Develop Reference

How can I find the center the hole with hough circles

For this image, I tried to use hough cirlce to find the center of the "black hole".
After playing with the parameters of cv2.HoughCircles for a long time, the following is the best I can get.
raw image:
# reproducible code for stackoverflow
import cv2
import os
import sys
from matplotlib import pyplot as plt
import numpy as np
# read image can turn it gray
img = cv2.imread(FILE)
cimg = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img_gray = dst = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
plt.figure(figsize = (18,18))
plt.imshow(cimg, cmap = "gray")
# removing noises
element = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
closing = cv2.morphologyEx(y, cv2.MORPH_CLOSE, element, iterations = 7)
plt.figure(figsize = (12,12))
plt.imshow(closing, cmap = "gray")
# try to find the circles
circles = cv2.HoughCircles(closing,cv2.HOUGH_GRADIENT,3,50,
param1=50,param2=30,minRadius=20,maxRadius=50)
circles = np.uint16(np.around(circles))
for i in circles[0,:]:
# draw the outer circle
cv2.circle(cimg,(i[0],i[1]),i[2],(0,255,0),2)
# draw the center of the circle
cv2.circle(cimg,(i[0],i[1]),2,(0,0,255),3)
plt.figure(figsize = (12,12))
plt.imshow(cimg)
Update::
The one with Canny:
edges = cv2.Canny(closing, 100, 300)
plt.figure(figsize = (12,12))
plt.imshow(edges, cmap = "gray")
circles = cv2.HoughCircles(edges,cv2.HOUGH_GRADIENT,2,50,
param1=50,param2=30,minRadius=20,maxRadius=60)
circles = np.uint16(np.around(circles))
cimg = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
for i in circles[0,:]:
# draw the outer circle
cv2.circle(cimg,(i[0],i[1]),i[2],(0,255,0),2)
# draw the center of the circle
cv2.circle(cimg,(i[0],i[1]),2,(0,0,255),3)
plt.figure(figsize = (12,12))
plt.imshow(cimg)
Still not the right circle that is wanted.
Update:
#crackanddie
Sometimes there is 6 or 9 in the identity number.
The circle in 6 or 9 is not very round.
Is there any way to filter that out?

This is an alternative method if you do not want to implement or fiddle with Hough's parameters. You must be sure there's at least one circle visible in your picture. The idea is to create a segmentation mask based on the CMYK color space and filter the blobs of interest by circularity and area. These are the steps:
Convert the image from BGR to CMYK
Threshold the K channel to get a binary mask
Filter blobs by circularity and area
Approximate the filtered blobs as circles
I'm choosing the CMYK color space because the circle is mostly black. The K (key) channel (in this case - black) should do a good job of representing the blob of interest, albeit, with some noise - as usual. Let's see the code:
# Imports:
import cv2
import numpy as np
# image path
path = "D://opencvImages//"
fileName = "dyj3O.jpg"
# load image
bgr = cv2.imread(path + fileName)
Alright, we need to convert the image from BGR to CMYK. OpenCV does not offer the conversion, so we need to do it manually. The formula is very straightforward. I'm just interested on the K channel, so I just calculate it like this:
# Make float and divide by 255:
bgrFloat = bgr.astype(np.float) / 255.
# Calculate K as (1 - whatever is biggest out of bgrFloat)
kChannel = 1 - np.max(bgrFloat, axis=2)
# Convert back to uint 8:
kChannel = 255 * kChannel
kChannel = kChannel.astype(np.uint8)
Gotta keep en eye on the data types, because there are float operations going on. This is the result:
As you see, the hole is almost 100% white, that's cool, we can threshold this image via Otsu like this:
# Compute binary mask of the hole via Otsu:
_, binaryImage = cv2.threshold(kChannel, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
Which gives you this nice binary mask:
Now, here comes the laborious part. Let's find contours on this image. For every contour/blob compute circularity and area. Use this info to filter noise and get the contour of interest, keep in mind that a perfect circle should have circularity close to 1.0. Once you get a contour of interest, approximate a circle to it. This is the process:
# Find the big contours/blobs on the filtered image:
contours, hierarchy = cv2.findContours(binaryImage, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
# Store the detected circles here:
detectedCircles = []
# Look for the potential contours of interest:
for _, c in enumerate(contours):
# Get the blob's area and perimeter:
contourArea = cv2.contourArea(c)
contourPerimeter = cv2.arcLength(c, True)
# Compute circularity:
if contourPerimeter > 0:
circularity = (4 * 3.1416 * contourArea) / (pow(contourPerimeter, 2))
else:
circularity = 0.0
# Set the min threshold values to identify the
# blob of interest:
minCircularity = 0.7
minArea = 2000
if circularity >= minCircularity and contourArea >= minArea:
# Approximate the contour to a circle:
(x, y), radius = cv2.minEnclosingCircle(c)
# Compute the center and radius:
center = (int(x), int(y))
# Cast radius to in:
radius = int(radius)
# Store the center and radius:
detectedCircles.append([center, radius])
# Draw the circles:
cv2.circle(bgr, center, radius, (0, 255, 0), 2)
cv2.imshow("Detected Circles", bgr)
print("Circles Found: " + str(len(detectedCircles)))
Additionally, I have stored the circle (center and radius) in the detectedCircles list. This is the final result:
Circles Found: 1

Here it is:
import numpy as np
import cv2
def threshold_gray_const(image_, rang: tuple):
return cv2.inRange(image_, rang[0], rang[1])
def binary_or(image_1, image_2):
return cv2.bitwise_or(image_1, image_2)
def negate_image(image_):
return cv2.bitwise_not(image_)
def particle_filter(image_, power):
# Abdrakov's particle filter
nb_components, output, stats, centroids = cv2.connectedComponentsWithStats(image_, connectivity=8)
sizes = stats[1:, -1]
nb_components = nb_components - 1
min_size = power
img2 = np.zeros(output.shape, dtype=np.uint8)
for i in range(0, nb_components):
if sizes[i] >= min_size:
img_to_compare = threshold_gray_const(output, (i + 1, i + 1))
img2 = binary_or(img2, img_to_compare)
img2 = img2.astype(np.uint8)
return img2
def reject_borders(image_):
# Abdrakov's border rejecter
out_image = image_.copy()
h, w = image_.shape[:2]
for row in range(h):
if out_image[row, 0] == 255:
cv2.floodFill(out_image, None, (0, row), 0)
if out_image[row, w - 1] == 255:
cv2.floodFill(out_image, None, (w - 1, row), 0)
for col in range(w):
if out_image[0, col] == 255:
cv2.floodFill(out_image, None, (col, 0), 0)
if out_image[h - 1, col] == 255:
cv2.floodFill(out_image, None, (col, h - 1), 0)
return out_image
src = cv2.imread("your_image")
img_gray = dst = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)
element = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
closing = cv2.morphologyEx(img_gray, cv2.MORPH_CLOSE, element, iterations=2)
tv, thresh = cv2.threshold(closing, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
neg = negate_image(thresh)
rej = reject_borders(neg)
filtered = particle_filter(rej, 300)
edges = cv2.Canny(filtered, 100, 200)
circles = cv2.HoughCircles(edges, cv2.HOUGH_GRADIENT, 3, 50, param1=50, param2=30, minRadius=20, maxRadius=50)
circles = np.uint16(np.around(circles))
for i in circles[0, :]:
# draw the outer circle
cv2.circle(src, (i[0], i[1]), i[2], (0, 255, 0), 2)
# draw the center of the circle
cv2.circle(src, (i[0], i[1]), 2, (0, 0, 255), 3)
cv2.imshow("closing", closing)
cv2.imshow("edges", edges)
cv2.imshow("out", src)
cv2.waitKey(0)
I changed cv2.morphologyEx parameters a bit, because they were too strong. And after this noise removing I made a binary image using cv2.THRESH_OTSU parameter, negated it, rejected borders and filtered a bit. Then I used cv2.Canny to find edges and this 'cannied' image I passed into cv2.HoughCircles. If any questions - ask me :)

If you want to use a "thinking out of the box" solution then check this solution out. Remember this might have a few false positives in some cases and would only work in cases where circle contour is complete or joined.
import numpy as np
import cv2
import matplotlib.pyplot as plt
from math import pi
pi_eps = 0.1
rgb = cv2.imread('/path/to/your/image/find_circle.jpg')
gray = cv2.cvtColor(rgb, cv2.COLOR_BGR2GRAY)
th = cv2.adaptiveThreshold(gray,255, cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY_INV,21,5)
contours, hier = cv2.findContours(th.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
out_img = rgb.copy()
for i in range(len(contours)):
x,y,w,h = cv2.boundingRect(contours[i])
ar = min(w,h)/max(w,h)
# For a circle aspect ratio is close to 1.0
# In your use case circle diameter is between 40px-100px
if ar < 0.9 or \
w < 40 or w > 100:
continue
# P = 2 * PI * r
perimeter = cv2.arcLength(contours[i], True)
if perimeter == 0:
continue
# Second level confirmation could be done using PI = P * P / (4 * A)
# A = PI * r * r
area = cv2.contourArea(contours[i])
if area == 0:
continue
# d = (w+h) / 2 average diameter
# A contour is a circle if (P / d) = PI
ctr_pi = perimeter / ((w+h) / 2)
if abs(ctr_pi - pi) < pi_eps * pi:
cv2.circle(out_img, (int(x+w/2), int(y+h/2)), int(max(w,h)/2), (0, 255, 0), 1)
print("Center of the circle: ", x + w/2, y+h/2)
plt.imshow(out_img)

How to center the content/object of a binary image in python?

I have a code that computes the orientation of a figure. Based on this orientation the figure is then rotated until it is straightened out. This all works fine. What I am struggling with, is getting the center of the rotated figure to the center of the whole image. So the center point of the figure should match the center point of the whole image.
Input image:
code:
import cv2
import numpy as np
import matplotlib.pyplot as plt
path = "inputImage.png"
image=cv2.imread(path)
gray=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
thresh=cv2.threshold(gray,0,255,cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
contours,hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
cnt1 = contours[0]
cnt=cv2.convexHull(contours[0])
angle = cv2.minAreaRect(cnt)[-1]
print("Actual angle is:"+str(angle))
rect = cv2.minAreaRect(cnt)
p=np.array(rect[1])
if p[0] < p[1]:
print("Angle along the longer side:"+str(rect[-1] + 180))
act_angle=rect[-1]+180
else:
print("Angle along the longer side:"+str(rect[-1] + 90))
act_angle=rect[-1]+90
#act_angle gives the angle of the minAreaRect with the vertical
if act_angle < 90:
angle = (90 + angle)
print("angleless than -45")
# otherwise, just take the inverse of the angle to make
# it positive
else:
angle=act_angle-180
print("grter than 90")
# rotate the image to deskew it
(h, w) = image.shape[:2]
print(h,w)
center = (w // 2, h // 2)
print(center)
M = cv2.getRotationMatrix2D(center, angle, 1.0)
rotated = cv2.warpAffine(image, M, (w, h),flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
plt.imshow(rotated)
cv2.imwrite("rotated.png", rotated)
With output:
As you can see the white figure is slightly placed to left, I want it to be perfectly centered.
Does anyone know how this can be done?
EDIT: I have tried #joe's suggestion and subtracted the centroid coordinates, from the center of the image by dividing the width and height of the picture by 2. From this I got an offset, this had to be added to the array that describes the image. But I don't know how I add the offset to the array. How would this work with the x and y coordinates?
The code:
img = cv2.imread("inputImage")
gray_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret,thresh = cv2.threshold(gray_image,127,255,0)
height, width = gray_image.shape
print(img.shape)
wi=(width/2)
he=(height/2)
print(wi,he)
M = cv2.moments(thresh)
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
offsetX = (wi-cX)
offsetY = (he-cY)
print(offsetX,offsetY)
print(cX,cY)

Here is one way in Python/OpenCV.
Get the bounding box for the white region from the contours. Compute the offset for the recentered region. Use numpy slicing to copy that to the center of a black background the size of the input.
Input:
import cv2
import numpy as np
# read image as grayscale
img = cv2.imread('white_shape.png', cv2.COLOR_BGR2GRAY)
# get shape
hh, ww = img.shape
# get contours (presumably just one around the nonzero pixels)
contours = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
for cntr in contours:
x,y,w,h = cv2.boundingRect(cntr)
# recenter
startx = (ww - w)//2
starty = (hh - h)//2
result = np.zeros_like(img)
result[starty:starty+h,startx:startx+w] = img[y:y+h,x:x+w]
# view result
cv2.imshow("RESULT", result)
cv2.waitKey(0)
cv2.destroyAllWindows()
# save reentered image
cv2.imwrite('white_shape_centered.png',result)

One approach is to obtain the bounding box coordinates of the binary object then crop the ROI using Numpy slicing. From here we calculate the new shifted coordinates then paste the ROI onto a new blank mask.
Code
import cv2
import numpy as np
# Load image as grayscale and obtain bounding box coordinates
image = cv2.imread('1.png', 0)
height, width = image.shape
x,y,w,h = cv2.boundingRect(image)
# Create new blank image and shift ROI to new coordinates
mask = np.zeros(image.shape, dtype=np.uint8)
ROI = image[y:y+h, x:x+w]
x = width//2 - ROI.shape[0]//2
y = height//2 - ROI.shape[1]//2
mask[y:y+h, x:x+w] = ROI
cv2.imshow('ROI', ROI)
cv2.imshow('mask', mask)
cv2.waitKey()

#NawinNarain, from this point onwards where you found out the relative shifts w.r.t. centroid of the image, it is very straightforward - You want to make an Affine matrix with this translations and apply cv2.warpAffine() to your image. That's -it.
T = np.float32([[1, 0, shift_x], [0, 1, shift_y]])
We then use warpAffine() to transform the image using the matrix, T
centered_image = cv2.warpAffine(image, T, (orig_width, orig_height))
This will transform your image so that the centroid is at the center. Hope this helps. The complete center image function will look like this:
def center_image(image):
height, width = image.shape
print(img.shape)
wi=(width/2)
he=(height/2)
print(wi,he)
ret,thresh = cv2.threshold(image,95,255,0)
M = cv2.moments(thresh)
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
offsetX = (wi-cX)
offsetY = (he-cY)
T = np.float32([[1, 0, offsetX], [0, 1, offsetY]])
centered_image = cv2.warpAffine(image, T, (width, height))
return centered_image

How to find rotate and crop a section of text in openCV, python

I'm in a struggle with a project that takes an image of a pretty clear font from say a label for example reads the "text region" and outputs it as a string using OCR tesseract for instance.
Now I've made quite some progress with the thing as I added varios global filters to get to a quite clear result but I'm struggling with finding method of filtering just the text out of there and then you have to think about rotating it to be as horizontal as possible and then after that the easy part should be to crop it.
May I have any leads to how to do that not using traning data and over complicating the system sins I only use a rasdpberry pi to do the computing?
Thanks for helping here's what I've came up with so far:
Original Image(Captured from PiCamera):
Adaptive thresh after shadow removal:
[
Glocad tresh after shadow removal:
Here's the code:
# import the necessary packages
from PIL import Image
import pytesseract
import argparse
import cv2
import os
import picamera
import time
import numpy as np
#preprocess = "tresh"
#Remaining textcorping and rotating:
import math
import json
from collections import defaultdict
from scipy.ndimage.filters import rank_filter
def dilate(ary, N, iterations):
"""Dilate using an NxN '+' sign shape. ary is np.uint8."""
kernel = np.zeros((N,N), dtype=np.uint8)
kernel[(N-1)/2,:] = 1
dilated_image = cv2.dilate(ary / 255, kernel, iterations=iterations)
kernel = np.zeros((N,N), dtype=np.uint8)
kernel[:,(N-1)/2] = 1
dilated_image = cv2.dilate(dilated_image, kernel, iterations=iterations)
return dilated_image
def props_for_contours(contours, ary):
"""Calculate bounding box & the number of set pixels for each contour."""
c_info = []
for c in contours:
x,y,w,h = cv2.boundingRect(c)
c_im = np.zeros(ary.shape)
cv2.drawContours(c_im, [c], 0, 255, -1)
c_info.append({
'x1': x,
'y1': y,
'x2': x + w - 1,
'y2': y + h - 1,
'sum': np.sum(ary * (c_im > 0))/255
})
return c_info
def union_crops(crop1, crop2):
"""Union two (x1, y1, x2, y2) rects."""
x11, y11, x21, y21 = crop1
x12, y12, x22, y22 = crop2
return min(x11, x12), min(y11, y12), max(x21, x22), max(y21, y22)
def intersect_crops(crop1, crop2):
x11, y11, x21, y21 = crop1
x12, y12, x22, y22 = crop2
return max(x11, x12), max(y11, y12), min(x21, x22), min(y21, y22)
def crop_area(crop):
x1, y1, x2, y2 = crop
return max(0, x2 - x1) * max(0, y2 - y1)
def find_border_components(contours, ary):
borders = []
area = ary.shape[0] * ary.shape[1]
for i, c in enumerate(contours):
x,y,w,h = cv2.boundingRect(c)
if w * h > 0.5 * area:
borders.append((i, x, y, x + w - 1, y + h - 1))
return borders
def angle_from_right(deg):
return min(deg % 90, 90 - (deg % 90))
def remove_border(contour, ary):
"""Remove everything outside a border contour."""
# Use a rotated rectangle (should be a good approximation of a border).
# If it's far from a right angle, it's probably two sides of a border and
# we should use the bounding box instead.
c_im = np.zeros(ary.shape)
r = cv2.minAreaRect(contour)
degs = r[2]
if angle_from_right(degs) <= 10.0:
box = cv2.cv.BoxPoints(r)
box = np.int0(box)
cv2.drawContours(c_im, [box], 0, 255, -1)
cv2.drawContours(c_im, [box], 0, 0, 4)
else:
x1, y1, x2, y2 = cv2.boundingRect(contour)
cv2.rectangle(c_im, (x1, y1), (x2, y2), 255, -1)
cv2.rectangle(c_im, (x1, y1), (x2, y2), 0, 4)
return np.minimum(c_im, ary)
def find_components(edges, max_components=16):
"""Dilate the image until there are just a few connected components.
Returns contours for these components."""
# Perform increasingly aggressive dilation until there are just a few
# connected components.
count = 21
dilation = 5
n = 1
while count > 16:
n += 1
dilated_image = dilate(edges, N=3, iterations=n)
contours, hierarchy = cv2.findContours(dilated_image, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
count = len(contours)
#print dilation
#Image.fromarray(edges).show()
#Image.fromarray(255 * dilated_image).show()
return contours
def find_optimal_components_subset(contours, edges):
"""Find a crop which strikes a good balance of coverage/compactness.
Returns an (x1, y1, x2, y2) tuple.
"""
c_info = props_for_contours(contours, edges)
c_info.sort(key=lambda x: -x['sum'])
total = np.sum(edges) / 255
area = edges.shape[0] * edges.shape[1]
c = c_info[0]
del c_info[0]
this_crop = c['x1'], c['y1'], c['x2'], c['y2']
crop = this_crop
covered_sum = c['sum']
while covered_sum < total:
changed = False
recall = 1.0 * covered_sum / total
prec = 1 - 1.0 * crop_area(crop) / area
f1 = 2 * (prec * recall / (prec + recall))
#print '----'
for i, c in enumerate(c_info):
this_crop = c['x1'], c['y1'], c['x2'], c['y2']
new_crop = union_crops(crop, this_crop)
new_sum = covered_sum + c['sum']
new_recall = 1.0 * new_sum / total
new_prec = 1 - 1.0 * crop_area(new_crop) / area
new_f1 = 2 * new_prec * new_recall / (new_prec + new_recall)
# Add this crop if it improves f1 score,
# _or_ it adds 25% of the remaining pixels for <15% crop expansion.
# ^^^ very ad-hoc! make this smoother
remaining_frac = c['sum'] / (total - covered_sum)
new_area_frac = 1.0 * crop_area(new_crop) / crop_area(crop) - 1
if new_f1 > f1 or (
remaining_frac > 0.25 and new_area_frac < 0.15):
print '%d %s -> %s / %s (%s), %s -> %s / %s (%s), %s -> %s' % (
i, covered_sum, new_sum, total, remaining_frac,
crop_area(crop), crop_area(new_crop), area, new_area_frac,
f1, new_f1)
crop = new_crop
covered_sum = new_sum
del c_info[i]
changed = True
break
if not changed:
break
return crop
def pad_crop(crop, contours, edges, border_contour, pad_px=15):
"""Slightly expand the crop to get full contours.
This will expand to include any contours it currently intersects, but will
not expand past a border.
"""
bx1, by1, bx2, by2 = 0, 0, edges.shape[0], edges.shape[1]
if border_contour is not None and len(border_contour) > 0:
c = props_for_contours([border_contour], edges)[0]
bx1, by1, bx2, by2 = c['x1'] + 5, c['y1'] + 5, c['x2'] - 5, c['y2'] - 5
def crop_in_border(crop):
x1, y1, x2, y2 = crop
x1 = max(x1 - pad_px, bx1)
y1 = max(y1 - pad_px, by1)
x2 = min(x2 + pad_px, bx2)
y2 = min(y2 + pad_px, by2)
return crop
crop = crop_in_border(crop)
c_info = props_for_contours(contours, edges)
changed = False
for c in c_info:
this_crop = c['x1'], c['y1'], c['x2'], c['y2']
this_area = crop_area(this_crop)
int_area = crop_area(intersect_crops(crop, this_crop))
new_crop = crop_in_border(union_crops(crop, this_crop))
if 0 < int_area < this_area and crop != new_crop:
print '%s -> %s' % (str(crop), str(new_crop))
changed = True
crop = new_crop
if changed:
return pad_crop(crop, contours, edges, border_contour, pad_px)
else:
return crop
def downscale_image(im, max_dim=2048):
"""Shrink im until its longest dimension is <= max_dim.
Returns new_image, scale (where scale <= 1).
"""
a, b = im.size
if max(a, b) <= max_dim:
return 1.0, im
scale = 1.0 * max_dim / max(a, b)
new_im = im.resize((int(a * scale), int(b * scale)), Image.ANTIALIAS)
return scale, new_im
def process_image(inputImg):
opnImg = Image.open(inputImg)
scale, im = downscale_image(opnImg)
edges = cv2.Canny(np.asarray(im), 100, 200)
# TODO: dilate image _before_ finding a border. This is crazy sensitive!
contours, hierarchy = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
borders = find_border_components(contours, edges)
borders.sort(key=lambda (i, x1, y1, x2, y2): (x2 - x1) * (y2 - y1))
border_contour = None
if len(borders):
border_contour = contours[borders[0][0]]
edges = remove_border(border_contour, edges)
edges = 255 * (edges > 0).astype(np.uint8)
# Remove ~1px borders using a rank filter.
maxed_rows = rank_filter(edges, -4, size=(1, 20))
maxed_cols = rank_filter(edges, -4, size=(20, 1))
debordered = np.minimum(np.minimum(edges, maxed_rows), maxed_cols)
edges = debordered
contours = find_components(edges)
if len(contours) == 0:
print '%s -> (no text!)' % path
return
crop = find_optimal_components_subset(contours, edges)
crop = pad_crop(crop, contours, edges, border_contour)
crop = [int(x / scale) for x in crop] # upscale to the original image size.
#draw = ImageDraw.Draw(im)
#c_info = props_for_contours(contours, edges)
#for c in c_info:
# this_crop = c['x1'], c['y1'], c['x2'], c['y2']
# draw.rectangle(this_crop, outline='blue')
#draw.rectangle(crop, outline='red')
#im.save(out_path)
#draw.text((50, 50), path, fill='red')
#orig_im.save(out_path)
#im.show()
text_im = opnImg.crop(crop)
text_im.save('Cropted_and_rotated_image.jpg')
return text_im
'''
text_im.save(out_path)
print '%s -> %s' % (path, out_path)
'''
#Camera capturing stuff:
myCamera = picamera.PiCamera()
myCamera.vflip = True
myCamera.hflip = True
'''
myCamera.start_preview()
time.sleep(6)
myCamera.stop_preview()
'''
myCamera.capture("Captured_Image.png")
#End capturing persidure
imgAddr = '/home/pi/My_examples/Mechanical_display_converter/Example1.jpg'
#imgAddr = "Captured_Image.png"
# construct the argument parse and parse the arguments
#ap = argparse.ArgumentParser()
'''
ap.add_argument("-i", "--image", required=True,
help="path to input image to be OCR'd")
ap.add_argument("-p", "--preprocess", type=str, default="thresh",
help="type of preprocessing to be done")
args = vars(ap.parse_args())
'''
# load the example image and convert it to grayscale
img = cv2.imread(imgAddr)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv2.imshow('Step1_gray_filter', gray)
'''
# check to see if we should apply thresholding to preprocess the
# image
if args["preprocess"] == "thresh":
gray = cv2.threshold(gray, 0, 255,
cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
# make a check to see if median blurring should be done to remove
# noise
elif args["preprocess"] == "blur":
gray = cv2.medianBlur(gray, 3)
if preprocess == "thresh":
gray = cv2.threshold(gray, 150, 255,
cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
# make a check to see if median blurring should be done to remove
# noise
elif preprocess == "blur":
gray = cv2.medianBlur(gray, 3)
'''
rgb_planes = cv2.split(img)
result_planes = []
result_norm_planes = []
for plane in rgb_planes:
dilated_img = cv2.dilate(plane, np.ones((7,7), np.uint8))
bg_img = cv2.medianBlur(dilated_img, 21)
diff_img = 255 - cv2.absdiff(plane, bg_img)
norm_img = cv2.normalize(diff_img, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8UC1)
result_planes.append(diff_img)
result_norm_planes.append(norm_img)
result = cv2.merge(result_planes)
result_norm = cv2.merge(result_norm_planes)
cv2.imshow('shadows_out.png', result)
cv2.imshow('shadows_out_norm.png', result_norm)
grayUnShadowedImg = cv2.cvtColor(result, cv2.COLOR_BGR2GRAY)
cv2.imshow('Shadow_Gray_CVT', grayUnShadowedImg)
ret, threshUnShadowedImg = cv2.threshold(grayUnShadowedImg, 200, 255, cv2.THRESH_BINARY)
cv2.imshow('unShadowed_Thresh_filtering', threshUnShadowedImg)
#v2.imwrite('unShadowed_Thresh_filtering.jpg', threshUnShadowedImg)
#croptedunShadowedImg = process_image('unShadowed_Thresh_filtering.jpg')
adptThreshUnShadowedImg = cv2.adaptiveThreshold(grayUnShadowedImg, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 115, 1)
cv2.imshow('unShadowed_Adaptive_Thresh_filtering', adptThreshUnShadowedImg)
'''
blurFImg = cv2.GaussianBlur(adptThreshUnShadowedImg,(25,25), 0)
ret, f3Img = cv2.threshold(blurFImg,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
cv2.imshow('f3Img', f3Img )
'''
#OCR Stage:
'''
# write the grayscale image to disk as a temporary file so we can
# apply OCR to it
filename = "{}.png".format(os.getpid())
cv2.imwrite(filename, threshImg)
# load the image as a PIL/Pillow image, apply OCR, and then delete
# the temporary file
text = pytesseract.image_to_string(Image.open(filename))
os.remove(filename)
print("\n" + text)
'''
cv2.waitKey(0)
cv2.destroyAllWindows()
Tryed this source out as well but this doesn't seem to work and is not that clear to understand:
https://www.danvk.org/2015/01/07/finding-blocks-of-text-in-an-image-using-python-opencv-and-numpy.html

I have made an example to maybe give you an idea on how to proceede. I made it without your transformations of the image but you could do it with them if you would like.
What I did was to first transform the image to binary with cv2.THRESH_BINARY. Next I made a mask and drew the contours by limiting them with size (cv2.contourArea()) and ratio (got it from cv2.boundingRect()) for threshold. Then I conected all the contours that are near each other using cv2.morphologyEx() and a big kernel size (50x50).
Then I selected the biggest contour (text) and drew a rotated rectangle with cv2.minAreaRect() which got me the rotational angle.
Then I could rotate the image using cv2.getRotationMatrix2D() and cv2.warpAffine() and get a slightly bigger bounding box using the highest X, Y and lowest X,Y values of the rotated rectangle which I used to crop the image.
Then I serched again for contours and removed the noise (little contours) from the image and the result is a text with high contrast.
Final result:
This code is meant only to give an idea or another point of view to the problem and it may not work with other images (if they differ from the original too much) or at least you would have to adjust some parameters of code. Hope it helps. Cheers!
Code:
import cv2
import numpy as np
# Read image and search for contours.
img = cv2.imread('rotatec.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
_, threshold = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY)
contours, hierarchy = cv2.findContours(threshold,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
# Create first mask used for rotation.
mask = np.ones(img.shape, np.uint8)*255
# Draw contours on the mask with size and ratio of borders for threshold.
for cnt in contours:
size = cv2.contourArea(cnt)
x,y,w,h = cv2.boundingRect(cnt)
if 10000 > size > 500 and w*2.5 > h:
cv2.drawContours(mask, [cnt], -1, (0,0,0), -1)
# Connect neighbour contours and select the biggest one (text).
kernel = np.ones((50,50),np.uint8)
opening = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
gray_op = cv2.cvtColor(opening, cv2.COLOR_BGR2GRAY)
_, threshold_op = cv2.threshold(gray_op, 150, 255, cv2.THRESH_BINARY_INV)
contours_op, hierarchy_op = cv2.findContours(threshold_op, cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
cnt = max(contours_op, key=cv2.contourArea)
# Create rotated rectangle to get the angle of rotation and the 4 points of the rectangle.
_, _, angle = rect = cv2.minAreaRect(cnt)
(h,w) = img.shape[:2]
(center) = (w//2,h//2)
# Rotate the image.
M = cv2.getRotationMatrix2D(center, angle, 1.0)
rotated = cv2.warpAffine(img, M, (int(w),int(h)), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_CONSTANT)
# Create bounding box for rotated text (use old points of rotated rectangle).
box = cv2.boxPoints(rect)
a, b, c, d = box = np.int0(box)
bound =[]
bound.append(a)
bound.append(b)
bound.append(c)
bound.append(d)
bound = np.array(bound)
(x1, y1) = (bound[:,0].min(), bound[:,1].min())
(x2, y2) = (bound[:,0].max(), bound[:,1].max())
cv2.drawContours(img,[box],0,(0,0,255),2)
# Crop the image and create new mask for the final image.
rotated = rotated[y1:y2, x1:x2]
mask_final = np.ones(rotated.shape, np.uint8)*255
# Remove noise from the final image.
gray_r = cv2.cvtColor(rotated, cv2.COLOR_BGR2GRAY)
_, threshold_r = cv2.threshold(gray_r, 150, 255, cv2.THRESH_BINARY_INV)
contours, hierarchy = cv2.findContours(threshold_r,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
for cnt in contours:
size = cv2.contourArea(cnt)
if size < 500:
cv2.drawContours(threshold_r, [cnt], -1, (0,0,0), -1)
# Invert black and white.
final_image = cv2.bitwise_not(threshold_r)
# Display results.
cv2.imshow('final', final_image)
cv2.imshow('rotated', rotated)
EDIT:
For text recognition I recomend you see this post from SO Simple Digit Recognition OCR in OpenCV-Python.
The result with the code from mentioned post:
EDIT:
This is my code implemented with the slightly modified code from the mentioned post. All steps are written in the comments. You should save the script and the training image to the same directory. This is my training image:
Code:
import cv2
import numpy as np
# Read image and search for contours.
img = cv2.imread('rotatec.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
_, threshold = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY)
contours, hierarchy = cv2.findContours(threshold,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
# Create first mask used for rotation.
mask = np.ones(img.shape, np.uint8)*255
# Draw contours on the mask with size and ratio of borders for threshold.
for cnt in contours:
size = cv2.contourArea(cnt)
x,y,w,h = cv2.boundingRect(cnt)
if 10000 > size > 500 and w*2.5 > h:
cv2.drawContours(mask, [cnt], -1, (0,0,0), -1)
# Connect neighbour contours and select the biggest one (text).
kernel = np.ones((50,50),np.uint8)
opening = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
gray_op = cv2.cvtColor(opening, cv2.COLOR_BGR2GRAY)
_, threshold_op = cv2.threshold(gray_op, 150, 255, cv2.THRESH_BINARY_INV)
contours_op, hierarchy_op = cv2.findContours(threshold_op, cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
cnt = max(contours_op, key=cv2.contourArea)
# Create rotated rectangle to get the angle of rotation and the 4 points of the rectangle.
_, _, angle = rect = cv2.minAreaRect(cnt)
(h,w) = img.shape[:2]
(center) = (w//2,h//2)
# Rotate the image.
M = cv2.getRotationMatrix2D(center, angle, 1.0)
rotated = cv2.warpAffine(img, M, (int(w),int(h)), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_CONSTANT)
# Create bounding box for rotated text (use old points of rotated rectangle).
box = cv2.boxPoints(rect)
a, b, c, d = box = np.int0(box)
bound =[]
bound.append(a)
bound.append(b)
bound.append(c)
bound.append(d)
bound = np.array(bound)
(x1, y1) = (bound[:,0].min(), bound[:,1].min())
(x2, y2) = (bound[:,0].max(), bound[:,1].max())
cv2.drawContours(img,[box],0,(0,0,255),2)
# Crop the image and create new mask for the final image.
rotated = rotated[y1:y2, x1-10:x2]
mask_final = np.ones(rotated.shape, np.uint8)*255
# Remove noise from the final image.
gray_r = cv2.cvtColor(rotated, cv2.COLOR_BGR2GRAY)
_, threshold_r = cv2.threshold(gray_r, 150, 255, cv2.THRESH_BINARY_INV)
contours, hierarchy = cv2.findContours(threshold_r,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
for cnt in contours:
size = cv2.contourArea(cnt)
if size < 500:
cv2.drawContours(threshold_r, [cnt], -1, (0,0,0), -1)
# Invert black and white.
final_image = cv2.bitwise_not(threshold_r)
# Display results.
cv2.imwrite('rotated12.png', final_image)
# Import module for finding path to database.
from pathlib import Path
# This code executes once amd writes two files.
# If file exists it skips this step, else it runs again.
file = Path("generalresponses.data")
if file.is_file() == False:
# Reading the training image
im = cv2.imread('pitrain1.png')
im3 = im.copy()
gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray,(5,5),0)
thresh = cv2.adaptiveThreshold(blur,255,1,1,11,2)
# Finding contour
_,contours,hierarchy = cv2.findContours(thresh,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
# Creates array and list for appending data
samples = np.empty((0,100))
responses = []
# Value serving to increment the "automatic" learning
i = 0
# Iterating through contours and appending the array and list with "learned" values
for cnt in contours:
i+=1
[x,y,w,h] = cv2.boundingRect(cnt)
cv2.rectangle(im,(x,y),(x+w,y+h),(0,0,255),2)
roi = thresh[y:y+h,x:x+w] # Croping ROI to bounding rectangle
roismall = cv2.resize(roi,(10,10)) # Resizing ROI to smaller image
cv2.imshow('norm',im)
# Appending values based on the pitrain1.png image
if i < 36:
responses.append(int(45))
elif 35 < i < 80:
responses.append(int(48))
elif 79 < i < 125:
responses.append(int(57))
elif 124 < i < 160:
responses.append(int(56))
elif 159 < i < 205:
responses.append(int(55))
elif 204 < i < 250:
responses.append(int(54))
elif 249 < i < 295:
responses.append(int(53))
elif 294 < i < 340:
responses.append(int(52))
elif 339 < i < 385:
responses.append(int(51))
elif 384 < i < 430:
responses.append(int(50))
elif 429 < i < 485:
responses.append(int(49))
else:
break
sample = roismall.reshape((1,100))
samples = np.append(samples,sample,0)
# Reshaping and saving database
responses = np.array(responses)
responses = responses.reshape((responses.size,1))
print('end')
np.savetxt('generalsamples.data',samples)
np.savetxt('generalresponses.data',responses, fmt='%s')
################### Recognition ########################
# Dictionary for numbers and characters (in this sample code the only
# character is " - ")
number = {
48 : "0",
53 : "5",
52 : "4",
50 : "2",
45 : "-",
55 : "7",
51 : "3",
57 : "9",
56 : "8",
54 : "6",
49 : "1"
}
####### training part ###############
samples = np.loadtxt('generalsamples.data',np.float32)
responses = np.loadtxt('generalresponses.data',np.float32)
responses = responses.reshape((responses.size,1))
model = cv2.ml.KNearest_create()
model.train(samples,cv2.ml.ROW_SAMPLE,responses)
############################# testing part #########################
im = cv2.imread('rotated12.png')
out = np.zeros(im.shape,np.uint8)
gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
thresh = cv2.adaptiveThreshold(gray,255,1,1,11,2)
contours,hierarchy = cv2.findContours(thresh,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
for cnt in contours:
[x,y,w,h] = cv2.boundingRect(cnt)
cv2.rectangle(im,(x,y),(x+w,y+h),(0,255,0),2)
roi = thresh[y:y+h,x:x+w]
roismall = cv2.resize(roi,(10,10))
roismall = roismall.reshape((1,100))
roismall = np.float32(roismall)
retval, results, neigh_resp, dists = model.findNearest(roismall,k=5)
string = int((results[0][0]))
string2 = number.get(string)
print(string2)
cv2.putText(out,str(string2),(x,y+h),0,1,(0,255,0))
cv2.imshow('im',im)
cv2.imshow('out',out)
cv2.waitKey(0)
cv2.destroyAllWindows()
Result:

Sorry for begin a complete moron in it,
I'm realy trying to learn as much as I can about coding,everything that goes around the computer and openCV with the very little time I have But here's the edited code I've managed to get partly working:
from PIL import Image
import pytesseract
import os
import picamera
import time
import cv2
import numpy as np
# Read image and search for contours.
img = cv2.imread('Example1.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
_, threshold = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY)
contours, hierarchy = cv2.findContours(threshold,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE) #EDITED
# Create first mask used for rotation.
mask = np.ones(img.shape, np.uint8)*255
# Draw contours on the mask with size and ratio of borders for threshold.
for cnt in contours:
size = cv2.contourArea(cnt)
x,y,w,h = cv2.boundingRect(cnt)
if 10000 > size > 500 and w*2.5 > h:
cv2.drawContours(mask, [cnt], -1, (0,0,0), -1)
# Connect neighbour contours and select the biggest one (text).
kernel = np.ones((50,50),np.uint8)
opening = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
gray_op = cv2.cvtColor(opening, cv2.COLOR_BGR2GRAY)
_, threshold_op = cv2.threshold(gray_op, 150, 255, cv2.THRESH_BINARY_INV)
contours_op, hierarchy_op = cv2.findContours(threshold_op, cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
cnt = max(contours_op, key=cv2.contourArea)
# Create rotated rectangle to get the angle of rotation and the 4 points of the rectangle.
_, _, angle = rect = cv2.minAreaRect(cnt)
(h,w) = img.shape[:2]
(center) = (w//2,h//2)
# Rotate the image.
M = cv2.getRotationMatrix2D(center, angle, 1.0)
rotated = cv2.warpAffine(img, M, (int(w),int(h)), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_CONSTANT)
# Create bounding box for rotated text (use old points of rotated rectangle).
box = cv2.cv.BoxPoints(rect) #edited
a, b, c, d = box = np.int0(box)
bound =[]
bound.append(a)
bound.append(b)
bound.append(c)
bound.append(d)
bound = np.array(bound)
(x1, y1) = (bound[:,0].min(), bound[:,1].min())
(x2, y2) = (bound[:,0].max(), bound[:,1].max())
cv2.drawContours(img,[box],0,(0,0,255),2)
# Crop the image and create new mask for the final image.
rotated = rotated[y1:y2, x1:x2]
mask_final = np.ones(rotated.shape, np.uint8)*255
# Remove noise from the final image.
gray_r = cv2.cvtColor(rotated, cv2.COLOR_BGR2GRAY)
_, threshold_r = cv2.threshold(gray_r, 150, 255, cv2.THRESH_BINARY_INV)
contours, hierarchy = cv2.findContours(threshold_r,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
for cnt in contours:
size = cv2.contourArea(cnt)
if size < 500:
cv2.drawContours(threshold_r, [cnt], -1, (0,0,0), -1)
# Invert black and white.
final_image = cv2.bitwise_not(threshold_r)
# Display results.
cv2.imshow('final', final_image)
cv2.imshow('rotated', rotated)
#OCR Stage:
# write the grayscale image to disk as a temporary file so we can
# apply OCR to it
filename = "{}.png".format(os.getpid())
cv2.imwrite('Final_proc.jpg', final_image)
# load the image as a PIL/Pillow image, apply OCR, and then delete
# the temporary file
text = pytesseract.image_to_string(Image.open('Final_proc.jpg'))
os.remove('Final_proc.jpg')
print("\n" + text)
cv2.waitKey(0)
cv2.destroyAllWindows()
When compiling it now it gives me this output:
[img]https://i.imgur.com/ImdKSCv.jpg[/img]
which is a little different from what you showed and compiled on the windows machine but still super close.
anyidea what happened? just after that this should be realy easy to dissect the code and learn it easily.
Again thank you very much for your time! :D

So for the python 3 and openCV 3 version of the code in order to make the img work with tesseract you'd need to add an around 20px white boarder to extend the image for somereason (I assume it's because the convolutional matrix scanning effort) according to my other post:
pytesseract struggling to recognize clean black and white pictures with font numbers and 7 seg digits(python)
and here's how you'd add the boarder:
how to add border around an image in opencv python
In one line of code:
outputImage = cv2.copyMakeBorder(
inputImage,
topBorderWidth,
bottomBorderWidth,
leftBorderWidth,
rightBorderWidth,
cv2.BORDER_CONSTANT,
value=color of border
)

Robustly crop rotated bounding box on photos

I'm trying to extract the rotated bounding box of contours robustly. I would like to take an image, find the largest contour, get its rotated bounding box, rotate the image to make the bounding box vertical, and crop to size.
For a demonstration, here is an original image linked in the following code. I would like to end up with that shoe rotated to vertical and cropped to size. The following code from this answer seems to work on simple images like opencv lines, etc., but not on photos.
Ends up with this, which is rotated and cropped wrong:
EDIT: After changing the threshold type to cv2.THRESH_BINARY_INV, it now is rotated correctly but cropped wrong:
import cv2
import matplotlib.pyplot as plt
import numpy as np
import urllib.request
plot = lambda x: plt.imshow(x, cmap='gray').figure
url = 'https://i.imgur.com/4E8ILuI.jpg'
img_path = 'shoe.jpg'
urllib.request.urlretrieve(url, img_path)
img = cv2.imread(img_path, 0)
plot(img)
threshold_value, thresholded_img = cv2.threshold(
img, 250, 255, cv2.THRESH_BINARY)
_, contours, _ = cv2.findContours(thresholded_img, 1, 1)
contours.sort(key=cv2.contourArea, reverse=True)
shoe_contour = contours[0][:, 0, :]
min_area_rect = cv2.minAreaRect(shoe_contour)
def crop_minAreaRect(img, rect):
# rotate img
angle = rect[2]
rows, cols = img.shape[0], img.shape[1]
M = cv2.getRotationMatrix2D((cols / 2, rows / 2), angle, 1)
img_rot = cv2.warpAffine(img, M, (cols, rows))
# rotate bounding box
rect0 = (rect[0], rect[1], 0.0)
box = cv2.boxPoints(rect)
pts = np.int0(cv2.transform(np.array([box]), M))[0]
pts[pts < 0] = 0
# crop
img_crop = img_rot[pts[1][1]:pts[0][1],
pts[1][0]:pts[2][0]]
return img_crop
cropped = crop_minAreaRect(thresholded_img, min_area_rect)
plot(cropped)
How can I get the correct cropping?

After some research, this is what I get:
This is how I get it:
pad the original image on each side (500 pixels in my case)
find the four corner points of the shoe (the four points should form a polygon enclosing the shoe, but do not need to be exact rectangle)
employing the code here to crop the shoe:
img = cv2.imread("padded_shoe.jpg")
# four corner points for padded shoe
cnt = np.array([
[[313, 794]],
[[727, 384]],
[[1604, 1022]],
[[1304, 1444]]
])
print("shape of cnt: {}".format(cnt.shape))
rect = cv2.minAreaRect(cnt)
print("rect: {}".format(rect))
box = cv2.boxPoints(rect)
box = np.int0(box)
width = int(rect[1][0])
height = int(rect[1][1])
src_pts = box.astype("float32")
dst_pts = np.array([[0, height-1],
[0, 0],
[width-1, 0],
[width-1, height-1]], dtype="float32")
M = cv2.getPerspectiveTransform(src_pts, dst_pts)
warped = cv2.warpPerspective(img, M, (width, height))
Cheers, hope it helps.

How to find the center and angle of objects in an image?

I am using python and OpenCV. I am trying to find the center and angle of the batteries:
Image of batteries with random angles:
The code than I have is this:
import cv2
import numpy as np
img = cv2.imread('image/baterias2.png')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
img2 = cv2.imread('image/baterias4.png',0)
minLineLength = 300
maxLineGap = 5
edges = cv2.Canny(img2,50,200)
cv2.imshow('Canny',edges)
lines = cv2.HoughLinesP(edges,1,np.pi/180,80,minLineLength,maxLineGap)
print lines
salida = np.zeros((img.shape[0],img.shape[1]))
for x in range(0, len(lines)):
for x1,y1,x2,y2 in lines[x]:
cv2.line(salida,(x1,y1),(x2,y2),(125,125,125),0)# rgb
cv2.imshow('final',salida)
cv2.imwrite('result/hough.jpg',img)
cv2.waitKey(0)
Any ideas to work it out?

Almost identical to one of my other answers. PCA seems to work fine.
import cv2
import numpy as np
img = cv2.imread("test_images/battery001.png") #load an image of a single battery
img_gs = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #convert to grayscale
#inverted binary threshold: 1 for the battery, 0 for the background
_, thresh = cv2.threshold(img_gs, 250, 1, cv2.THRESH_BINARY_INV)
#From a matrix of pixels to a matrix of coordinates of non-black points.
#(note: mind the col/row order, pixels are accessed as [row, col]
#but when we draw, it's (x, y), so have to swap here or there)
mat = np.argwhere(thresh != 0)
#let's swap here... (e. g. [[row, col], ...] to [[col, row], ...])
mat[:, [0, 1]] = mat[:, [1, 0]]
#or we could've swapped at the end, when drawing
#(e. g. center[0], center[1] = center[1], center[0], same for endpoint1 and endpoint2),
#probably better performance-wise
mat = np.array(mat).astype(np.float32) #have to convert type for PCA
#mean (e. g. the geometrical center)
#and eigenvectors (e. g. directions of principal components)
m, e = cv2.PCACompute(mat, mean = np.array([]))
#now to draw: let's scale our primary axis by 100,
#and the secondary by 50
center = tuple(m[0])
endpoint1 = tuple(m[0] + e[0]*100)
endpoint2 = tuple(m[0] + e[1]*50)
red_color = (0, 0, 255)
cv2.circle(img, center, 5, red_color)
cv2.line(img, center, endpoint1, red_color)
cv2.line(img, center, endpoint2, red_color)
cv2.imwrite("out.png", img)

To find out the center of an object, you can use the Moments.
Threshold the image and get the contours of the object with findContours.
Compute the Moments withcv.Moments(arr, binary=0) → moments.
As arr you can pass the contours. Then the coordinates of the center are computed as x = m10/m00 and y = m01/m00.
To get the orientation, you can draw a minimum Rectangle around the object and compute the angle between the longer side of the rectangle and a vertical line.

You can reference the code.
import cv2
import imutils
import numpy as np
PIC_PATH = r"E:\temp\Battery.jpg"
image = cv2.imread(PIC_PATH)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
edged = cv2.Canny(gray, 100, 220)
kernel = np.ones((5,5),np.uint8)
closed = cv2.morphologyEx(edged, cv2.MORPH_CLOSE, kernel)
cnts = cv2.findContours(closed.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
cv2.drawContours(image, cnts, -1, (0, 255, 0), 4)
cv2.imshow("Output", image)
cv2.waitKey(0)
The result picture is,