I am trying to extract handwritten numbers and alphabet from an image, for that i followed this stackoverflow link
but it is still not extracting the numbers and alphabet properly and picking up the border line as well.
You can find the result below:
Code:
import cv2
import imutils
# Load image, grayscale, Otsu's threshold
image = cv2.imread('xxx/ocr/pic_crop_2.png')
image = imutils.resize(image, width=375)
img=image.copy()
# Remove border
kernel_vertical = cv2.getStructuringElement(cv2.MORPH_RECT, (1,50))
temp1 = 255 - cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel_vertical)
horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (50,1))
temp2 = 255 - cv2.morphologyEx(image, cv2.MORPH_CLOSE, horizontal_kernel)
temp3 = cv2.add(temp1, temp2)
result = cv2.add(temp3, image)
# Convert to grayscale and Otsu's threshold
gray = cv2.cvtColor(result, cv2.COLOR_BGR2GRAY)
_,thresh = cv2.threshold(gray, 120, 255, cv2.THRESH_OTSU | cv2.THRESH_BINARY_INV)
# thresh=cv2.dilate(thresh,None,iterations=1)
# Find contours and filter using contour area
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[0]
for c in cnts:
x,y,w,h = cv2.boundingRect(c)
cv2.rectangle(img, (x, y), (x + w, y + h), (36,255,12), 2)
cv2.imshow('thresh', thresh)
cv2.imshow('img', img)
cv2.waitKey()
I tried to use dialate but no luck.
Please find the sample image below:
you Can Check the Contour Area For Fix That.
import cv2
# Load image, grayscale, Otsu's threshold
image = cv2.imread('pic_crop_2.png')
#image = cv2.resize(image, width=375)
img=image.copy()
# Remove border
kernel_vertical = cv2.getStructuringElement(cv2.MORPH_RECT, (1,50))
temp1 = 255 - cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel_vertical)
horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (50,1))
temp2 = 255 - cv2.morphologyEx(image, cv2.MORPH_CLOSE, horizontal_kernel)
temp3 = cv2.add(temp1, temp2)
result = cv2.add(temp3, image)
# Convert to grayscale and Otsu's threshold
gray = cv2.cvtColor(result, cv2.COLOR_BGR2GRAY)
_,thresh = cv2.threshold(gray, 120, 255, cv2.THRESH_OTSU | cv2.THRESH_BINARY_INV)
# thresh=cv2.dilate(thresh,None,iterations=1)
# Find contours and filter using contour area
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[0]
MIN_AREA=200
for c in cnts:
if cv2.contourArea(c)>MIN_AREA:
x,y,w,h = cv2.boundingRect(c)
cv2.rectangle(img, (x, y), (x + w, y + h), (36,255,12), 2)
cv2.imshow('thresh', thresh)
cv2.imshow('img', img)
cv2.waitKey()
3x3 closing and binarization give good results, it seems (thought the black area is problematic and should be erased or cropped explicitly).
Related
I am new in computer vision, and I want to create a program which helps me to detect box in the image and save as an image.
and etc...
I tried some code but did not get my desired result.
here is my code and its output.
import cv2
# Load iamge, grayscale, adaptive threshold
image = cv2.imread('image.jpeg')
result = image.copy()
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
thresh = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV,51,9)
# Fill rectangular contours
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
cv2.drawContours(thresh, [c], -1, (255,255,255), -1)
# Morph open
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9,9))
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=4)
# Draw rectangles
cnts = cv2.findContours(opening, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
x,y,w,h = cv2.boundingRect(c)
cv2.rectangle(image, (x, y), (x + w, y + h), (36,255,12), 3)
cv2.imshow('thresh', thresh)
cv2.imshow('opening', opening)
cv2.imshow('image', image)
cv2.waitKey()
output:
All you need to do is simply first remove the outermost white area, that is, make it black so that we can detect the boxes without any issues using the cv2.RETR_EXTERNAL flag as they are not touching. Then we'll just extract the boxes one by one.
To remove the outmost area, I have used the point polygon test of the contours. If the point (1, 1) lies inside or on a contour, it is not drawn and every other contour will be drawn on a new image. From this new image, I have read the box contours and extracted them.
import cv2
import numpy as np
img = cv2.imread("2lscp.png", cv2.IMREAD_GRAYSCALE)
ret, img = cv2.threshold(img, 50, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
Contours = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)[-2]
newImg = np.zeros(img.shape, dtype=np.uint8)
for Contour in Contours:
if cv2.pointPolygonTest(Contour, (1, 1), False) == -1:
cv2.drawContours(newImg, [Contour], -1, 255, 1)
Contours = cv2.findContours(newImg, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2]
for Contour in Contours:
[x, y, w, h] = cv2.boundingRect(Contour)
cv2.imshow("box extracted", img[y:y+h, x:x+w])
cv2.waitKey(0)
cv2.destroyAllWindows()
This case seems particularly simple because the image is quasi-binary. Detect the contours of the white regions and select those that have an area like 10 to 15% of the whole image. These are the desired boxes. Then fit a rectangle or rotated rectangle.
No need for additional processing.
Here is solution
try this:
import cv2
import numpy as np
#Read input image
img = cv2.imread('hw_data.png')
#convert from BGR to HSV color space
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#apply threshold
thresh = cv2.threshold(gray, 30, 255, cv2.THRESH_BINARY)[1]
# find contours and get one with area about 180*35
# draw all contours in green and accepted ones in red
contours = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
#area_thresh = 0
min_area = 0.95*180*44
max_area = 1.05*180*44
print(min_area)
print(max_area)
result = img.copy()
i = 1
for c in contours:
# print(c)
area = cv2.contourArea(c)
cv2.drawContours(result, [c], -1, (0, 255, 0), 1)
x,y,w,h = cv2.boundingRect(c)
# crop region of img using bounding box
region = result[y:y+h, x:x+w]
# save region to new image
print(region.shape,' i ',i)
# cv2.imwrite("black_region_{0}.png".format(i), region)
i = i + 1
if region.shape[0]>70 and region.shape[1]<100:
cv2.imwrite("black_region_{0}.png".format(i), region)
# break
# if area > min_area and area < max_area:
# cv2.drawContours(result, [c], -1, (0, 0, 255), 1)
# break
# save result
# cv2.imwrite("box_found.png", result)
# show images
# cv2.imshow("GRAY", gray)
# cv2.imshow("THRESH", thresh)
# cv2.imshow("RESULT", result)
# cv2.waitKey(0)
I tried to count building from a gmaps image using opencv.
Here's the code I used from an answer in stackoverflow :
import cv2
import numpy as np
image = cv2.imread('converted2.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.medianBlur(gray, 5)
sharpen_kernel = np.array([[-1,-1,-1], [-1,9,-1], [-1,-1,-1]])
sharpen = cv2.filter2D(blur, -1, sharpen_kernel)
thresh = cv2.threshold(sharpen,160,255, cv2.THRESH_BINARY_INV)[1]
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3))
close = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel, iterations=2)
cnts = cv2.findContours(close, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
min_area = 10
max_area = 1000000
image_number = 0
for c in cnts:
area = cv2.contourArea(c)
if area > min_area and area < max_area:
x,y,w,h = cv2.boundingRect(c)
ROI = image[y:y+h, x:x+w]
cv2.imwrite('ROI_{}.png'.format(image_number), ROI)
cv2.rectangle(image, (x, y), (x + w, y + h), (50,255,10), 2)
image_number += 1
cv2.imshow('sharpen', sharpen)
cv2.imshow('close', close)
cv2.imshow('thresh', thresh)
print(image_number)
cv2.imshow('image', image)
cv2.waitKey()
The image is black and white.
I tried to detect how many building. But it gave me wrong number of building.
The output was 17.
The original image =
The result =
Edited :
I tried using different code :
org=cv2.imread('maps_converted2.jpg')
cv2.imshow('sample',org)
img = cv2.imread('maps_converted2.jpg',0)
ret,thresh = cv2.threshold(img,127,255,0)
contours,hierarchy = cv2.findContours(thresh, 1, 2)
for cnt in contours:
x,y,w,h = cv2.boundingRect(cnt)
img = cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)
cv2.imshow("image", img)
cv2.waitKey()
This is the result :
I have the image, i have removed the noise (dots in the background) and, I want to draw a bounding box around the block of text In image how can I do it using python OpenCV
Input image:
Noise Removed Image:
Here is the code used to remove noise in background Where i can change to save images with bounding boxes around the text
import cv2
import matplotlib.pyplot as plt
import glob
import os
def remove_dots(image_path,outdir):
image = cv2.imread(image_path)
mask = np.zeros(image.shape, dtype=np.uint8)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (3,3), 0)
thresh = cv2.adaptiveThreshold(blur,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV,51,9)
# Create horizontal kernel then dilate to connect text contours
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))
dilate = cv2.dilate(thresh, kernel, iterations=2)
# Find contours and filter out noise using contour approximation and area filtering
cnts = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.04 * peri, True)
x,y,w,h = cv2.boundingRect(c)
area = w * h
ar = w / float(h)
if area > 1200 and area < 50000 and ar <8:
cv2.drawContours(mask, [c], -1, (255,255,255), -1)
# Bitwise-and input image and mask to get result
mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
result = cv2.bitwise_and(image, image, mask=mask)
result[mask==0] = (255,255,255) # Color background white
cv2.imwrite(os.path.join(outdir,os.path.basename(image_path)),result)
for jpgfile in glob.glob(r'C:\custom\TableDetectionWork\text_detection_dataset/*'):
print(jpgfile)
remove_dots(jpgfile,r'C:\custom\TableDetectionWork\textdetect/')
You can do that by using a horizontal morphology filter to merge the letters in a mask image. Then find the contours. Then get the bounding boxes.
Input:
import cv2
import numpy as np
img = cv2.imread("john.jpg")
# convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# threshold
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1]
# invert
thresh = 255 - thresh
# apply horizontal morphology close
kernel = np.ones((5 ,191), np.uint8)
morph = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
# get external contours
contours = cv2.findContours(morph, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
# draw contours
result = img.copy()
for cntr in contours:
# get bounding boxes
pad = 10
x,y,w,h = cv2.boundingRect(cntr)
cv2.rectangle(result, (x-pad, y-pad), (x+w+pad, y+h+pad), (0, 0, 255), 4)
# save result
cv2.imwrite("john_bbox.png",result)
# display result
cv2.imshow("thresh", thresh)
cv2.imshow("morph", morph)
cv2.imshow("result", result)
cv2.waitKey(0)
cv2.destroyAllWindows()
Morphology Closed Image:
Bounding Boxes Image:
Here is the core of your code modified to do what you want in Python/OpenCV. It is just adding my code to the end of your code.
Input:
import cv2
import numpy as np
image = cv2.imread("john.jpg")
mask = np.zeros(image.shape, dtype=np.uint8)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (3,3), 0)
thresh = cv2.adaptiveThreshold(blur,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV,51,9)
# Create horizontal kernel then dilate to connect text contours
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))
dilate = cv2.dilate(thresh, kernel, iterations=2)
# Find contours and filter out noise using contour approximation and area filtering
cnts = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.04 * peri, True)
x,y,w,h = cv2.boundingRect(c)
area = w * h
ar = w / float(h)
if area > 1200 and area < 50000 and ar <8:
cv2.drawContours(mask, [c], -1, (255,255,255), -1)
# Bitwise-and input image and mask to get result
mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
result = cv2.bitwise_and(image, image, mask=mask)
result[mask==0] = (255,255,255) # Color background white
# NEW CODE HERE TO END _____________________________________________________________
gray2 = cv2.cvtColor(result, cv2.COLOR_BGR2GRAY)
thresh2 = cv2.threshold(gray2, 128, 255, cv2.THRESH_BINARY)[1]
thresh2 = 255 - thresh2
kernel = np.ones((5 ,191), np.uint8)
close = cv2.morphologyEx(thresh2, cv2.MORPH_CLOSE, kernel)
# get external contours
contours = cv2.findContours(close, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
# draw contours
result2 = result.copy()
for cntr in contours:
# get bounding boxes
pad = 10
x,y,w,h = cv2.boundingRect(cntr)
cv2.rectangle(result2, (x-pad, y-pad), (x+w+pad, y+h+pad), (0, 0, 255), 4)
cv2.imwrite("john_bboxes.jpg", result2)
cv2.imshow("mask",mask)
cv2.imshow("thresh",thresh)
cv2.imshow("dilate",dilate)
cv2.imshow("result",result)
cv2.imshow("gray2",gray2)
cv2.imshow("thresh2",thresh2)
cv2.imshow("close",close)
cv2.imshow("result2",result2)
cv2.waitKey(0)
cv2.destroyAllWindows()
Bounding Boxes on Your Result:
I need to remove first row (headers in this example) from a tabular image, using simple numpy functions or some image filtering
I want to get this output:
here is the solution I've made to solve this issue.
import cv2
def crop (image):
'''
remove additional upper lines from tabular image
:param image: image to crop
:return: image cropped
'''
# Load image, convert to grayscale, Otsu's threshold
result = image.copy()
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
# Detect horizontal lines
horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (40,1))
detect_horizontal = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, horizontal_kernel, iterations=2)
cnts = cv2.findContours(detect_horizontal, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
x, y, w, h = cv2.boundingRect(cnts[-1])
height, width = image.shape[:2]
crop_img = image[y+h+2:height,0:width]
return crop_img
image = cv2.imread('k.png')
cropped_img = crop(image)
cv2.imshow("cropped", cropped_img)
cv2.imwrite("o.png", cropped_img)
cv2.waitKey(0)
Use function selectROI()
cv2.namedWindow('ROI')
r=cv2.selectROI('ROI', img,False,False)
# cut rectangle
imROI = img[int(r[1]):int(r[1]+r[3]), int(r[0]):int(r[0]+r[2])]
cv2.destroyWindow('ROI')
cv2.imshow("ROI", imROI)
cv2.waitKey(0)
cv2.destroyAllWindows()
I am trying to extract handwritten characters from field boxes
My desired output would be the character segments with the boxes removed. So far, I've tried defining contours and filtering by area but that hasn't yielded any good results.
# Reading image and binarization
im = cv2.imread('test.png')
char_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
char_bw = cv2.adaptiveThreshold(char_gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 75, 10)
# Applying erosion and dilation
kernel = np.ones((5,5), np.uint8)
img_erosion = cv2.erode(char_bw, kernel, iterations=1)
img_dilation = cv2.dilate(img_erosion, kernel, iterations=1)
# Find Canny edges
edged = cv2.Canny(img_dilation, 100, 200)
# Finding Contours
edged_copy = edged.copy()
im2, cnts, hierarchy = cv2.findContours(edged_copy, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
print("Number of Contours found = " + str(len(cnts)))
# Draw all contours
cv2.drawContours(im, cnts, -1, (0, 255, 0), 3)
# Filter using area and save
for no, c in enumerate(cnts):
area = cv2.contourArea(c)
if area > 100:
contour = c
(x, y, w, h) = cv2.boundingRect(contour)
img = im[y:y+h, x:x+w]
cv2.imwrite(f'./cnts/cnt-{no}.png', img_dilation)
Here's a simple approach:
Obtain binary image. We load the image, enlarge using imutils.resize(), convert to grayscale, and perform Otsu's thresholding to obtain a binary image
Remove horizontal lines. We create a horizontal kernel then perform morphological opening and remove the horizontal lines using cv2.drawContours
Remove vertical lines. We create a vertical kernel then perform morphological opening and remove the vertical lines using cv2.drawContours
Here's a visualization of each step:
Binary image
Detected lines/boxes to remove highlighted in green
Result
Code
import cv2
import numpy as np
import imutils
# Load image, enlarge, convert to grayscale, Otsu's threshold
image = cv2.imread('1.png')
image = imutils.resize(image, width=500)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
# Remove horizontal
horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (25,1))
detect_horizontal = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, horizontal_kernel, iterations=2)
cnts = cv2.findContours(detect_horizontal, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
cv2.drawContours(image, [c], -1, (255,255,255), 5)
# Remove vertical
vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1,25))
detect_vertical = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, vertical_kernel, iterations=2)
cnts = cv2.findContours(detect_vertical, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
cv2.drawContours(image, [c], -1, (255,255,255), 5)
cv2.imshow('thresh', thresh)
cv2.imshow('image', image)
cv2.waitKey()