Python ROI not being croped properly from contour - python-3.x

Question

Hello everybody before you close this question, i have already searched here, here too and also here
I am using python code to detect the Leaf in an image, using contours finding and then find out the largest contour, the part works best, but then i want only the leaf part of the image and skip the rest of the image to avoid unnecessary content in the resultant output, some of the methods in the link suggests bounding box but this still includes extra content in the image as the shape is not rectangular it's irregular, sample is attached
The code is following
import cv2
import numpy as np
img = cv2.imread("blob.jpg", -1)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 101, 3)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5))
blob = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (9,9))
blob = 255 - cv2.morphologyEx(blob, cv2.MORPH_CLOSE, kernel)
cnts = cv2.findContours(blob, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if len(cnts) == 2:
cnts = cnts[0]
else:
cnts = cnts[1]
big_contour = max(cnts, key = cv2.contourArea)
blob_area_thresh = 1000
blob_area = cv2.contourArea(big_contour)
if blob_area < blob_area_thresh:
print("Leaf is Too Small")
else:
#problem starts from here . i tested big_contour is just perfect by drawing on actual image
mask = np.zeros_like(img)
cv2.drawContours(mask, big_contour, -1, 255, -1)
out = np.zeros_like(img)
out[mask == 255] = img[mask == 255]
cv2.imwrite('output.jpg', out)
Now the problem is i am getting the resultant image as black nothing cropped all black pixels

Answer 1

There is a problem with your contour because it is not circulating the leaf as the end of the leaf on the right is out of the image.
You can see this when I try to fill the contour to create a mask using
cv2.fillPoly(mask, pts =[big_contour], color=(255,255,255))
it doesn't fill the leaf.
However I tried something although not perfect and has some background left but it crops the leaf to some extend.
import cv2
import numpy as np
img = cv2.imread("96Klg.jpg", -1)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 101, 3)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5))
blob = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
#kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (9,9))
#blob = 255 - cv2.morphologyEx(blob, cv2.MORPH_CLOSE, kernel)
cnts = cv2.findContours(blob, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if len(cnts) == 2:
cnts = cnts[0]
else:
cnts = cnts[1]
big_contour = max(cnts, key = cv2.contourArea)
blob_area_thresh = 1000
blob_area = cv2.contourArea(big_contour)
if blob_area < blob_area_thresh:
print("Leaf is Too Small")
else:
#problem starts from here . i tested big_contour is just perfect by drawing on actual image
mask = np.ones_like(img)
mask.fill(255)
cv2.fillPoly(mask, pts =[big_contour], color=(0,0,0))
#cv2.drawContours(mask, big_contour, -1, (255,255,255), 1)
out = np.zeros_like(img)
out[mask == 255] = img[mask == 255]
width = int(gray.shape[1] * 0.25)
height = int(gray.shape[0] * 0.25)
dim = (width, height)
# resize image
resized = cv2.resize(out, dim, interpolation = cv2.INTER_AREA)
resizedmask = cv2.resize(mask, dim, interpolation = cv2.INTER_AREA)
cv2.imshow('gray',resized)
cv2.imshow('out',resizedmask)
Output