CV, detecting object size in photo (Python) [closed]-CodePudding

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size of each ball is 1cm, so i should detect them, calculate it's size in pixels and find scale. Then i will calculate distance between each two balls. We can't use manual marking, because of large number of photos. So I want to write python script that does it automatically. Сan you advice me on python libraries or frameworks that can solve my problem. Maybe there are some ready-made solutions for detection geometric figures on contrast background.

CodePudding user response：

The Python package opencv-python is a great fit for this task.

Image segmentation based on color should work fine, you can couple it with edge detection as the balls are edges are well defined.

There are a lot of Stack Overflow questions about segmentation of things that could inspire you:

import cv2
import numpy as np

# Read image
img = cv2.imread('beads.jpg')

# Convert to HSV and keep the hue channel
hue = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)[:,:,0]

# beads are red but on the blue side, so very bright hues
# so threshold hue at 165 (out of 180)
thresh = cv2.threshold(hue, 165, 255, cv2.THRESH_BINARY)[1]

# apply morphology to fill beads and remove excess spots
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5))
morph = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (6,6))
morph = cv2.morphologyEx(morph, cv2.MORPH_CLOSE, kernel)

# get contours and enclosing circles
contours = cv2.findContours(morph, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
circles = img.copy()
num_circles = len(contours)
ave = 0
for cntr in contours:
    center, radius = cv2.minEnclosingCircle(cntr)
    cx = int(round(center[0]))
    cy = int(round(center[1]))
    rr = int(round(radius))
    # draw enclosing circle over beads
    cv2.circle(circles, (cx,cy), rr, (0, 255, 0), 2)
    # cumulate radii for average
    ave = ave   radius

# print average radius
ave_radius = ave / num_circles
print("average radius:", ave_radius)
print ("number of circles:", num_circles)

# save results
cv2.imwrite('beads_thresh.jpg', thresh)
cv2.imwrite('beads_morph.jpg', morph)
cv2.imwrite('beads_circles.jpg', circles)

cv2.imshow('thresh', thresh)
cv2.imshow('morph', morph)
cv2.imshow('circles', circles)
cv2.waitKey(0)
cv2.destroyAllWindows()

Thresholded Image:

Morphology Cleaned Image:

Circles on Input:

Average Radius and Count:

average radius: 13.95605175635394
number of circles: 34

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Tags：

python

image-processing

computer-vision

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