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Region extraction_V2.py
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Region extraction_V2.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Oct 28 15:17:55 2020
@author: Akshay
"""
import glob
import cv2 as cv
import matplotlib.pyplot as plt
import numpy as np
import imutils
images = glob.glob("D:\\My_project\\Image segmentation model\\Main image folde\\Apple\\*.jpg")
file_count = len(images)
for i in range(0,file_count):
i=5
image = images[i]
image = cv.imread(image)
im_rgb = cv.cvtColor(image, cv.COLOR_BGR2RGB)
plt.imshow(im_rgb)
plt.imshow(image)
# Convert image in grayscale
gray_im = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
plt.imshow(gray_im)
# # plt.subplot(221)
# plt.title('Grayscale image')
# plt.imshow(gray_im, cmap="gray", vmin=0, vmax=255)
blur = cv.GaussianBlur(image, (9,9), 0)
blur2 = cv.GaussianBlur(image, (27,27), 0)
plt.imshow(blur)
edges = cv.Canny(blur,75,75)
edges2 = cv.Canny(blur2,75,75)
plt.imshow(edges)
### Morphological transformations
kernel =np.ones((6,6))
closing = cv.morphologyEx(edges, cv.MORPH_CLOSE, kernel)
closing=closing/255
plt.imshow(closing)
kernel =np.ones((15,15))
erosion = cv.dilate(edges2, kernel, iterations= 1 )
erosion = erosion/255
plt.imshow(erosion)
mask = np.zeros(gray_im.shape, dtype=np.uint8)
diff = cv.bitwise_and(erosion,closing)
diff = diff.astype(np.uint8)
# plt.figure()
# plt.subplot(221)
# plt.title('Image_1')
# plt.imshow(closing)
# plt.subplot(222)
# plt.title('Image_2')
# plt.imshow(erosion)
# plt.subplot(223)
# plt.title('Difference')
# plt.imshow(diff)
def flood_fill(input_img):
im_floodfill = input_img.copy()
# Taking two pixels more than the actual image
h, w = input_img.shape[:2]
mask = np.zeros((h+2, w+2), np.uint8)
# Floodfill from point (0, 0)
cv.floodFill(im_floodfill, mask, (0,0), 255);
# Inverting floodfilled image
im_floodfill_inv = cv.bitwise_not(im_floodfill)
# Combine the two images to get the foreground.
out_img = input_img | im_floodfill_inv
return out_img
fill = flood_fill(diff)
plt.imshow(fill)
ret,thresh = cv.threshold(fill,0,1,0)
plt.imshow(thresh)
im2,contours,hierarchy = cv.findContours(thresh, cv.RETR_LIST, cv.CHAIN_APPROX_SIMPLE)
# This step is for finding actual contours
items = cv.findContours(thresh,cv.RETR_LIST, cv.CHAIN_APPROX_SIMPLE)
for c in contours:
# cv.boundingRect(c):: straight rectangle, it doesn't consider the rotation of the object.
#For rotatiated boundbox use cv.minAreaRect(cnt)
rect = cv.boundingRect(c)
x,y,w,h = rect
Boundbox = cv.rectangle(im_rgb,(x,y),(x+w,y+h),(0,255,0),1)
plt.imshow(Boundbox)
# Saving the region of interest
image_patch = im_rgb[y:y+h, x:x+w]
plt.imshow(image_patch)
plt.imsave("ROI.png", image_patch)
#FInding actual contours of the object
cnts = imutils.grab_contours(items)
c = max(cnts, key=cv.contourArea)
# draw the contours of c
Act_con= cv.drawContours(im_rgb, [c], -1, (0, 0, 255), 1)
plt.imshow(Act_con)
#### Additional physical parameters
M = cv.moments(c)
print( M )
# Calculating centroid of the region
cx = int(M['m10']/M['m00'])
cy = int(M['m01']/M['m00'])
print('\nCx = ', cx)
print('Cy = ', cy)
print('Centroid = ',(cx,cy))
###Contour area
area = cv.contourArea(c)
print('Area = ',area)
# plt.figure()
# plt.subplot(221)
# plt.title('Image_1')
# plt.imshow(Boundbox)
# plt.subplot(222)
# plt.title('ROI')
# plt.imshow(image_patch)