opencv:如何合并近轮廓以获得最大的轮廓?
opencv: how to merge near contours to get the one big outest contour?
我正在尝试将孩子的绘图数字化为 SVG 或透明 png 文件格式,以便它们可以在 Scratch 中使用。白纸换成透明背景,所有绘图部分保留。
我的计划是获取图纸的最外轮廓并生成一个遮罩,然后使用遮罩来获得没有纸张背景的绘图部分。
问题是绘图可能不连续,这意味着可能有一些小孔导致将整个绘图轮廓分成许多小轮廓。
现在我想连接最近的最外轮廓以形成一个大的最外轮廓用于掩蔽。
附上原图和处理后的结果
代码:
from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
import random as rng
rng.seed(12345)
def thresh_callback(val):
threshold = val
# Detect edges using Canny
canny_output = cv.Canny(src_gray, threshold, threshold * 2)
# Find contours
contours, hierarchy = cv.findContours(canny_output, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
# Draw contours
drawing = np.zeros((canny_output.shape[0], canny_output.shape[1], 3), dtype=np.uint8)
for i in range(len(contours)):
color = (rng.randint(0,256), rng.randint(0,256), rng.randint(0,256))
cv.drawContours(drawing, contours, i, color, 2, cv.LINE_8, hierarchy, 0)
# Show in a window
cv.imshow('Contours', drawing)
# Load source image
parser = argparse.ArgumentParser(description='Code for Finding contours in your image tutorial.')
parser.add_argument('--input', help='Path to input image.', default='IMG_4446.jpg')
args = parser.parse_args()
src = cv.imread(cv.samples.findFile(args.input))
if src is None:
print('Could not open or find the image:', args.input)
exit(0)
# Convert image to gray and blur it
src_gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
src_gray = cv.blur(src_gray, (3,3))
# Create Window
source_window = 'Source'
cv.namedWindow(source_window)
cv.imshow(source_window, src)
max_thresh = 255
thresh = 100 # initial threshold
cv.createTrackbar('Canny Thresh:', source_window, thresh, max_thresh, thresh_callback)
thresh_callback(thresh)
cv.waitKey()
import cv2, numpy as np
# Read Image
img = cv2.imread('/home/stephen/Desktop/test_img.png')
img =cv2.resize(img, (750,1000))
# Find the gray image
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Gray
gray = cv2.blur(gray, (2,2))
cv2.imwrite('/home/stephen/Desktop/gray.png',gray)
# Find the canny image
canny = cv2.Canny(gray, 30, 150) # Canny
# Find contours
contours, _ = cv2.findContours(canny,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
# Draw contours on canny (this connects the contours)
cv2.drawContours(canny, contours, -1, 255, 6)
cv2.imwrite('/home/stephen/Desktop/contours.png',canny)
# Get mask for floodfill
h, w = canny.shape[:2]
mask = np.zeros((h+2, w+2), np.uint8)
# Floodfill from point (0, 0)
cv2.floodFill(canny, mask, (0,0), 123)
cv2.imwrite('/home/stephen/Desktop/floodfill.png',canny)
# Exclude everying but the floodfill region
canny = cv2.inRange(canny, 122, 124)
cv2.imwrite('/home/stephen/Desktop/inrange.png',canny)
我正在尝试将孩子的绘图数字化为 SVG 或透明 png 文件格式,以便它们可以在 Scratch 中使用。白纸换成透明背景,所有绘图部分保留。
我的计划是获取图纸的最外轮廓并生成一个遮罩,然后使用遮罩来获得没有纸张背景的绘图部分。
问题是绘图可能不连续,这意味着可能有一些小孔导致将整个绘图轮廓分成许多小轮廓。
现在我想连接最近的最外轮廓以形成一个大的最外轮廓用于掩蔽。
附上原图和处理后的结果
代码:
from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
import random as rng
rng.seed(12345)
def thresh_callback(val):
threshold = val
# Detect edges using Canny
canny_output = cv.Canny(src_gray, threshold, threshold * 2)
# Find contours
contours, hierarchy = cv.findContours(canny_output, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
# Draw contours
drawing = np.zeros((canny_output.shape[0], canny_output.shape[1], 3), dtype=np.uint8)
for i in range(len(contours)):
color = (rng.randint(0,256), rng.randint(0,256), rng.randint(0,256))
cv.drawContours(drawing, contours, i, color, 2, cv.LINE_8, hierarchy, 0)
# Show in a window
cv.imshow('Contours', drawing)
# Load source image
parser = argparse.ArgumentParser(description='Code for Finding contours in your image tutorial.')
parser.add_argument('--input', help='Path to input image.', default='IMG_4446.jpg')
args = parser.parse_args()
src = cv.imread(cv.samples.findFile(args.input))
if src is None:
print('Could not open or find the image:', args.input)
exit(0)
# Convert image to gray and blur it
src_gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
src_gray = cv.blur(src_gray, (3,3))
# Create Window
source_window = 'Source'
cv.namedWindow(source_window)
cv.imshow(source_window, src)
max_thresh = 255
thresh = 100 # initial threshold
cv.createTrackbar('Canny Thresh:', source_window, thresh, max_thresh, thresh_callback)
thresh_callback(thresh)
cv.waitKey()
import cv2, numpy as np
# Read Image
img = cv2.imread('/home/stephen/Desktop/test_img.png')
img =cv2.resize(img, (750,1000))
# Find the gray image
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Gray
gray = cv2.blur(gray, (2,2))
cv2.imwrite('/home/stephen/Desktop/gray.png',gray)
# Find the canny image
canny = cv2.Canny(gray, 30, 150) # Canny
# Find contours
contours, _ = cv2.findContours(canny,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
# Draw contours on canny (this connects the contours)
cv2.drawContours(canny, contours, -1, 255, 6)
cv2.imwrite('/home/stephen/Desktop/contours.png',canny)
# Get mask for floodfill
h, w = canny.shape[:2]
mask = np.zeros((h+2, w+2), np.uint8)
# Floodfill from point (0, 0)
cv2.floodFill(canny, mask, (0,0), 123)
cv2.imwrite('/home/stephen/Desktop/floodfill.png',canny)
# Exclude everying but the floodfill region
canny = cv2.inRange(canny, 122, 124)
cv2.imwrite('/home/stephen/Desktop/inrange.png',canny)