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A procedure to perform a blob analysis in OpenCV using Python. Details at https://code.adonline.id.au/blob-analysis-with-opencv-in-python/
#!/usr/bin/python
import matplotlib as mpl
import matplotlib.pyplot as plt
import cv2
import numpy as np
import csv
colours = [(230, 63, 7), (48, 18, 59), (68, 81, 191), (69, 138, 252), (37, 192, 231), (31, 233, 175), (101, 253, 105), (175, 250, 55), (227, 219, 56), (253, 172, 52), (246, 108, 25), (216, 55, 6), (164, 19, 1), (90, 66, 98), (105, 116, 203), (106, 161, 253), (81, 205, 236), (76, 237, 191), (132, 253, 135), (191, 251, 95), (233, 226, 96), (254, 189, 93), (248, 137, 71), (224, 95, 56), (182, 66, 52), (230, 63, 7), (48, 18, 59), (68, 81, 191), (69, 138, 252), (37, 192, 231), (31, 233, 175), (101, 253, 105), (175, 250, 55), (227, 219, 56), (253, 172, 52), (246, 108, 25), (216, 55, 6), (164, 19, 1), (90, 66, 98), (105, 116, 203), (106, 161, 253), (81, 205, 236), (76, 237, 191), (132, 253, 135), (191, 251, 95), (233, 226, 96), (254, 189, 93), (248, 137, 71), (224, 95, 56), (182, 66, 52)]
img = cv2.imread('/path/to/australian_states.png') #Read image
grey = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Convert to greyscale
grey_inv = cv2.bitwise_not(grey) #Inverse
ret,thresh_au = cv2.threshold(grey_inv,20,255,cv2.THRESH_BINARY) #Threshold
def blob_properties(contours):
cont_props= []
i = 0
for cnt in contours:
area= cv2.contourArea(cnt)
perimeter = cv2.arcLength(cnt,True)
convexity = cv2.isContourConvex(cnt)
x1,y1,w,h = cv2.boundingRect(cnt)
x2 = x1+w
y2 = y1+h
aspect_ratio = float(w)/h
rect_area = w*h
extent = float(area)/rect_area
hull = cv2.convexHull(cnt)
hull_area = cv2.contourArea(hull)
solidity = float(area)/hull_area
(xa,ya),(MA,ma),angle = cv2.fitEllipse(cnt)
rect = cv2.minAreaRect(cnt)
(xc,yc),radius = cv2.minEnclosingCircle(cnt)
ellipse = cv2.fitEllipse(cnt)
rows,cols = img.shape[:2]
[vx,vy,xf,yf] = cv2.fitLine(cnt, cv2.DIST_L2,0,0.01,0.01)
lefty = int((-xf*vy/vx) + yf)
righty = int(((cols-xf)*vy/vx)+yf)
# Add parameters to list
add = i+1, area, round(perimeter, 1), convexity, round(aspect_ratio, 3), round(extent, 3), w, h, round(hull_area, 1), round(angle, 1), x1, y1, x2, y2,round(radius, 6), xa, ya, xc, yc, xf[0], yf[0], rect, ellipse, vx[0], vy[0], lefty, righty
cont_props.append(add)
i += 1
return cont_props
# Sort contours
sorted_contours= sorted(contours, key=cv2.contourArea, reverse= True)
blobs_data = blob_properties(sorted_contours)
# Plot contours
image_plot = img.copy()
i = 0
for rows in blobs_data:
pos = blobs_data[i]
inverted_colours = (255-colours[i][0],255-colours[i][1],255-colours[i][2])
cv2.drawContours(image_plot, [sorted_contours[i]], -1, colours[i], -1) #, colours[i], thickness=cv2.FILLED)
#cv2.rectangle(image_plot, (pos[10], pos[11]), (pos[12], pos[13]), inverted_colours, 2)
cv2.putText(image_plot, str(pos[0]), (int(pos[19]), int(pos[20])), cv2.FONT_HERSHEY_SIMPLEX, 1, inverted_colours, 2, cv2.LINE_AA)
i += 1
plt.imshow(image_plot)
# Save image
cv2.imwrite("/path/to/australian_states_blobs_ids.png", image_plot)
# Collect data
header = ['blob_id','area','perimeter','convexity','aspect_ratio','extent','width','height','hull_area','ellipse_angle','rect_x1','rect_y1','rect_x2','rect_y2','radius','xa','ya','xc','yc','xf','yf','min_area_rectangle','ellipse','vx','vy','left_y','right_y']
with open("path/to/australian_states.csv", 'w', newline='') as file:
dw = csv.DictWriter(file, delimiter=',', fieldnames=header)
dw.writeheader()
writer = csv.writer(file)
writer.writerows(blobs_data)
# Grab stats to plot for biggest blob
# Rotated rectangle
rect = blobs_data[0][21]
box = cv2.boxPoints(rect)
box = np.int0(box)
# Mimimum enclosing circle
centre = (int(blobs_data[0][17]),int(blobs_data[0][18]))
radius = int(blobs_data[0][14])
# Plot the parameters on the biggest blob
image_plot2 = img.copy()
pos = blobs_data[0]
rows,cols = img.shape[:2]
cv2.rectangle(image_plot2, (pos[10], pos[11]), (pos[12], pos[13]), colours[1], 2) # Bounding rectangle
cv2.drawContours(image_plot2,[box],0,colours[4],2) # Rotated rectangle
cv2.circle(image_plot2,centre,radius,colours[7],2) # Minimum Enclosing Circle
cv2.ellipse(image_plot2,pos[22],colours[10],2) # Fitted ellipse
cv2.line(image_plot2,(cols-1,pos[26]),(0,pos[25]),colours[14],2) # Fitted line
plt.imshow(image_plot2)
# Save image
cv2.imwrite("/path/to/australian_states_blobs_biggest.png", image_plot2)
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