Is there a Contour Method to detect arrows in Python CV? Maybe with Contours, Shapes, and Vertices.
# find contours in the thresholded image and initialize the shape detector
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
perimeterValue = cv2.arcLength(cnts , True)
vertices = cv2.approxPolyDP(cnts , 0.04 * perimeterValue, True)
Perhaps we can look at tips of the contours, and also detect triangles?
Hopefully it can detect arrows among different objects, among squares, rectangles, and circles. (otherwise, will have to use machine learning).
Also nice to get these three results if possible (arrow length, thickness, directionAngle)
This question recommends template matching, and doesn't specify any code base. Looking for something workable that can be code created
how to detect arrows using open cv python?
If PythonOpenCV doesn't have capability, open to utilizing another library.
The solution you are asking for is too complex to be solved by one function or particular algorithm. In fact, the problem could be broken down into smaller steps, each with their own algorithms and solutions. Instead of offering you a free, complete, copy-paste solution, I'll give you a general outline of the problem and post part of the solution I'd design. These are the steps I propose:
Identify and extract all the arrow blobs from the image, and process them one by one.
Try to find the end-points of the arrow. That is end and starting point (or "tail" and "tip")
Undo the rotation, so you have straightened arrows always, no matter their angle.
After this, the arrows will always point to one direction. This normalization let's itself easily for classification.
After processing, you can pass the image to a Knn classifier, a Support Vector Machine or even (if you are willing to call the "big guns" on this problem) a CNN (in which case, you probably won't need to undo the rotation - as long as you have enough training samples). You don't even have to compute features, as passing the raw image to a SVM would be probably enough. However, you need more than one training sample for each arrow class.
Alright, let's see. First, let's extract each arrow from the input. This is done using cv2.findCountours, this part is very straightforward:
# Imports:
import cv2
import math
import numpy as np
# image path
path = "D://opencvImages//"
fileName = "arrows.png"
# Reading an image in default mode:
inputImage = cv2.imread(path + fileName)
# Grayscale conversion:
grayscaleImage = cv2.cvtColor(inputImage, cv2.COLOR_BGR2GRAY)
grayscaleImage = 255 - grayscaleImage
# Find the big contours/blobs on the binary image:
contours, hierarchy = cv2.findContours(grayscaleImage, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
Now, let's check out the contours and process them one by one. Let's compute a (non-rotated) bounding box of the arrow and crop that sub-image. Now, note that some noise could come up. In which case, we won't be processing that blob. I apply an area filter to bypass blobs of small area. Like this:
# Process each contour 1-1:
for i, c in enumerate(contours):
# Approximate the contour to a polygon:
contoursPoly = cv2.approxPolyDP(c, 3, True)
# Convert the polygon to a bounding rectangle:
boundRect = cv2.boundingRect(contoursPoly)
# Get the bounding rect's data:
rectX = boundRect[0]
rectY = boundRect[1]
rectWidth = boundRect[2]
rectHeight = boundRect[3]
# Get the rect's area:
rectArea = rectWidth * rectHeight
minBlobArea = 100
We set a minBlobArea and process that contour. Crop the image if the contour is above that area threshold value:
# Check if blob is above min area:
if rectArea > minBlobArea:
# Crop the roi:
croppedImg = grayscaleImage[rectY:rectY + rectHeight, rectX:rectX + rectWidth]
# Extend the borders for the skeleton:
borderSize = 5
croppedImg = cv2.copyMakeBorder(croppedImg, borderSize, borderSize, borderSize, borderSize, cv2.BORDER_CONSTANT)
# Store a deep copy of the crop for results:
grayscaleImageCopy = cv2.cvtColor(croppedImg, cv2.COLOR_GRAY2BGR)
# Compute the skeleton:
skeleton = cv2.ximgproc.thinning(croppedImg, None, 1)
There are some couple of things going on here. After I crop the ROI of the current arrow, I extend borders on that image. I store a deep-copy of this image for further processing and, lastly, I compute the skeleton. The border-extending is done prior to skeletonizing because the algorithm produces artifacts if the contour is too close to the image limits. Padding the image in all directions prevents these artifacts. The skeleton is needed for the way I'm finding ending and starting points of the arrow. More of this latter, this is the first arrow cropped and padded:
This is the skeleton:
Note that the "thickness" of the contour is normalized to 1 pixel. That's cool, because that's what I need for the following processing step: Finding start/ending points. This is done by applying a convolution with a kernel designed to identify one-pixel wide end-points on a binary image. Refer to this post for the specifics. We will prepare the kernel and use cv2.filter2d to get the convolution:
# Threshold the image so that white pixels get a value of 0 and
# black pixels a value of 10:
_, binaryImage = cv2.threshold(skeleton, 128, 10, cv2.THRESH_BINARY)
# Set the end-points kernel:
h = np.array([[1, 1, 1],
[1, 10, 1],
[1, 1, 1]])
# Convolve the image with the kernel:
imgFiltered = cv2.filter2D(binaryImage, -1, h)
# Extract only the end-points pixels, those with
# an intensity value of 110:
binaryImage = np.where(imgFiltered == 110, 255, 0)
# The above operation converted the image to 32-bit float,
# convert back to 8-bit uint
binaryImage = binaryImage.astype(np.uint8)
After the convolution, all end-points have a value of 110. Setting these pixels to 255, while the rest are set to black, yields the following image (after proper conversion):
Those tiny pixels correspond to the "tail" and "tip" of the arrow. Notice there's more than one point per "Arrow section". This is because the end-points of the arrow do not perfectly end in one pixel. In the case of the tip, for example, there will be more end-points than in the tail. This is a characteristic we will exploit latter. Now, pay attention to this. There are multiple end-points but we only need an starting point and an ending point. I'm gonna use K-Means to group the points in two clusters.
Using K-means will also let me identify which end-points belong to the tail and which to the tip, so I'll always know the direction of the arrow. Let's roll:
# Find the X, Y location of all the end-points
# pixels:
Y, X = binaryImage.nonzero()
# Check if I got points on my arrays:
if len(X) > 0 or len(Y) > 0:
# Reshape the arrays for K-means
Y = Y.reshape(-1,1)
X = X.reshape(-1,1)
Z = np.hstack((X, Y))
# K-means operates on 32-bit float data:
floatPoints = np.float32(Z)
# Set the convergence criteria and call K-means:
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
_, label, center = cv2.kmeans(floatPoints, 2, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)
Be careful with the data types. If I print the label and center matrices, I get this (for the first arrow):
Center:
[[ 6. 102. ]
[104. 20.5]]
Labels:
[[1]
[1]
[0]]
center tells me the center (x,y) of each cluster – That is the two points I was originally looking for. label tells me on which cluster the original data falls in. As you see, there were originally 3 points. 2 of those points (the points belonging to the tip of the arrow) area assigned to cluster 1, while the remaining end-point (the arrow tail) is assigned to cluster 0. In the centers matrix the centers are ordered by cluster number. That is – first center is that one of cluster 0, while second cluster is the center of cluster 1. Using this info I can easily look for the cluster that groups the majority of points - that will be the tip of the arrow, while the remaining will be the tail:
# Set the cluster count, find the points belonging
# to cluster 0 and cluster 1:
cluster1Count = np.count_nonzero(label)
cluster0Count = np.shape(label)[0] - cluster1Count
# Look for the cluster of max number of points
# That cluster will be the tip of the arrow:
maxCluster = 0
if cluster1Count > cluster0Count:
maxCluster = 1
# Check out the centers of each cluster:
matRows, matCols = center.shape
# We need at least 2 points for this operation:
if matCols >= 2:
# Store the ordered end-points here:
orderedPoints = [None] * 2
# Let's identify and draw the two end-points
# of the arrow:
for b in range(matRows):
# Get cluster center:
pointX = int(center[b][0])
pointY = int(center[b][1])
# Get the "tip"
if b == maxCluster:
color = (0, 0, 255)
orderedPoints[0] = (pointX, pointY)
# Get the "tail"
else:
color = (255, 0, 0)
orderedPoints[1] = (pointX, pointY)
# Draw it:
cv2.circle(grayscaleImageCopy, (pointX, pointY), 3, color, -1)
cv2.imshow("End Points", grayscaleImageCopy)
cv2.waitKey(0)
This is the result; the tip of the end-point of the arrow will always be in red and the end-point for the tail in blue:
Now, we know the direction of the arrow, let's compute the angle. I will measure this angle from 0 to 360. The angle will always be the one between the horizon line and the tip. So, we manually compute the angle:
# Store the tip and tail points:
p0x = orderedPoints[1][0]
p0y = orderedPoints[1][1]
p1x = orderedPoints[0][0]
p1y = orderedPoints[0][1]
# Compute the sides of the triangle:
adjacentSide = p1x - p0x
oppositeSide = p0y - p1y
# Compute the angle alpha:
alpha = math.degrees(math.atan(oppositeSide / adjacentSide))
# Adjust angle to be in [0,360]:
if adjacentSide < 0 < oppositeSide:
alpha = 180 + alpha
else:
if adjacentSide < 0 and oppositeSide < 0:
alpha = 270 + alpha
else:
if adjacentSide > 0 > oppositeSide:
alpha = 360 + alpha
Now you have the angle, and this angle is always measured between the same references. That's cool, we can undo the rotation of the original image like follows:
# Deep copy for rotation (if needed):
rotatedImg = croppedImg.copy()
# Undo rotation while padding output image:
rotatedImg = rotateBound(rotatedImg, alpha)
cv2. imshow("rotatedImg", rotatedImg)
cv2.waitKey(0)
else:
print( "K-Means did not return enough points, skipping..." )
else:
print( "Did not find enough end points on image, skipping..." )
This yields the following result:
The arrow will always point top the right regardless of its original angle. Use this as normalization for a batch of training images, if you want to classify each arrow in its own class.
Now, you noticed that I used a function to rotate the image: rotateBound. This function is taken from here. This functions correctly pads the image after rotation, so you do not end up with a rotated image that is cropped incorrectly.
This is the definition and implementation of rotateBound:
def rotateBound(image, angle):
# grab the dimensions of the image and then determine the
# center
(h, w) = image.shape[:2]
(cX, cY) = (w // 2, h // 2)
# grab the rotation matrix (applying the negative of the
# angle to rotate clockwise), then grab the sine and cosine
# (i.e., the rotation components of the matrix)
M = cv2.getRotationMatrix2D((cX, cY), -angle, 1.0)
cos = np.abs(M[0, 0])
sin = np.abs(M[0, 1])
# compute the new bounding dimensions of the image
nW = int((h * sin) + (w * cos))
nH = int((h * cos) + (w * sin))
# adjust the rotation matrix to take into account translation
M[0, 2] += (nW / 2) - cX
M[1, 2] += (nH / 2) - cY
# perform the actual rotation and return the image
return cv2.warpAffine(image, M, (nW, nH))
These are results for the rest of your arrows. The tip (always in red), the tail (always in blue) and their "projective normalization" - always pointing to the right:
What remains is collect samples of your different arrow classes, set up a classifier, train it with your samples and test it with the straightened image coming from the last processing block we examined.
Some remarks: Some arrows, like the one that is not filled, failed the end-point identification part, thus, not yielding enough points for clustering. That arrow is by-passed by the algorithm. The problem is tougher than initially though, right? I recommend doing some research on the topic, because not matter how "easy" the task seems, at the end, it will be performed by an automated "smart" system. And those systems aren't really that smart at the end of the day.
Here is the workflow I put together that would make this work:
Import the necessary libraries:
import cv2
import numpy as np
Define a function that will take in an image, and process it into something that can allow python to more easily find the necessary contours of each shape. The values can be adjusted to better suit your needs:
def preprocess(img):
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_blur = cv2.GaussianBlur(img_gray, (5, 5), 1)
img_canny = cv2.Canny(img_blur, 50, 50)
kernel = np.ones((3, 3))
img_dilate = cv2.dilate(img_canny, kernel, iterations=2)
img_erode = cv2.erode(img_dilate, kernel, iterations=1)
return img_erode
Define a function that will take in two lists; an approximate contour of a shape, points, and the indices of the convex hull of that contour, convex_hull. For the below function, you must make sure that the length of the points list is exactly 2 units greater than the length of the convex_hull list before calling the function. The reasoning is that optimally, the arrow should have exactly 2 more points that aren't present in the convex hull of the arrow.
def find_tip(points, convex_hull):
In the find_tip function, define a list of the indices of the points array where the values are not present in the convex_hull array:
length = len(points)
indices = np.setdiff1d(range(length), convex_hull)
In order to find the tip of the arrow, given we have the approximate outline of the arrow as points and the indices of the two points that are concave to the arrow, indices, we can find the tip by either subtracting 2 from the first index in the indices list, or by adding 2 to the first index of the indices list. See the below examples for reference:
In order to know whether you should subtract 2 from the first element of the indices list, or add 2, you'll need to do the exact opposite to the second (which is the last) element of the indices list; if the resulting two indices returns the same value from the points list, then you found the tip of the arrow. I used a for loop that loops through numbers 0 and 1. The first iteration will add 2 to the second element of the indices list: j = indices[i] + 2, and subtract 2 from the first element of the indices list: indices[i - 1] - 2:
for i in range(2):
j = indices[i] + 2
if j > length - 1:
j = length - j
if np.all(points[j] == points[indices[i - 1] - 2]):
return tuple(points[j])
This part:
if j > length - 1:
j = length - j
is there for cases like this:
where if you try adding 2 to the index 5, you will get an IndexError. So if, say j becomes 7 from the j = indices[i] + 2, the above condition will convert j to len(points) - j.
Read the image and get its contours, utilizing the preprocess function defined earlier before passing it into the cv2.findContours method:
img = cv2.imread("arrows.png")
contours, hierarchy = cv2.findContours(preprocess(img), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
Loop through the contours, and find the approximate contour and convex hull of each shape:
for cnt in contours:
peri = cv2.arcLength(cnt, True)
approx = cv2.approxPolyDP(cnt, 0.025 * peri, True)
hull = cv2.convexHull(approx, returnPoints=False)
sides = len(hull)
If the number of sides of the convex hull is 4 or 5 (the extra side in case the arrow has a flat bottom), and if the shape of the arrow has exactly two more points that are not present in the convex hull, find the tip of the arrow:
if 6 > sides > 3 and sides + 2 == len(approx):
arrow_tip = find_tip(approx[:,0,:], hull.squeeze())
If there is indeed a tip, then congratulation! You found a decent arrow! Now the arrow can be highlighted, and a circle can be drawn at the location of the tip of the arrow:
if arrow_tip:
cv2.drawContours(img, [cnt], -1, (0, 255, 0), 3)
cv2.circle(img, arrow_tip, 3, (0, 0, 255), cv2.FILLED)
Finally, show the image:
cv2.imshow("Image", img)
cv2.waitKey(0)
Altogether:
import cv2
import numpy as np
def preprocess(img):
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_blur = cv2.GaussianBlur(img_gray, (5, 5), 1)
img_canny = cv2.Canny(img_blur, 50, 50)
kernel = np.ones((3, 3))
img_dilate = cv2.dilate(img_canny, kernel, iterations=2)
img_erode = cv2.erode(img_dilate, kernel, iterations=1)
return img_erode
def find_tip(points, convex_hull):
length = len(points)
indices = np.setdiff1d(range(length), convex_hull)
for i in range(2):
j = indices[i] + 2
if j > length - 1:
j = length - j
if np.all(points[j] == points[indices[i - 1] - 2]):
return tuple(points[j])
img = cv2.imread("arrows.png")
contours, hierarchy = cv2.findContours(preprocess(img), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
for cnt in contours:
peri = cv2.arcLength(cnt, True)
approx = cv2.approxPolyDP(cnt, 0.025 * peri, True)
hull = cv2.convexHull(approx, returnPoints=False)
sides = len(hull)
if 6 > sides > 3 and sides + 2 == len(approx):
arrow_tip = find_tip(approx[:,0,:], hull.squeeze())
if arrow_tip:
cv2.drawContours(img, [cnt], -1, (0, 255, 0), 3)
cv2.circle(img, arrow_tip, 3, (0, 0, 255), cv2.FILLED)
cv2.imshow("Image", img)
cv2.waitKey(0)
Original image:
Python program output:
Here is an approach with cv2.connectedComponentsWithStats. After extracting every arrow individually, I am getting the farthest points on the arrow. The distance between these points give me (more or less) the length of the arrow. Also, I am calculating the angle of the arrow by using these two points, i.e., slope between two points. Finally, in order to find the thickness, I am drawing a straight line between these points. And, I am calculating the shortest distance of each pixel of the arrow to the line. The most repeated distance value should give me the thickness of arrow.
The algorithm is not perfect, as it is. Especially, if the arrow is tilted. But, I feel like it is a good starting point and you can improve it.
import cv2
import numpy as np
import matplotlib.pyplot as plt
from scipy.spatial import distance
import math
img = cv2.imread('arrows.png',0)
_,img = cv2.threshold(img,10,255,cv2.THRESH_BINARY_INV)
labels, stats = cv2.connectedComponentsWithStats(img, 8)[1:3]
for label in np.unique(labels)[1:]:
arrow = labels==label
indices = np.transpose(np.nonzero(arrow)) #y,x
dist = distance.cdist(indices, indices, 'euclidean')
far_points_index = np.unravel_index(np.argmax(dist), dist.shape) #y,x
far_point_1 = indices[far_points_index[0],:] # y,x
far_point_2 = indices[far_points_index[1],:] # y,x
### Slope
arrow_slope = (far_point_2[0]-far_point_1[0])/(far_point_2[1]-far_point_1[1])
arrow_angle = math.degrees(math.atan(arrow_slope))
### Length
arrow_length = distance.cdist(far_point_1.reshape(1,2), far_point_2.reshape(1,2), 'euclidean')[0][0]
### Thickness
x = np.linspace(far_point_1[1], far_point_2[1], 20)
y = np.linspace(far_point_1[0], far_point_2[0], 20)
line = np.array([[yy,xx] for yy,xx in zip(y,x)])
thickness_dist = np.amin(distance.cdist(line, indices, 'euclidean'),axis=0).flatten()
n, bins, patches = plt.hist(thickness_dist,bins=150)
thickness = 2*bins[np.argmax(n)]
print(f"Thickness: {thickness}")
print(f"Angle: {arrow_angle}")
print(f"Length: {arrow_length}\n")
plt.figure()
plt.imshow(arrow,cmap='gray')
plt.scatter(far_point_1[1],far_point_1[0],c='r',s=10)
plt.scatter(far_point_2[1],far_point_2[0],c='r',s=10)
plt.scatter(line[:,1],line[:,0],c='b',s=10)
plt.show()
Thickness: 4.309328382835436
Angle: 58.94059117029002
Length: 102.7277956543408
Thickness: 7.851144897915465
Angle: -3.366460663429801
Length: 187.32325002519042
Thickness: 2.246710258748367
Angle: 55.51004336926862
Length: 158.93709447451215
Thickness: 25.060450615293227
Angle: -37.184706453233126
Length: 145.60219778561037
As your main concern is to filter out arrows from different shapes. I have implemented a method using convexityDefects. you can read more about convexity defects here.
Also, I have added more arrow inside other shapes to demonstrate the robustness of the method.
Updated Image
Method to filter arrows from image using convexity defects.
def get_filter_arrow_image(threslold_image):
blank_image = np.zeros_like(threslold_image)
# dilate image to remove self-intersections error
kernel_dilate = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2))
threslold_image = cv2.dilate(threslold_image, kernel_dilate, iterations=1)
contours, hierarchy = cv2.findContours(threslold_image, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
if hierarchy is not None:
threshold_distnace = 1000
for cnt in contours:
hull = cv2.convexHull(cnt, returnPoints=False)
defects = cv2.convexityDefects(cnt, hull)
if defects is not None:
for i in range(defects.shape[0]):
start_index, end_index, farthest_index, distance = defects[i, 0]
# you can add more filteration based on this start, end and far point
# start = tuple(cnt[start_index][0])
# end = tuple(cnt[end_index][0])
# far = tuple(cnt[farthest_index][0])
if distance > threshold_distnace:
cv2.drawContours(blank_image, [cnt], -1, 255, -1)
return blank_image
else:
return None
filter arrow image
I have added methods for the angle and length of the arrow, If this isn't good enough, let me know; there are more complicated methods for angle detection based on 3 coordinate points.
def get_max_distace_point(cnt):
max_distance = 0
max_points = None
for [[x1, y1]] in cnt:
for [[x2, y2]] in cnt:
distance = get_length((x1, y1), (x2, y2))
if distance > max_distance:
max_distance = distance
max_points = [(x1, y1), (x2, y2)]
return max_points
def angle_beween_points(a, b):
arrow_slope = (a[0] - b[0]) / (a[1] - b[1])
arrow_angle = math.degrees(math.atan(arrow_slope))
return arrow_angle
def get_arrow_info(arrow_image):
arrow_info_image = cv2.cvtColor(arrow_image.copy(), cv2.COLOR_GRAY2BGR)
contours, hierarchy = cv2.findContours(arrow_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
arrow_info = []
if hierarchy is not None:
for cnt in contours:
# draw single arrow on blank image
blank_image = np.zeros_like(arrow_image)
cv2.drawContours(blank_image, [cnt], -1, 255, -1)
point1, point2 = get_max_distace_point(cnt)
angle = angle_beween_points(point1, point2)
lenght = get_length(point1, point2)
cv2.line(arrow_info_image, point1, point2, (0, 255, 255), 1)
cv2.circle(arrow_info_image, point1, 2, (255, 0, 0), 3)
cv2.circle(arrow_info_image, point2, 2, (255, 0, 0), 3)
cv2.putText(arrow_info_image, "angle : {0:0.2f}".format(angle),
point2, cv2.FONT_HERSHEY_PLAIN, 0.8, (0, 0, 255), 1)
cv2.putText(arrow_info_image, "lenght : {0:0.2f}".format(lenght),
(point2[0], point2[1]+20), cv2.FONT_HERSHEY_PLAIN, 0.8, (0, 0, 255), 1)
return arrow_info_image, arrow_info
else:
return None, None
angle and length image
CODE
import math
import cv2
import numpy as np
def get_filter_arrow_image(threslold_image):
blank_image = np.zeros_like(threslold_image)
# dilate image to remove self-intersections error
kernel_dilate = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2))
threslold_image = cv2.dilate(threslold_image, kernel_dilate, iterations=1)
contours, hierarchy = cv2.findContours(threslold_image, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
if hierarchy is not None:
threshold_distnace = 1000
for cnt in contours:
hull = cv2.convexHull(cnt, returnPoints=False)
defects = cv2.convexityDefects(cnt, hull)
if defects is not None:
for i in range(defects.shape[0]):
start_index, end_index, farthest_index, distance = defects[i, 0]
# you can add more filteration based on this start, end and far point
# start = tuple(cnt[start_index][0])
# end = tuple(cnt[end_index][0])
# far = tuple(cnt[farthest_index][0])
if distance > threshold_distnace:
cv2.drawContours(blank_image, [cnt], -1, 255, -1)
return blank_image
else:
return None
def get_length(p1, p2):
line_length = ((p1[0] - p2[0]) ** 2 + (p1[1] - p2[1]) ** 2) ** 0.5
return line_length
def get_max_distace_point(cnt):
max_distance = 0
max_points = None
for [[x1, y1]] in cnt:
for [[x2, y2]] in cnt:
distance = get_length((x1, y1), (x2, y2))
if distance > max_distance:
max_distance = distance
max_points = [(x1, y1), (x2, y2)]
return max_points
def angle_beween_points(a, b):
arrow_slope = (a[0] - b[0]) / (a[1] - b[1])
arrow_angle = math.degrees(math.atan(arrow_slope))
return arrow_angle
def get_arrow_info(arrow_image):
arrow_info_image = cv2.cvtColor(arrow_image.copy(), cv2.COLOR_GRAY2BGR)
contours, hierarchy = cv2.findContours(arrow_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
arrow_info = []
if hierarchy is not None:
for cnt in contours:
# draw single arrow on blank image
blank_image = np.zeros_like(arrow_image)
cv2.drawContours(blank_image, [cnt], -1, 255, -1)
point1, point2 = get_max_distace_point(cnt)
angle = angle_beween_points(point1, point2)
lenght = get_length(point1, point2)
cv2.line(arrow_info_image, point1, point2, (0, 255, 255), 1)
cv2.circle(arrow_info_image, point1, 2, (255, 0, 0), 3)
cv2.circle(arrow_info_image, point2, 2, (255, 0, 0), 3)
cv2.putText(arrow_info_image, "angle : {0:0.2f}".format(angle),
point2, cv2.FONT_HERSHEY_PLAIN, 0.8, (0, 0, 255), 1)
cv2.putText(arrow_info_image, "lenght : {0:0.2f}".format(lenght),
(point2[0], point2[1] + 20), cv2.FONT_HERSHEY_PLAIN, 0.8, (0, 0, 255), 1)
return arrow_info_image, arrow_info
else:
return None, None
if __name__ == "__main__":
image = cv2.imread("image2.png")
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
_, thresh_image = cv2.threshold(gray_image, 100, 255, cv2.THRESH_BINARY_INV)
cv2.imshow("thresh_image", thresh_image)
arrow_image = get_filter_arrow_image(thresh_image)
if arrow_image is not None:
cv2.imshow("arrow_image", arrow_image)
cv2.imwrite("arrow_image.png", arrow_image)
arrow_info_image, arrow_info = get_arrow_info(arrow_image)
cv2.imshow("arrow_info_image", arrow_info_image)
cv2.imwrite("arrow_info_image.png", arrow_info_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
convexity defects on a thin arrow.
Blue point - start point of defect
Green point - far point if defect
Red point - end point of defect
yellow line = defect line from start point to end point.
image
defect-1
defect-2
and so on...
..
I have 1000 images like that
I tried the cv2 library and Hough Line Transform by this tutorial, but I'm don't understand it is my case? I have 1000 images, i.e. I almost don't have the possibility to enter any data (like width or coordinates) manually.
By the logic, I must find every blue pixel in the image and check, if the neighbors' pixels are white
So for it I must know pixels format of a PNG image. How I must to read the image, like common file open (path, 'r') as file_object or it must be some special method with a library?
You could count the line ends and divide by two...
#!/usr/bin/env python3
import numpy as np
from PIL import Image
from scipy.ndimage import generic_filter
# Line ends filter
def lineEnds(P):
global ends
# Central pixel and one other must be 255 for line end
if (P[4]==255) and np.sum(P)==510:
ends += 1
return 255
return 0
# Global count of line ends
ends = 0
# Open image and make into Numpy array
im = Image.open('lines.png').convert('L')
im = np.array(im)
# Invert and threshold for white lines on black
im = 255 - im
im[im>0] = 255
# Save result, just for debug
Image.fromarray(im).save('intermediate.png')
# Find line ends
result = generic_filter(im, lineEnds, (3, 3))
print(f'Line ends: {ends}')
# Save result, just for debug
Image.fromarray(result).save('result.png')
Output
Line ends: 16
Note this is not production quality code. You should add extra checks, such as the total number of line-ends being even, and adding a 1 pixel wide black border around the edge in case a line touches the edge and so on.
At first glance the problem looks simple - convert to binary image, use Hough Line Transform, and count the lines, but it's not working...
Note:
The solution I found is based on finding and merging contours, but using Hough Transform may be more robust.
Instead of merging contours, you may find many short lines, and merge them into long lines based on close angle and edges proximity.
The solution below uses the following stages:
Convert image to binary image with white lines on black background.
Split intersection points between lines (fill crossing points with black).
Find contours in binary image (and remove small contours).
Merge contours with close angles, and close edges.
Here is a working code sample:
import cv2
import numpy as np
def box2line(box):
"""Convert rotated rectangle box into two array of two points that defines a line"""
b = box.copy()
for i in range(2):
p0 = b[0]
dif0 = (b[1:, 0] - p0[0])**2 + (b[1:, 1] - p0[1])**2
min_idx = np.argmin(dif0, 0)
b = np.delete(b, min_idx+1, 0)
return b
def minlinesdist(line, line2):
"""Finds minimum distance between any two edges of two lines"""
a0 = line[0, :]
a1 = line[1, :]
b0 = line2[0, :]
b1 = line2[1, :]
d00 = np.linalg.norm(a0 - b0)
d01 = np.linalg.norm(a0 - b1)
d10 = np.linalg.norm(a1 - b0)
d11 = np.linalg.norm(a1 - b1)
min_dist = np.min((d00, d01, d10, d11))
return min_dist
def get_rect_box_line_and_angle(c):
"""Return minAreaRect, boxPoints, line and angle of contour"""
rect = cv2.minAreaRect(c)
box = cv2.boxPoints(rect)
line = box2line(box)
angle = rect[2]
return rect, box, line, angle
(cv_major_ver, cv_minor_ver, cv_subminor_ver) = (cv2.__version__).split('.') # Get version of OpenCV
im = cv2.imread('BlueLines.png') # Read input image
# Convert image to binary image with white lines on black background
################################################################################
gray = im[:, :, 1] # Get only the green color channel (the blue lines should be black).
# Apply threshold
ret, thresh_gray = cv2.threshold(gray, 10, 255, cv2.THRESH_BINARY)
# Invert polarity
thresh_gray = 255 - thresh_gray
################################################################################
# Split intersection points between lines (fill crossing points with black).
################################################################################
thresh_float = thresh_gray.astype(float) / 255 # Convert to float with range [0, 1]
thresh_float = cv2.filter2D(thresh_float, -1, np.ones((3, 3))) # Filter with ones 5x5
# Find pixels with "many" neighbors
thresh_intersect = np.zeros_like(thresh_gray)
thresh_intersect[(thresh_float > 3)] = 255; # Image of intersection points only.
thresh_gray[(thresh_float > 3)] = 0;
################################################################################
# Find contours in thresh_gray, and remove small contours.
################################################################################
if int(cv_major_ver) < 4:
_, contours, _ = cv2.findContours(thresh_gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
else:
contours, _ = cv2.findContours(thresh_gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
# Remove small contours, because their angle is not well defined
fcontours = []
for i in range(len(contours)):
c = contours[i]
if c.shape[0] > 6: # Why 6?
fcontours.append(c)
contours = fcontours
# Starting value.
n_lines = len(contours)
################################################################################
# Merge contours with close angles, and close edges
# Loop decreases n_lines when two lines are merged.
# Note: The solution is kind of "brute force" solution, and can be better.
################################################################################
# https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_contours/py_contour_features/py_contour_features.html
# Fitting a Line
rows,cols = im.shape[:2]
for i in range(len(contours)):
c = contours[i]
rect, box, line, angle = get_rect_box_line_and_angle(c)
for j in range(i+1, len(contours)):
c2 = contours[j]
rect2 = cv2.minAreaRect(c2)
box2 = cv2.boxPoints(rect2)
line2 = box2line(box2)
angle2 = rect2[2]
angle_diff = (angle - angle2 + 720) % 180 # Angle difference in degrees (force it to be positive number in range [0, 180].
angle_diff = np.minimum(angle_diff, 180 - angle_diff)
min_dist = minlinesdist(line, line2) # Minimum distance between any two edges of line and line2
if (angle_diff < 3) and (min_dist < 20):
color = (int((i+3)*100 % 255),int((i+3)*50 % 255), int((i+3)*70 % 255))
# https://stackoverflow.com/questions/22801545/opencv-merge-contours-together
# Merge contours together
tmp = np.vstack((c, c2))
c = cv2.convexHull(tmp)
# Draw merged contour (for testing)
im = cv2.drawContours(im, [c], 0, color, 2)
# Replace contour with merged one.
contours[j] = c
n_lines -= 1 # Subtract lines counter
break
################################################################################
print('Number of lines = {}'.format(n_lines))
# Display result (for testing):
cv2.imshow('thresh_gray', thresh_gray)
cv2.imshow('im', im)
cv2.waitKey(0)
cv2.destroyAllWindows()
Result:
Number of lines = 8
thresh_gray (before splitting):
thresh_gray (after splitting):
im:
Note:
I know the solution is not perfect, and not going to find perfect results on all of your 1000 images.
I think there is a better change that using Hough Transform and merging lines is going to give perfect results.
I am trying to detect a white Object on a black/white road to let an autonmous RC car drive around it. And i am detecting everything but the white box on the road.
What I tried can be seen in my code Example
#input= one video stream frame 320x240
frame = copy.deepcopy(input)
grayFrame = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
threshGray = cv2.adaptiveThreshold(
grayFrame,
255,
cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY,
blockSize=123,
C=-19,
)
contours,_ = cv2.findContours(threshGray, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
for cnt in contours:
#some filtering needs to be done
#
#after filtering append contour
filteredContours.append(cnt)
cv2.rectangle(frame, (x, y), (x + w, y + h), (3, 244, 244), 1)
cv2.drawContours(frame, filteredContours, -1, (255, 0, 255),1 )
cv2.imshow("with contours", frame)
cv2.imshow("adaptiveThreshhold", threshGray)
cv2.imshow("input", input)
I'm looking for a way to draw a bounding box around the obstacle.
Problem is I dont know how to extract this box from the rest.
It is probably because the contour of the box and the lines on the right are connected and thats why the bounding box is that big. Would be great if someone knows a way to do that.
Click here to see the Result
First: Input image
Second: after adaptiveThreshold
third: with contours(pink) and bounding boxes(yellow)
At this point in time, you got several candidates of white color value.
You need to add code in to the #some filtering needs to be done to rid candidate list of NOT bounding box you want to find.
I suggest you to compare your candidate list with square box as bigger as enough.
Because all of contours without BOX(that you want to find on the road) do not satisfy condition about square box as I mentioned above.
I think what you are looking for is triangular masking, as seen in the input image you have lane marking as well. Did try using a lane detector with this all the areas out of lane can be masked and only the spaces in lane can be processed.
Below I have tried to use Lane detector using HoughLinesP and added Contours as well. Try to use this, I did not test this code but I see no issues.
#! /usr/bin/env python 3
"""
Lane detector using the Hog transform method
"""
import cv2 as cv
import numpy as np
# import matplotlib.pyplot as plt
import random as rng
rng.seed(369)
def do_canny(frame):
# Converts frame to grayscale because we only need the luminance channel for detecting edges - less computationally expensive
gray = cv.cvtColor(frame, cv.COLOR_RGB2GRAY)
# Applies a 5x5 gaussian blur with deviation of 0 to frame - not mandatory since Canny will do this for us
blur = cv.GaussianBlur(gray, (5, 5), 0)
# Applies Canny edge detector with minVal of 50 and maxVal of 150
canny = cv.Canny(blur, 50, 150)
return canny
def do_segment(frame):
# Since an image is a multi-directional array containing the relative intensities of each pixel in the image, we can use frame.shape to return a tuple: [number of rows, number of columns, number of channels] of the dimensions of the frame
# frame.shape[0] give us the number of rows of pixels the frame has. Since height begins from 0 at the top, the y-coordinate of the bottom of the frame is its height
height = frame.shape[0]
# Creates a triangular polygon for the mask defined by three (x, y) coordinates
polygons = np.array([
[(0, height), (800, height), (380, 290)]
])
# Creates an image filled with zero intensities with the same dimensions as the frame
mask = np.zeros_like(frame)
# Allows the mask to be filled with values of 1 and the other areas to be filled with values of 0
cv.fillPoly(mask, polygons, 255)
# A bitwise and operation between the mask and frame keeps only the triangular area of the frame
segment = cv.bitwise_and(frame, mask)
return segment
def calculate_lines(frame, lines):
# Empty arrays to store the coordinates of the left and right lines
left = []
right = []
# Loops through every detected line
for line in lines:
# Reshapes line from 2D array to 1D array
x1, y1, x2, y2 = line.reshape(4)
# Fits a linear polynomial to the x and y coordinates and returns a vector of coefficients which describe the slope and y-intercept
parameters = np.polyfit((x1, x2), (y1, y2), 1)
slope = parameters[0]
y_intercept = parameters[1]
# If slope is negative, the line is to the left of the lane, and otherwise, the line is to the right of the lane
if slope < 0:
left.append((slope, y_intercept))
else:
right.append((slope, y_intercept))
# Averages out all the values for left and right into a single slope and y-intercept value for each line
left_avg = np.average(left, axis = 0)
right_avg = np.average(right, axis = 0)
# Calculates the x1, y1, x2, y2 coordinates for the left and right lines
left_line = calculate_coordinates(frame, left_avg)
right_line = calculate_coordinates(frame, right_avg)
return np.array([left_line, right_line])
def calculate_coordinates(frame, parameters):
slope, intercept = parameters
# Sets initial y-coordinate as height from top down (bottom of the frame)
y1 = frame.shape[0]
# Sets final y-coordinate as 150 above the bottom of the frame
y2 = int(y1 - 150)
# Sets initial x-coordinate as (y1 - b) / m since y1 = mx1 + b
x1 = int((y1 - intercept) / slope)
# Sets final x-coordinate as (y2 - b) / m since y2 = mx2 + b
x2 = int((y2 - intercept) / slope)
return np.array([x1, y1, x2, y2])
def visualize_lines(frame, lines):
# Creates an image filled with zero intensities with the same dimensions as the frame
lines_visualize = np.zeros_like(frame)
# Checks if any lines are detected
if lines is not None:
for x1, y1, x2, y2 in lines:
# Draws lines between two coordinates with green color and 5 thickness
cv.line(lines_visualize, (x1, y1), (x2, y2), (0, 255, 0), 5)
return lines_visualize
# The video feed is read in as a VideoCapture object
cap = cv.VideoCapture(1)
while (cap.isOpened()):
# ret = a boolean return value from getting the frame, frame = the current frame being projected in the video
ret, frame = cap.read()
canny = do_canny(frame)
cv.imshow("canny", canny)
# plt.imshow(frame)
# plt.show()
segment = do_segment(canny)
hough = cv.HoughLinesP(segment, 2, np.pi / 180, 100, np.array([]), minLineLength = 100, maxLineGap = 50)
# Averages multiple detected lines from hough into one line for left border of lane and one line for right border of lane
lines = calculate_lines(frame, hough)
# Visualizes the lines
lines_visualize = visualize_lines(frame, lines)
cv.imshow("hough", lines_visualize)
# Overlays lines on frame by taking their weighted sums and adding an arbitrary scalar value of 1 as the gamma argument
output = cv.addWeighted(frame, 0.9, lines_visualize, 1, 1)
contours, _ = cv.findContours(output, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
contours_poly = [None]*len(contours)
boundRect = [None]*len(contours)
centers = [None]*len(contours)
radius = [None]*len(contours)
for i, c in enumerate(contours):
contours_poly[i] = cv.approxPolyDP(c, 3, True)
boundRect[i] = cv.boundingRect(contours_poly[i])
centers[i], radius[i] = cv.minEnclosingCircle(contours_poly[i])
## [allthework]
## [zeroMat]
drawing = np.zeros((output.shape[0], output.shape[1], 3), dtype=np.uint8)
## [zeroMat]
## [forContour]
# Draw polygonal contour + bonding rects + circles
for i in range(len(contours)):
color = (rng.randint(0,256), rng.randint(0,256), rng.randint(0,256))
cv.drawContours(drawing, contours_poly, i, color)
cv.rectangle(drawing, (int(boundRect[i][0]), int(boundRect[i][1])), \
(int(boundRect[i][0]+boundRect[i][2]), int(boundRect[i][1]+boundRect[i][3])), color, 2)
# Opens a new window and displays the output frame
cv.imshow('Contours', drawing)
# Frames are read by intervals of 10 milliseconds. The programs breaks out of the while loop when the user presses the 'q' key
if cv.waitKey(10) & 0xFF == ord('q'):
break
# The following frees up resources and closes all windows
cap.release()
cv.destroyAllWindows()
try different values in the threshold for canny.
I work with logos and other simple graphics, in which there are no gradients or complex patterns. My task is to extract from the logo segments with letters and other elements.
To do this, I define the background color, and then I go through the picture in order to segment the images. Here is my code for more understanding:
MAXIMUM_COLOR_TRANSITION_DELTA = 100 # 0 - 765
def expand_segment_recursive(image, unexplored_foreground, segment, point, color):
height, width, _ = image.shape
# Unpack coordinates from point
py, px = point
# Create list of pixels to check
neighbourhood_pixels = [(py, px + 1), (py, px - 1), (py + 1, px), (py - 1, px)]
allowed_zone = unexplored_foreground & np.invert(segment)
for y, x in neighbourhood_pixels:
# Add pixel to segment if its coordinates within the image shape and its color differs from segment color no
# more than MAXIMUM_COLOR_TRANSITION_DELTA
if y in range(height) and x in range(width) and allowed_zone[y, x]:
color_delta = np.sum(np.abs(image[y, x].astype(np.int) - color.astype(np.int)))
print(color_delta)
if color_delta <= MAXIMUM_COLOR_TRANSITION_DELTA:
segment[y, x] = True
segment = expand_segment_recursive(image, unexplored_foreground, segment, (y, x), color)
allowed_zone = unexplored_foreground & np.invert(segment)
return segment
if __name__ == "__main__":
if len(sys.argv) < 2:
print("Pass image as the argument to use the tool")
exit(-1)
IMAGE_FILENAME = sys.argv[1]
print(IMAGE_FILENAME)
image = cv.imread(IMAGE_FILENAME)
height, width, _ = image.shape
# To filter the background I use median value of the image, as background in most cases takes > 50% of image area.
background_color = np.median(image, axis=(0, 1))
print("Background color: ", background_color)
# Create foreground mask to find segments in it (TODO: Optimize this part)
foreground = np.zeros(shape=(height, width, 1), dtype=np.bool)
for y in range(height):
for x in range(width):
if not np.array_equal(image[y, x], background_color):
foreground[y, x] = True
unexplored_foreground = foreground
for y in range(height):
for x in range(width):
if unexplored_foreground[y, x]:
segment = np.zeros(foreground.shape, foreground.dtype)
segment[y, x] = True
segment = expand_segment_recursive(image, unexplored_foreground, segment, (y, x), image[y, x])
cv.imshow("segment", segment.astype(np.uint8) * 255)
while cv.waitKey(0) != 27:
continue
Here is the desired result:
In the end of run-time I expect 13 extracted separated segments (for this particular image). But instead I got RecursionError: maximum recursion depth exceeded, which is not surprising as expand_segment_recursive() can be called for every pixel of the image. And since even with small image resolution of 600x500 i got at maximum 300K calls.
My question is how can I get rid of recursion in this case and possibly optimize the algorithm with Numpy or OpenCV algorithms?
You can actually use a thresholded image (binary) and connectedComponents to do this job in a couple of steps. Also, you may use findContours or other methods.
Here is the code:
import numpy as np
import cv2
# load image as greyscale
img = cv2.imread("hp.png", 0)
# puts 0 to the white (background) and 255 in other places (greyscale value < 250)
_, thresholded = cv2.threshold(img, 250, 255, cv2.THRESH_BINARY_INV)
# gets the labels and the amount of labels, label 0 is the background
amount, labels = cv2.connectedComponents(thresholded)
# lets draw it for visualization purposes
preview = np.zeros((img.shape[0], img.shape[2], 3), dtype=np.uint8)
print (amount) #should be 3 -> two components + background
# draw label 1 blue and label 2 green
preview[labels == 1] = (255, 0, 0)
preview[labels == 2] = (0, 255, 0)
cv2.imshow("frame", preview)
cv2.waitKey(0)
At the end, the thresholded image will look like this:
and the preview image (the one with the colored segments) will look like this:
With the mask you can always use numpy functions to get things like, coordinates of the segments you want or to color them (like I did with preview)
UPDATE
To get different colored segments, you may try to create a "border" between the segments. Since they are plain colors and not gradients, you can try to do an edge detector like canny and then put it black in the image....
import numpy as np
import cv2
img = cv2.imread("total.png", 0)
# background to black
img[img>=200] = 0
# get edges
canny = cv2.Canny(img, 60, 180)
# make them thicker
kernel = np.ones((3,3),np.uint8)
canny = cv2.morphologyEx(canny, cv2.MORPH_DILATE, kernel)
# apply edges as border in the image
img[canny==255] = 0
# same as before
amount, labels = cv2.connectedComponents(img)
preview = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)
print (amount) #should be 14 -> 13 components + background
# color them randomly
for i in range(1, amount):
preview[labels == i] = np.random.randint(0,255, size=3, dtype=np.uint8)
cv2.imshow("frame", preview )
cv2.waitKey(0)
The result is:
I have read few articles and saw videos of Lane Detection and thus decided to learn how it works
I'm completely new to OpenCV so kindly forgive me for dumb Doubts.
I took Udacity Opensource Project to develop Lane Detection,but I'm not able to execute the code. I 'm getting a value error which I'm not able to understand
Code:
import numpy as np
import cv2
import math
import matplotlib.pyplot as plt
def grayscale(img):
"""Applies the Grayscale transform
This will return an image with only one color channel
but NOTE: to see the returned image as grayscale
you should call plt.imshow(gray, cmap='gray')"""
return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
def canny(img, low_threshold, high_threshold):
"""Applies the Canny transform"""
return cv2.Canny(img, low_threshold, high_threshold)
def gaussian_blur(img, kernel_size):
"""Applies a Gaussian Noise kernel"""
return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)
def region_of_interest(img, vertices):
"""
Applies an image mask.
Only keeps the region of the image defined by the polygon
formed from `vertices`. The rest of the image is set to black.
"""
# defining a blank mask to start with
mask = np.zeros_like(img)
# defining a 3 channel or 1 channel color to fill the mask with depending on the input image
if len(img.shape) > 2:
channel_count = img.shape[2] # i.e. 3 or 4 depending on your image
ignore_mask_color = (255,) * channel_count
else:
ignore_mask_color = 255
# filling pixels inside the polygon defined by "vertices" with the fill color
cv2.fillPoly(mask, vertices, ignore_mask_color)
# returning the image only where mask pixels are nonzero
masked_image = cv2.bitwise_and(img, mask)
return masked_image
def draw_lines(img, lines, color=[255, 0, 0], thickness=10):
"""
NOTE: this is the function you might want to use as a starting point once you want to
average/extrapolate the line segments you detect to map out the full
extent of the lane (going from the result shown in raw-lines-example.mp4
to that shown in P1_example.mp4).
Think about things like separating line segments by their
slope ((y2-y1)/(x2-x1)) to decide which segments are part of the left
line vs. the right line. Then, you can average the position of each of
the lines and extrapolate to the top and bottom of the lane.
This function draws `lines` with `color` and `thickness`.
Lines are drawn on the image inplace (mutates the image).
If you want to make the lines semi-transparent, think about combining
this function with the weighted_img() function below
"""
imshape = img.shape
left_x1 = []
left_x2 = []
right_x1 = []
right_x2 = []
y_min = img.shape[0]
y_max = int(img.shape[0] * 0.611)
for line in lines:
for x1, y1, x2, y2 in line:
if ((y2 - y1) / (x2 - x1)) < 0:
mc = np.polyfit([x1, x2], [y1, y2], 1)
left_x1.append(np.int(np.float((y_min - mc[1])) / np.float(mc[0])))
left_x2.append(np.int(np.float((y_max - mc[1])) / np.float(mc[0])))
# cv2.line(img, (xone, imshape[0]), (xtwo, 330), color, thickness)
elif ((y2 - y1) / (x2 - x1)) > 0:
mc = np.polyfit([x1, x2], [y1, y2], 1)
right_x1.append(np.int(np.float((y_min - mc[1])) / np.float(mc[0])))
right_x2.append(np.int(np.float((y_max - mc[1])) / np.float(mc[0])))
# cv2.line(img, (xone, imshape[0]), (xtwo, 330), color, thickness)
l_avg_x1 = np.int(np.nanmean(left_x1))
l_avg_x2 = np.int(np.nanmean(left_x2))
r_avg_x1 = np.int(np.nanmean(right_x1))
r_avg_x2 = np.int(np.nanmean(right_x2))
# print([l_avg_x1, l_avg_x2, r_avg_x1, r_avg_x2])
cv2.line(img, (l_avg_x1, y_min), (l_avg_x2, y_max), color, thickness)
cv2.line(img, (r_avg_x1, y_min), (r_avg_x2, y_max), color, thickness)
def hough_lines(img, rho, theta, threshold, min_line_len, max_line_gap):
"""
`img` should be the output of a Canny transform.
Returns an image with hough lines drawn.
"""
lines = cv2.HoughLinesP(img, rho, theta, threshold, np.array([]), minLineLength=min_line_len,
maxLineGap=max_line_gap)
line_img = np.zeros(img.shape, dtype=np.uint8)
draw_lines(line_img, lines)
return line_img
def process_image(img):
img_test = grayscale(img)
img_test = gaussian_blur(img_test, 7)
img_test = canny(img_test, 50, 150)
imshape = img.shape
vertices = np.array([[(100, imshape[0]), (400, 330), (600, 330), (imshape[1], imshape[0])]], dtype=np.int32)
img_test = region_of_interest(img_test, vertices)
rho = 2 # distance resolution in pixels of the Hough grid
theta = np.pi / 180 # angular resolution in radians of the Hough grid
threshold = 55 # minimum number of votes (intersections in Hough grid cell)
min_line_length = 40 # minimum number of pixels making up a line
max_line_gap = 100 # maximum gap in pixels between connectable line segments
line_image = np.copy(img) * 0 # creating a blank to draw lines on
img_test = hough_lines(img_test, rho, theta, threshold, min_line_length, max_line_gap)
return img_test
img = cv2.imread("sy1.jpg")
res = process_image(img)
plt.imshow(res)
The Resulting Error:
/Users/ViditShah/anaconda/envs/py27/bin/python /Users/ViditShah/Downloads/untitled1/gist.py
/Users/ViditShah/Downloads/untitled1/gist.py:85: RuntimeWarning: Mean of empty slice
r_avg_x1 = np.int(np.nanmean(right_x1))
Traceback (most recent call last):
File "/Users/ViditShah/Downloads/untitled1/gist.py", line 122, in <module>
res = process_image(img)
File "/Users/ViditShah/Downloads/untitled1/gist.py", line 117, in process_image
img_test = hough_lines(img_test, rho, theta, threshold, min_line_length, max_line_gap)
File "/Users/ViditShah/Downloads/untitled1/gist.py", line 100, in hough_lines
draw_lines(line_img, lines)
File "/Users/ViditShah/Downloads/untitled1/gist.py", line 85, in draw_lines
r_avg_x1 = np.int(np.nanmean(right_x1))
ValueError: cannot convert float NaN to integer
Process finished with exit code 1
I'm using python2.7
Please Guide me.
Your Sincerely,
Vidit Shah
One possibility is dividing by zero creating Nans when you calculate gradient; try filtering out for x1 == x2. This potential source of errors will crop up only rarely.
The most important issue is that the threshold is set too high in the Hough transform (at 55) for the structure of your code. If the Hough Lines stage does not identify lines then you will not be able to plot them.
You can get around this by either lowering the threshold (and losing quality in your line-detection for the cases when it does work) or adjusting something else in your code, for example using error handling or pre-processing the image differently so that there will always be lines output by the Hough step.