin lines.
For example:
import numpy as np
import cv2
img = np.ones((400,400,3), np.uint8) * 255
# See:
# http://docs.opencv.org/2.4/modules/core/doc/drawing_functions.html#cv2.ellipse
# http://docs.opencv.org/3.1.0/dc/da5/tutorial_py_drawing_functions.html
circ_center = (200,200)
circ_radius = 150
circ_thick = 12
def draw_arc(img, center, rad, angle, startAngle, endAngle, color, thickness, lineType, thick=1):
for r in range(rad,rad+thickness):
cv2.ellipse(img, center, (r,r), angle, startAngle, endAngle, color, thick, lineType)
draw_arc(img, circ_center, circ_radius, 0, 0, 90, (255,0,0), circ_thick, cv2.LINE_AA)
draw_arc(img, circ_center, circ_radius, 0, 90, 360, (0,255,0), circ_thick, cv2.LINE_AA)
cv2.imshow("Image", img)
cv2.imwrite("circ2.png", img)
cv2.waitKey()
Produces:
You can adjust the starting and ending points of the coloring by adjusting the startAngle and endAngle parameters. There are a few other parameters you may want to adjust in there, but this should give you an idea of one approach.
You could also just draw a complete circle and layer an arc on top of it corresponding to what you want colored different, which may be easier in the end.
Related
I'm trying to cut a piece from a circle using Python along with opencv, here is the code
firstly, I constructed the circle
layer1 = np.zeros((48, 48, 4))
cv2.circle(layer1, (24, 24), 23, (0, 0, 0, 255), -1)
res = layer1[:]
and I got
and then, I drew a smaller square on it
start_point = (24, 0); end_point = (48, 24); color = (255, 0, 0)
cv2.rectangle(res, start_point, end_point, color, -1)
which gives
similarly, I drew a triangle on the circle
pt1 = (24, 0); pt2 = (48, 0); pt3 = (24, 24)
triangle_cnt = np.array( [pt1, pt2, pt3] )
cv2.drawContours(res, [triangle_cnt], 0, (255,0,0), -1)
which gives
I can go along this way to draw a smaller triangle, 1/16, 1/32 and so on.
I have to do the math manually to get the vertices.
Is there a smarter (more elegant) way to do the job?
import cv2
import numpy as np
# Colors (B, G, R)
WHITE = (255, 255, 255)
BLACK = (0, 0, 0)
# Create new blank 300x150 white image
width, height = 800, 500
img = np.zeros((height, width, 3), np.uint8)
img[...] = BLACK
center = (width//2, height//2)
axes = (200, 200) # axes radius, keep equal to draw circle.
angle = 0 #clockwise first axis
startAngle = 0
endAngle = 90
color = WHITE
img = cv2.ellipse(img, center, axes, angle, startAngle, endAngle, color, thickness=-1)
cv2.imshow('image', img)
cv2.waitKey(-1)
You can play with startAngle and endAngle to change the position of the white part.
Another option is to change the angle option (to -90 for example to rotate counter clockwise).
EDIT to show the different end angles add
img = cv2.ellipse(img, center, axes, angle, startAngle, endAngle/2, (255, 0, 0), thickness=-1)
img = cv2.ellipse(img, center, axes, angle, startAngle, endAngle/4, (0, 255, 0), thickness=-1)
img = cv2.ellipse(img, center, axes, angle, startAngle, endAngle/8, (0, 0, 255), thickness=-1)
New image: test image
I'm trying to quantify the distance between two contours in a video of a microvessel (see snapshot)
Image analysis structure
Right now I'm only able to select for one contour (which is outlined) and I'm acquiring dimensions from this outline, but what I'd like to select for is the top and bottom contour of the structure and measure the distance (labeled with an orange line and A in the snapshot).
Any suggestions as to do this? My code for this video analysis is the following. Thanks for the help in advance!:
import cv2
import pandas as pd
import numpy as np
import imutils
from scipy.spatial import distance as dist
from imutils import perspective
from imutils import contours
videocapture = cv2.VideoCapture('RTMLV.mp4')
def safe_div(x,y):
if y==0: return 0
return x/y
def nothing(x):
pass
def rescale_frame(frame, percent=100): #make the video windows a bit smaller
width = int(frame.shape[1]*percent/100)
height = int(frame.shape[0]*percent/100)
dim = (width, height)
return cv2.resize(frame, dim, interpolation=cv2.INTER_AREA)
if not videocapture.isOpened():
print("Unable to open video")
exit()
windowName="Vessel Tracking"
cv2.namedWindow(windowName)
# Sliders to adjust image
cv2.createTrackbar("Threshold", windowName, 75, 255, nothing)
cv2.createTrackbar("Kernel", windowName, 5, 30, nothing)
cv2.createTrackbar("Iterations", windowName, 1, 10, nothing)
showLive=True
while(showLive):
ret, frame=videocapture.read()
frame_resize=rescale_frame(frame)
if not ret:
print("Cannot capture the frame")
exit()
thresh = cv2.getTrackbarPos("Threshold", windowName)
ret,thresh1 = cv2.threshold(frame_resize, thresh, 255, cv2.THRESH_BINARY)
kern = cv2.getTrackbarPos("Kernel", windowName)
kernel = np.ones((kern, kern), np.uint8) # square image kernel used for erosion
itera=cv2.getTrackbarPos("Iterations", windowName)
dilation = cv2.dilate(thresh1, kernel, iterations=itera)
erosion = cv2.erode(dilation, kernel, iterations=itera) #refines all edges in the binary image
opening = cv2.morphologyEx(erosion, cv2.MORPH_OPEN, kernel)
closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel)
closing = cv2.cvtColor(closing, cv2.COLOR_BGR2GRAY)
contours,hierarchy = cv2.findContours(closing,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE) # find contours with simple approximation cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE
closing = cv2.cvtColor(closing,cv2.COLOR_GRAY2RGB)
cv2.drawContours(closing, contours, -1, (128,255,0), 1)
# focus on only the largest outline by area
areas = [] #list to hold all areas
for contour in contours:
ar = cv2.contourArea(contour)
areas.append(ar)
max_area = max(areas)
max_area_index = areas.index(max_area) # index of the list element with largest area
cnt = contours[max_area_index - 1] # largest area contour is usually the viewing window itself, why?
cv2.drawContours(closing, [cnt], 0, (0,0,255), 1)
def midpoint(ptA, ptB):
return ((ptA[0] + ptB[0]) * 0.5, (ptA[1] + ptB[1]) * 0.5)
# compute the rotated bounding box of the contour
orig = frame_resize.copy()
box = cv2.minAreaRect(cnt)
box = cv2.cv.BoxPoints(box) if imutils.is_cv2() else cv2.boxPoints(box)
box = np.array(box, dtype="int")
# order the points in the contour such that they appear
# in top-left, top-right, bottom-right, and bottom-left
# order, then draw the outline of the rotated bounding
# box
box = perspective.order_points(box)
cv2.drawContours(orig, [box.astype("int")], -1, (0, 255, 0), 1)
# loop over the original points and draw them
for (x, y) in box:
cv2.circle(orig, (int(x), int(y)), 5, (0, 0, 255), -1)
# unpack the ordered bounding box, then compute the midpoint
# between the top-left and top-right coordinates, followed by
# the midpoint between bottom-left and bottom-right coordinates
(tl, tr, br, bl) = box
(tltrX, tltrY) = midpoint(tl, tr)
(blbrX, blbrY) = midpoint(bl, br)
# compute the midpoint between the top-left and top-right points,
# followed by the midpoint between the top-right and bottom-right
(tlblX, tlblY) = midpoint(tl, bl)
(trbrX, trbrY) = midpoint(tr, br)
# draw the midpoints on the image
cv2.circle(orig, (int(tltrX), int(tltrY)), 5, (255, 0, 0), -1)
cv2.circle(orig, (int(blbrX), int(blbrY)), 5, (255, 0, 0), -1)
cv2.circle(orig, (int(tlblX), int(tlblY)), 5, (255, 0, 0), -1)
cv2.circle(orig, (int(trbrX), int(trbrY)), 5, (255, 0, 0), -1)
# draw lines between the midpoints
cv2.line(orig, (int(tltrX), int(tltrY)), (int(blbrX), int(blbrY)),(255, 0, 255), 1)
cv2.line(orig, (int(tlblX), int(tlblY)), (int(trbrX), int(trbrY)),(255, 0, 255), 1)
cv2.drawContours(orig, [cnt], 0, (0,0,255), 1)
# compute the Euclidean distance between the midpoints
dA = dist.euclidean((tltrX, tltrY), (blbrX, blbrY))
dB = dist.euclidean((tlblX, tlblY), (trbrX, trbrY))
# compute the size of the object
P2M4x = 1.2
P2M10x = 3.2
P2M20x = 6
pixelsPerMetric = P2M10x # Pixel to micron conversion
dimA = dA / pixelsPerMetric
dimB = dB / pixelsPerMetric
dimensions = [dimA, dimB]
# draw the object sizes on the image
cv2.putText(orig, "{:.1f}um".format(dimA), (int(tltrX - 15), int(tltrY - 10)), cv2.FONT_HERSHEY_SIMPLEX, 0.65, (255, 255, 255), 2)
cv2.putText(orig, "{:.1f}um".format(dimB), (int(trbrX + 10), int(trbrY)), cv2.FONT_HERSHEY_SIMPLEX, 0.65, (255, 255, 255), 2)
# compute the center of the contour
M = cv2.moments(cnt)
cX = int(safe_div(M["m10"],M["m00"]))
cY = int(safe_div(M["m01"],M["m00"]))
# draw the contour and center of the shape on the image
cv2.circle(orig, (cX, cY), 5, (255, 255, 255), -1)
cv2.putText(orig, "center", (cX - 20, cY - 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
cv2.imshow(windowName, orig)
cv2.imshow('', closing)
if cv2.waitKey(30)>=0:
showLive=False
videocapture.release()
cv2.destroyAllWindows()
Edits have been made to this answer in reponse to the new test image that was added to the post.
I was unable to segment the blood vessel in the test image using the code that you uploaded. I segmented the image by using manual annotation and the GrabCut algorithm.
This is the code that I used for the manual segmentation:
import cv2, os, numpy as np
import time
# Plot with Matplotlib
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
img_path = '/home/stephen/Desktop/0lszR.jpg'
img = cv2.imread(img_path)
img = img[420:1200, :]
h,w,_ = img.shape
mask = np.zeros((h,w), np.uint8)
mask[:] = 2
src = img.copy()
h,w,_ = img.shape
drawing = src.copy()
# Mouse callback function
global k, px, py
k = 0
px, py = 0,0
def callback(event, x, y, flags, param):
global k, px, py
print(x,y, k, px, py)
if k == 115: # 's' for sure background
if px+py!=0:
cv2.line(img, (x,y), (px, py), (255,255,0), 8)
cv2.line(mask, (x,y), (px, py), 0, 8)
if k == 116: # 't' for sure foreground
if px+py!=0:
cv2.line(img, (x,y), (px, py), (0,255,255), 8)
cv2.line(mask, (x,y), (px, py), 1, 8)
else: print(px, py)
px, py = x,y
#if k != 115 or 116: px, py = 0,0
cv2.namedWindow('img')
cv2.setMouseCallback('img', callback)
while k != 27:
cv2.imshow('img', img)
k_temp = cv2.waitKey(1)
if k_temp!=-1: k = k_temp
cv2.destroyAllWindows()
After I had found the segmented image, I used the function np.nonzero() to find the tops and bottoms of the columns:
This is the code that I used to find the width:
# Initialize parameters for the GrabCut algorithm
bgdModel = np.zeros((1,65),np.float64)
fgdModel = np.zeros((1,65),np.float64)
# Apply GrabCut
out_mask = mask.copy()
out_mask, _, _ = cv2.grabCut(src,out_mask,None,bgdModel,fgdModel,1,cv2.GC_INIT_WITH_MASK)
out_mask = np.where((out_mask==2)|(out_mask==0),0,1).astype('uint8')
# Open the mask to fill in the holes
out_img = src*out_mask[:,:,np.newaxis]
flip_mask = cv2.flip(out_mask, 0)
# Find the distances
distances = []
for col_num in range(src.shape[1]-1):
col = out_mask[:, col_num:col_num+1]
flip_col = flip_mask[:, col_num:col_num+1]
top = np.nonzero(col)[0][0]
bottom = h-np.nonzero(flip_col)[0][0]
if col_num % 12 == 0:
cv2.line(drawing, (col_num, top), (col_num, bottom), (234,345,34), 4)
distances.append(bottom-top)
f, axarr = plt.subplots(2,3, sharex=True)
axarr[0,0].imshow(src)
axarr[0,1].imshow(out_mask)
axarr[0,2].imshow(drawing)
axarr[1,0].imshow(img)
axarr[1,1].imshow(out_img)
axarr[1,2].plot(distances)
axarr[0,0].set_title("Source")
axarr[0,1].set_title('Mask from GrabCut')
axarr[0,2].set_title('Widths')
axarr[1,0].set_title('Manual Annotation')
axarr[1,1].set_title('GrabCut Mask')
axarr[1,2].set_title('Graph of Width')
axarr[0,0].axis('off')
axarr[0,1].axis('off')
axarr[1,0].axis('off')
axarr[1,1].axis('off')
axarr[1,2].axis('off')
axarr[0,2].axis('off')
plt.show()
I want to blur a rectangle (with rounded corners) in an image using python pillow. I already found a way to blur only a certain part of a picture.
img = Image.open('assets/images/image.png')
x, y = 300, 1600
cropped_img = img.crop((x, y, 1000, 2600))
blurred_img = cropped_img.filter(ImageFilter.GaussianBlur(20))
img.paste(blurred_img, (x, y))
img.save('assets/images/new.png')
img.show()
Furthermore I found a method to add rounded corners on a rectangle(Transparency issues drawing a rectangle with rounded corners)
def round_corner(radius):
corner = Image.new('RGBA', (radius, radius), (0, 0, 0, 0))
draw = ImageDraw.Draw(corner)
draw.pieslice((0, 0, radius * 2, radius * 2), 180, 270)
return corner
def round_rectangle(rectangle, radius):
corner = round_corner(radius)
rectangle.paste(corner, (0, 0))
rectangle.paste(corner.rotate(90), (0, rectangle.size[1] - radius))
rectangle.paste(corner.rotate(180), (rectangle.size[0] - radius, rectangle.size[1] - radius))
rectangle.paste(corner.rotate(270), (rectangle.size[0] - radius, 0))
return rectangle
Unfortunately, I can't find a way to combine these two source codes so that they work.
My Example Image:
What you need to do, is essentially to create a mask for the Image.paste() that only pastes those parts of the blurred image that lie inside the rounded rectangle.
import PIL
from PIL import Image
from PIL import ImageFilter
from PIL import ImageDraw
# when using an image as mask only the alpha channel is important
solid_fill = (50,50,50,255)
def create_rounded_rectangle_mask(rectangle, radius):
# create mask image. all pixels set to translucent
i = Image.new("RGBA",rectangle.size,(0,0,0,0))
# create corner
corner = Image.new('RGBA', (radius, radius), (0, 0, 0, 0))
draw = ImageDraw.Draw(corner)
# added the fill = .. you only drew a line, no fill
draw.pieslice((0, 0, radius * 2, radius * 2), 180, 270, fill = solid_fill)
# max_x, max_y
mx,my = rectangle.size
# paste corner rotated as needed
# use corners alpha channel as mask
i.paste(corner, (0, 0), corner)
i.paste(corner.rotate(90), (0, my - radius),corner.rotate(90))
i.paste(corner.rotate(180), (mx - radius, my - radius),corner.rotate(180))
i.paste(corner.rotate(270), (mx - radius, 0),corner.rotate(270))
# draw both inner rects
draw = ImageDraw.Draw(i)
draw.rectangle( [(radius,0),(mx-radius,my)],fill=solid_fill)
draw.rectangle( [(0,radius),(mx,my-radius)],fill=solid_fill)
return i
Mask:
Apply the mask to your image:
img = Image.open('pic.jpg')
x, y = 300, 160
radius = 75
cropped_img = img.crop((x, y, 600, 600))
# the filter removes the alpha, you need to add it again by converting to RGBA
blurred_img = cropped_img.filter(ImageFilter.GaussianBlur(20),).convert("RGBA")
# paste blurred, uses alphachannel of create_rounded_rectangle_mask() as mask
# only those parts of the mask that have a non-zero alpha gets pasted
img.paste(blurred_img, (x, y), create_rounded_rectangle_mask(cropped_img,radius))
img.save('new2.png')
img.show()
I changed some dimensions and paths. Your code lacked the imports, I completed it to a minimal verifyable complete example.
The picture and code below is a toy example that should reflect an experiment I am running.
I would like to extract a disk corresponding to the boundary in the picture where the pixels intensities are the same or similar (in this example the bluish disk)
Using HoughCircles procedure, I can extract the center of the most probable circle of the picture.
From there I would like to probe 360° from the center at the various radius (higher or lower) from the detected center to define the boundaries (max radius and min radius) of the bluish color in the picture below.
How can I do that?
I try to analyze the histogram by applying multiple masks without success.
The green circle is the one detected with HoughCircles, the blue and red circle are the +/- 15% radius circle.
import cv2
import numpy as np
from matplotlib import pyplot as plt
image = cv2.imread("./picture.jpg")
output = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 2, 800,
param1=300,
param2=1,
minRadius=100,
maxRadius=0)
if circles is not None:
# convert the (x, y) coordinates and radius of the circles to integers
circles = np.round(circles[0, :]).astype("int")
output = image.copy()
# 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)
# create the mask and explore histograms
# height,width,depth = output.shape
# mask = np.zeros((height,width), np.uint8)
# cv2.circle(mask, (x, y), int(round(r - (r* .15))), 1, thickness=-1)
# output = cv2.bitwise_and(output, output, mask=mask)
# hist_full = cv2.calcHist([output],[0],None,[256],[0,256])
# hist_mask = cv2.calcHist([output],[0],mask,[256],[0,256])
# plt.hist(image.ravel(),256,[0,256]); plt.show()
# plt.plot(hist_full),
# plt.plot(hist_mask)
# plt.xlim([0,256])
# plt.show()
cv2.circle(output, (x, y), int(round(r * 1.15)), (255, 0, 0), 2)
cv2.circle(output, (x, y), int(round(r - (r* .15))), (0, 0, 255), 2)
# show the output image
cv2.imshow("output", np.hstack([image, output]))
cv2.waitKey(0)
I resized the disk image, because the origin is too large. So you may modify the parameters in the function.
The source:
I found in S(HSV), the disk is more clear, so I did canny in "S".
The result:
You can reproduce the result using the code.
#!/usr/bin/python3
# 2017.11.21 21:03:09 CST
# 2017.11.22 23:21:42 CST
# 2017.11.25 16:32:46 CST
import cv2
import numpy as np
img = cv2.imread("disk2.png")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
## Canny edge in S(HSV)
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
h,s,v = cv2.split(hsv)
canny = cv2.Canny(s, 30, 200)
## The inner circle using gray
circles1 = cv2.HoughCircles(gray, method = cv2.HOUGH_GRADIENT,
dp = 2, minDist = 100,
param1=200, param2=100,
minRadius=80, maxRadius=200)
## The outer circle using canny
circles2 = cv2.HoughCircles(canny, method = cv2.HOUGH_GRADIENT,
dp = 2, minDist = 100,
param1=200, param2=100,
minRadius=200, maxRadius=0)
x1,y1, r1 = circles1[0][0]
x2,y2, r2 = circles2[0][0]
## create the mask
mask = np.zeros_like(canny)
cv2.circle(mask, (x2, y2), r2, 255, -1)
cv2.circle(mask, (x1, y1), r1, 0, -1)
## crop
imask = mask > 0
masked = np.zeros_like(img)
masked[imask] = img[imask]
cv2.imshow("canny", canny)
cv2.imshow("mask", mask)
cv2.imshow("croped", masked)
cv2.waitKey()
cv2.destroyAllWindows()
I'm trying to detect circles from the image using hough transform
my code :
import numpy as np
import cv2
image = cv2.imread("C:/Users/Anmol/Desktop/your_file.bmp")
output = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1.2,10)
cv2.waitKey(0)
print (circles)
# 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)
stops at the line
circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1.2,10)
I left my code running for about 5 hours but still its not going ahead of this line. It is not giving any error.
Plzz guide me what to do.
I suggest downscaling the input image if it's really large, and running a blur filter. Both of these will speed up Hough tremendously.