68 lines
1.6 KiB
Python
68 lines
1.6 KiB
Python
# import required libraries
|
|
import cv2
|
|
import sys
|
|
|
|
# read the input image
|
|
img = cv2.imread('hires_test.png')
|
|
|
|
|
|
if not img:
|
|
print("unable to read image")
|
|
sys.exit(-1)
|
|
|
|
print("read done")
|
|
|
|
# convert the image to grayscale
|
|
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
|
|
print("convert to gray done")
|
|
|
|
# apply thresholding on the gray image to create a binary image
|
|
ret,thresh = cv2.threshold(gray,127,255,0)
|
|
|
|
# find the contours
|
|
contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
areas = list()
|
|
|
|
for cnt in contours:
|
|
#print(cv2.contourArea(cnt))
|
|
areas.append(cv2.contourArea(cnt))
|
|
|
|
areas.sort(reverse=True)
|
|
|
|
#print(areas)
|
|
|
|
if (len(areas) >= 5):
|
|
outer = areas[0]
|
|
inter_min = areas[4]
|
|
print("Outer area: " + str(outer))
|
|
print("Inner area: " + str(inter_min))
|
|
|
|
index = 0
|
|
|
|
for cnt in contours:
|
|
area = cv2.contourArea(cnt)
|
|
if( (area < outer) and (area >= inter_min)):
|
|
# compute the bounding rectangle of the contour
|
|
x,y,w,h = cv2.boundingRect(cnt)
|
|
# draw the bounding rectangle
|
|
#imgView = cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)
|
|
|
|
x = x + 5
|
|
y = y + 5
|
|
w = w - 10
|
|
h = h - 10
|
|
|
|
crop_img = img[y:y+h, x:x+w]
|
|
#cv2.imshow("cropped", crop_img)
|
|
cv2.imwrite("export_"+str(index)+".png", crop_img)
|
|
index = index + 1
|
|
#cv2.waitKey(0)
|
|
|
|
# display the image with bounding rectangle drawn on it
|
|
#imgDownscaled = cv2.resize(imgView, (410, 876))
|
|
#cv2.imshow("Bounding Rectangle", imgDownscaled)
|
|
#cv2.waitKey(0)
|
|
#cv2.destroyAllWindows()
|
|
|