I am creating a program which allows a user to annotate images with points.
This program allows user to zoom in an image so user can annotate more precisely.
Program zooms in an image doing the following:
Find the center of image
Find minimum and maximum coordinates of new cropped image relative to center
Crop image
Resize the image to original size
For this I have written the following Python code:
import cv2
def zoom_image(original_image, cut_off_percentage, list_of_points):
height, width = original_image.shape[:2]
center_x, center_y = int(width/2), int(height/2)
half_new_width = center_x - int(center_x * cut_off_percentage)
half_new_height = center_y - int(center_y * cut_off_percentage)
min_x, max_x = center_x - half_new_width, center_x + half_new_width
min_y, max_y = center_y - half_new_height, center_y + half_new_height
#I want to include max coordinates in new image, hence +1
cropped = original_image[min_y:max_y+1, min_x:max_x+1]
new_height, new_width = cropped.shape[:2]
resized = cv2.resize(cropped, (width, height))
translate_points(list_of_points, height, width, new_height, new_width, min_x, min_y)
I want to resize the image to original width and height so user always works on same "surface"
regardless of how zoomed image is.
The problem I encounter is how to correctly scale points (annotations) when doing this. My algorithm to do so was following:
Translate points on original image by subtracting min_x from x coordinate and min_y from y coordinate
Calculate constants for scaling x and y coordinates of points
Multiply coordinates by constants
For this I use the following Python code:
import cv2
def translate_points(list_of_points, height, width, new_height, new_width, min_x, min_y):
#Calculate constants for scaling points
scale_x, scale_y = width / new_width, height / new_height
#Translate and scale points
for point in list_of_points:
point.x = (point.x - min_x) * scale_x
point.y = (point.y - min_y) * scale_y
This code doesn't work. If I zoom in once, it is hard to detect the offset of pixels but it happens. If I keep zooming in, it will be much easier to detect the "drift" of points. Here are images to provide examples. On original image (1440x850) I places a point in the middle of blue crosshair. The more I zoom in the image it is easier to see that algorithm doesn't work with bigger cut-ofs.
Original image. Blue crosshair is middle point of an image. Red angles indicate what will be borders after image is zoomed once
Image after zooming in once.
Image after zooming in 5 times. Clearly, green point is no longer in the middle of image
The cut_off_percentage I used is 15% (meaning that I keep 85% of width and height of original image, calculated from the center).
I have also tried the following library: Augmentit python library
Library has functions for cropping images and resizing them together with points. Library also causes the points to drift. This is expected since the code I implemented and library's functions use the same algorithm.
Additionally, I have checked whether this is a rounding problem. It is not. Library rounds the points after multiplying coordinates with scales. Regardless on how they are rounded, points are still off by 4-5 px. This increases the more I zoom in the picture.
EDIT: A more detailed explanation is given here since I didn't understand a given answer.
The following is an image of right human hand.
Image of a hand in my program
Original dimension of this image is 1440 pixels in width and 850 pixels in height. As you can see in this image, I have annotated right wrist at location (756.0, 685.0). To check whether my program works correctly, I have opened this exact image in GIMP and placed a white point at location (756.0, 685.0). The result is following:
Image of a hand in GIMP
Coordinates in program work correctly. Now, if I were to calculate parameters given in first answer according to code given in first answer I get following:
vec = [756, 685]
hh = 425
hw = 720
cov = [720, 425]
These parameters make sense to me. Now I want to zoom the image to scale of 1.15. I crop the image by choosing center point and calculating low and high values which indicate what rectangle of image to keep and what to cut. On the following image you can see what is kept after cutting (everything inside red rectangle).
What is kept when cutting
Lows and highs when cutting are:
xb = [95,1349]
yb = [56,794]
Size of cropped image: 1254 x 738
This cropped image will be resized back to original image. However, when I do that my annotation gets completely wrong coordinates when using parameters described above.
After zoom
This is the code I used to crop, resize and rescale points, based on the first answer:
width, height = image.shape[:2]
center_x, center_y = int(width / 2), int(height / 2)
scale = 1.15
scaled_width = int(center_x / scale)
scaled_height = int(center_y / scale)
xlow = center_x - scaled_width
xhigh = center_x + scaled_width
ylow = center_y - scaled_height
yhigh = center_y + scaled_height
xb = [xlow, xhigh]
yb = [ylow, yhigh]
cropped = image[yb[0]:yb[1], xb[0]:xb[1]]
resized = cv2.resize(cropped, (width, height), cv2.INTER_CUBIC)
#Rescaling poitns
cov = (width / 2, height / 2)
width, height = resized.shape[:2]
hw = width / 2
hh = height / 2
for point in points:
x, y = point.scx, point.scy
x -= xlow
y -= ylow
x -= cov[0] - (hw / scale)
y -= cov[1] - (hh / scale)
x *= scale
y *= scale
x = int(x)
y = int(y)
point.set_coordinates(x, y)
So this really is an integer rounding issue. It's magnified at high zoom levels because being off by 1 pixel at 20x zoom throws you off much further. I tried out two versions of my crop-n-zoom gui. One with int rounding, another without.
You can see that the one with int rounding keeps approaching the correct position as the zoom grows, but as soon as the zoom takes another step, it rebounds back to being wrong. The non-rounded version sticks right up against the mid-lines (denoting the proper position) the whole time.
Note that the resized rectangle (the one drawn on the non-zoomed image) blurs past the midlines. This is because of the resize interpolation from OpenCV. The yellow rectangle that I'm using to check that my points are correctly scaling is redrawn on the zoomed frame so it stays crisp.
With Int Rounding
Without Int Rounding
I have the center-of-view locked to the bottom right corner of the rectangle for this demo.
import cv2
import numpy as np
# clamp value
def clamp(val, low, high):
if val < low:
return low;
if val > high:
return high;
return val;
# bound the center-of-view
def boundCenter(cov, scale, hh, hw):
# scale half res
scaled_hw = int(hw / scale);
scaled_hh = int(hh / scale);
# bound
xlow = scaled_hw;
xhigh = (2*hw) - scaled_hw;
ylow = scaled_hh;
yhigh = (2*hh) - scaled_hh;
cov[0] = clamp(cov[0], xlow, xhigh);
cov[1] = clamp(cov[1], ylow, yhigh);
# do a zoomed view
def zoomView(orig, cov, scale, hh, hw):
# calculate crop
scaled_hh = int(hh / scale);
scaled_hw = int(hw / scale);
xlow = cov[0] - scaled_hw;
xhigh = cov[0] + scaled_hw;
ylow = cov[1] - scaled_hh;
yhigh = cov[1] + scaled_hh;
xb = [xlow, xhigh];
yb = [ylow, yhigh];
# crop and resize
copy = np.copy(orig);
crop = copy[yb[0]:yb[1], xb[0]:xb[1]];
display = cv2.resize(crop, (width, height), cv2.INTER_CUBIC);
return display;
# draw vector shape
def drawVec(img, vec, pos, cov, hh, hw, scale):
con = [];
for point in vec:
# unpack point
x,y = point;
x += pos[0];
y += pos[1];
# here's the int version
# Note: this is the same as xlow and ylow from the above function
# x -= cov[0] - int(hw / scale);
# y -= cov[1] - int(hh / scale);
# rescale point
x -= cov[0] - (hw / scale);
y -= cov[1] - (hh / scale);
x *= scale;
y *= scale;
x = int(x);
y = int(y);
# add
con.append([x,y]);
con = np.array(con);
cv2.drawContours(img, [con], -1, (0,200,200), -1);
# font stuff
font = cv2.FONT_HERSHEY_SIMPLEX;
fontScale = 1;
fontColor = (255, 100, 0);
thickness = 2;
# draw blank
res = (800,1200,3);
blank = np.zeros(res, np.uint8);
print(blank.shape);
# draw a rectangle on the original
cv2.rectangle(blank, (100,100), (400,200), (200,150,0), -1);
# vectored shape
# comparison shape
bshape = [[100,100], [400,100], [400,200], [100,200]];
bpos = [0,0]; # offset
# random shape
vshape = [[148, 89], [245, 179], [299, 67], [326, 171], [385, 222], [291, 235], [291, 340], [229, 267], [89, 358], [151, 251], [57, 167], [167, 164]];
vpos = [100,100]; # offset
# get original image res
height, width = blank.shape[:2];
hh = int(height / 2);
hw = int(width / 2);
# center of view
cov = [600, 400];
camera_spd = 5;
# scale
scale = 1;
scale_step = 0.2;
# loop
done = False;
while not done:
# crop and show image
display = zoomView(blank, cov, scale, hh, hw);
# drawVec(display, vshape, vpos, cov, hh, hw, scale);
drawVec(display, bshape, bpos, cov, hh, hw, scale);
# draw a dot in the middle
cv2.circle(display, (hw, hh), 4, (0,0,255), -1);
# draw center lines
cv2.line(display, (hw,0), (hw,height), (0,0,255), 1);
cv2.line(display, (0,hh), (width,hh), (0,0,255), 1);
# draw zoom text
cv2.putText(display, "Zoom: " + str(scale), (15,40), font,
fontScale, fontColor, thickness, cv2.LINE_AA);
# show
cv2.imshow("Display", display);
key = cv2.waitKey(1);
# check keys
done = key == ord('q');
# Note: if you're actually gonna make a GUI
# use the keyboard module or something else for this
# wasd to move center-of-view
if key == ord('d'):
cov[0] += camera_spd;
if key == ord('a'):
cov[0] -= camera_spd;
if key == ord('w'):
cov[1] -= camera_spd;
if key == ord('s'):
cov[1] += camera_spd;
# z,x to decrease/increase zoom (lower bound is 1.0)
if key == ord('x'):
scale += scale_step;
if key == ord('z'):
scale -= scale_step;
scale = round(scale, 2);
# bound cov
boundCenter(cov, scale, hh, hw);
Edit: Explanation of the drawVec parameters
img: The OpenCV image to be drawn on
vec: A list of [x,y] points
pos: The offset to draw those points at
cov: Center-Of-View, where the middle of our zoomed display is pointed at
hh: Half-Height, the height of "img" divided by 2
hw: Half-Width, the width of "img" divided by 2
I have looked through my code and realized where I was making a mistake which caused points to be offset.
In my program, I have a canvas of specific size. The size of canvas is a constant and is always larger than images being drawn on canvas. When program draws an image on canvas it first resizes that image so it could fit on canvas. The size of resized image is somewhat smaller than size of canvas. Image is usually drawn starting from top left corner of canvas. Since I wanted to always draw image in the center of canvas, I shifted the location from top left corner of canvas to another point. This is what I didn't account when doing image zooming.
def zoom(image, ratio, points, canvas_off_x, canvas_off_y):
width, height = image.shape[:2]
new_width, new_height = int(ratio * width), int(ratio * height)
center_x, center_y = int(new_width / 2), int(new_height / 2)
radius_x, radius_y = int(width / 2), int(height / 2)
min_x, max_x = center_x - radius_x, center_x + radius_x
min_y, max_y = center_y - radius_y, center_y + radius_y
img_resized = cv2.resize(image, (new_width,new_height), interpolation=cv2.INTER_LINEAR)
img_cropped = img_resized[min_y:max_y+1, min_x:max_x+1]
for point in points:
x, y = point.get_original_coordinates()
x -= canvas_off_x
y -= canvas_off_y
x = int((x * ratio) - min_x + canvas_off_x)
y = int((y * ratio) - min_y + canvas_off_y)
point.set_scaled_coordinates(x, y)
In the code below canvas_off_x and canvas_off_y is the location of offset from top left corner of canvas
I have bunch of images of gear and they all are in different orientation and I need them all in same orientation. I mean there is one reference image and rest of the images should be rotated so they look like same as reference image. I followed these steps, first segment the gear and then tried to find an angle using moments but its not working correctly. I've attached the 3 images considering the first image as reference image and here's the code so far
def adjust_gamma(image, gamma=1.0):
invGamma = 1.0 / gamma
table = np.array([((i / 255.0) ** invGamma) * 255
for i in np.arange(0, 256)]).astype("uint8")
return cv2.LUT(image, table)
def unsharp_mask(image, kernel_size=(13, 13), sigma=1.0, amount=2.5, threshold=10):
"""Return a sharpened version of the image, using an unsharp mask."""
blurred = cv2.GaussianBlur(image, kernel_size, sigma)
sharpened = float(amount + 1) * image - float(amount) * blurred
sharpened = np.maximum(sharpened, np.zeros(sharpened.shape))
sharpened = np.minimum(sharpened, 255 * np.ones(sharpened.shape))
sharpened = sharpened.round().astype(np.uint8)
if threshold > 0:
low_contrast_mask = np.absolute(image - blurred) < threshold
np.copyto(sharpened, image, where=low_contrast_mask)
return sharpened
def find_orientation(cont):
m = cv2.moments(cont, True)
cen_x = m['m10'] / m['m00']
cen_y = m['m01'] / m['m00']
m_11 = 2*m['m11'] - m['m00'] * (cen_x*cen_x+cen_y*cen_y)
m_02 = m['m02'] - m['m00'] * cen_y*cen_y
m_20 = m['m20'] - m['m00'] * cen_x*cen_x
theta = 0 if m_20==m_02 else atan2(m_11, m_20-m_02)/2.0
theta = theta * 180 / pi
return (cen_x, cen_y, theta)
def rotate_image(img, angles):
height, width = img.shape[:2]
rotation_matrix = cv2.getRotationMatrix2D((width/2, height/2), angles, 1)
rotated_image = cv2.warpAffine(img, rotation_matrix, (width,height))
return rotated_image
img = cv2.imread('gear1.jpg')
resized_img = imutils.resize(img, width=540)
height, width = resized_img.shape[:2]
gamma_adjusted = adjust_gamma(resized_img, 2.5)
sharp = unsharp_mask(gamma_adjusted)
gray = cv2.cvtColor(sharp, cv2.COLOR_BGR2GRAY)
gauss_blur = cv2.GaussianBlur(gray, (13,13), 2.5)
ret, thresh = cv2.threshold(gauss_blur, 250, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3,3))
thresh = cv2.morphologyEx(thresh, cv2.MORPH_DILATE, kernel, iterations=2)
kernel = np.ones((3,3), np.uint8)
thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=2)
contours = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)[0]
cen_x, cen_y, theta = find_orientation(contours[0])
reference_angle = -24.14141919602858
rot_angle = 0.0
if theta < reference_angle:
rot_angle = -(theta - reference_angle)
else:
rot_angle = (reference_angle - theta)
rot_img = rotate_image(resized_img, rot_angle)
Can anyone tell me where did i go wrong? Any help would be appreciated.
Binarization of the gear and the holes seems easy. You should be able to discriminate the holes from noise and extra small features.
First find the geometric center, and sort the holes by angle around the center. Also compute the areas of the holes. Then you can try to match the holes to the model in a cyclic way. There are 20 holes, and you just need to test 20 positions. You can rate a matching by some combination of the differences in the angles and the areas. The best match tells you the orientation.
This should be very reliable.
You can obtain a very accurate value of the angle by computing the average error per hole and correcting to cancel that value (this is equivalent to least-squares fitting).
I have an image with image.shape=(20,10)and I want to resize this image so that new image size would be image.size = 90.
I want to use np.resize(image,(new_width, new_height)), but how can I calculate new_width and new_height, so that it maintains aspect_ratio as same as in original image.
Well, you choose which dimension you want to enforce and then you adjust the other one by calculating either new_width = new_height*aspect_ratio or new_height = new_width/aspect_ratio.
You might want to round those numbers and convert them to int too.
The height of your image is 20 and the width is 10, so the height is 2x the width, i.e.
h = 2 * w
You want your new image to have an area of 90 pixels, and the area (A) is:
A = h * w
90 = 2 * w * w
w = sqrt(45)
So the sides of your image need to be 6.7 and 13.4
I hope that helps, even if I doubt it will.
You can use this simple function for finding the new height of an image with width as an input
def findHeight(original_width, original_height, new_width):
area = original_width * original_height
new_height = area/new_width
return new_height
Okay, I've been at this all day and haven't a clue. I need to get my turtle object to draw random lines outside of a circle.
I've made code that restricts the random lines within the boundaries before, so I thought all I had to do was change the sign, but that didn't work. I'm not allowed to use coordinate geometry - it has to be something more basic...
Here's my code in it's current format:
import turtle, random
mRoshi = turtle.Turtle()
def draw_any_shape(myTurtle, sideLength, numSides):
turnAng = 360/numSides
for i in range(numSides):
myTurtle.forward(sideLength)
myTurtle.right(turnAng)
def drawCircle(myTurtle, radius, startX, startY):
circumference = 2*3.1415*radius
sideLength = circumference/360
myTurtle.penup()
myTurtle.goto(startX, startY)
#myTurtle.dot()
myTurtle.goto(startX, startY+radius)
myTurtle.pendown()
draw_any_shape(myTurtle, sideLength, 360)
def stumblingTurtle(myTurtle, radius, startX, startY, paramN5):
circumference = 2*3.1415*radius
myTurtle.speed(6)
drawCircle(myTurtle, radius, startX, startY)
myTurtle.penup()
for i in range(paramN5):
drx = random.randint(-800, 800)
drw = random.randint(-800, 800)
if (drx**2 + drw**2) > radius**2:
myTurtle.goto(drx,drw)
crx = random.randint(-800, 800)
crw = random.randint(-800, 800)
xdif = crx-drx
ydif = crw-drw
for j in range(drx, crx):
for k in range(drw, crw):
if (xdif**2 + ydif**2) > radius**2:
myTurtle.goto(crx,crw)
Does this do what you want? It's also based on code that originally kept the turtle within a circle. It uses Python3 turtle's undo capability to allow the turtle to accidentally wander into the circle and then undo that accident as if it never happened:
import turtle
import random
RADIUS = 50
MAXIMUM_TURN = 45
STEP_SIZE = 10
BORDER = 20
def bounded_random_move():
yertle.forward(STEP_SIZE)
x, y = yertle.position()
if (x * x + y * y) < RADIUS * RADIUS or x < -window_width/2 or x > window_width/2 or y < -window_height/2 or y > window_height/2:
yertle.undo() # undo misstep
turn = random.randint(180 - MAXIMUM_TURN, 180 + MAXIMUM_TURN)
yertle.left(turn)
turtle.ontimer(bounded_random_move, 100)
turtle.setup(RADIUS * 10, RADIUS * 10)
window_width = turtle.window_width() - BORDER
window_height = turtle.window_height() - BORDER
magic_marker = turtle.Turtle(visible=False)
magic_marker.penup()
magic_marker.color("red")
magic_marker.sety(-RADIUS)
magic_marker.pendown()
magic_marker.circle(RADIUS)
yertle = turtle.Turtle(shape="turtle", visible=False)
yertle.speed("fastest")
yertle.penup()
yertle.goto(RADIUS * 2, RADIUS * 2) # start outside circle
yertle.pendown()
yertle.showturtle()
turtle.ontimer(bounded_random_move, 100)
turtle.exitonclick()
My undo trick might not be rigorous enough for everyone, however.