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I want to remove the black noise at the border of the image

I am looking for a way to remove the black dots around the image border using OpenCV.
Image:
Expected solution:
import cv2
def get_img(img_name):
lower = (0, 0, 0)
upper = (75, 75, 75)
img = cv2.imread(img_name)
#print(img)
img_rgb_inrange = cv2.inRange(img, lower, upper)
neg_rgb_image = ~img_rgb_inrange
w = cv2.cvtColor(neg_rgb_image,cv2.COLOR_GRAY2RGB)
image3 = img-w
cv2.imwrite('img.png', image3)
get_img('address of the img')
I used the above code that I saw in link. The results are below:
output mask I got after running the code:
Final Output:
Wondering, is there any dynamic way (instead of initializing upper and lower bounds) where I can remove the noise from the image but still maintain my foreground and background?

import cv2
import matplotlib.pyplot as plt
image = cv2.imread('E3BbU.jpeg')
img_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(img_gray, 150, 255, cv2.THRESH_BINARY)
contours, hierarchy = cv2.findContours(image=thresh, mode=cv2.RETR_TREE, method=cv2.CHAIN_APPROX_NONE)
image2 = image.copy()
cv2.drawContours(image=image2, contours=sorted(contours, key=len)[:-1], contourIdx=-1, color=(255, 255, 255), thickness=2, lineType=cv2.LINE_AA)
fig, ax = plt.subplots(2, 1)
for i, img in enumerate([image, image2]):
ax[i].imshow(img);
ax[i].axes.get_xaxis().set_visible(False)
ax[i].axes.get_yaxis().set_visible(False)
plt.show()

changing hsv values of an image in opencv python isnt working

Im trying to set the Minimum and Maximum value of HSV of an Image in opencv python but after running the code all I can see is a blank rectangle box.
import cv2
import sys
import numpy as np
# Load in image
image = cv2.imread('power.jpg')
# Set minimum and max HSV values to display
lower = np.array([0, 209, 0])
upper = np.array([179, 255, 236])
# Create HSV Image and threshold into a range.
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv, lower, upper)
output = cv2.bitwise_and(image,image, mask= mask)
# Display output image
cv2.imshow('image',output)

I was able to solve it.
import numpy as np
import cv2
img = cv2.imread( "power.jpg" )
## convert to hsv
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
## mask of red (36,0,0) ~ (70, 255,255)
mask = cv2.inRange(hsv, (0, 209, 0), (179, 255,236))
bak = img.copy()
# Show only red
#bak[mask > 0] = (0, 0, 255)
imask = mask>0
green = np.zeros_like(img, np.uint8)
green[imask] = img[imask]
## save
cv2.imwrite("image.png", green)

I want to mask multiple Image horizontally

I have few Journal pages images where there are two columns I want to mask one column white without a changing the dimension.which means the output image should have same dimensions as input image even though there is one column.
I was able to mask image but the mask part is coming black which I want as white.
import cv2
import numpy as np
# Load the original image
image = cv2.imread(filename = "D:\output_final_word5\image1.jpg")
# Create the basic black image
mask = np.zeros(shape = image.shape, dtype = "uint8")
# Draw a white, filled rectangle on the mask image
cv2.rectangle(img = mask, pt1 = (0, 0), pt2 = (795, 3000), color = (255, 255,
255), thickness = -1)
# Apply the mask and display the result
maskedImg = cv2.bitwise_and(src1 = image, src2 = mask)
#cv2.namedWindow(winname = "masked image", flags = cv2.WINDOW_NORMAL)
cv2.imshow("masked image",maskedImg)
cv2.waitKey(delay = 0)
cv2.imwrite("D:\Test_Mask.jpg",maskedImg)
My final objective is to read a folder where are several Journal pages In which need to be saved by masking first one column and then another column without affecting the dimension of Input image and mask part should be white.
Below are Input Image Attached...
And Output Should be like this....

You don't need mask to draw rectangle. You can draw it directly on image.
You can also use image.copy() to create second image with other column
BTW: if 795 is in the middle of width then you can use image.shape to get its (height,width) and use width//2 instead of 795 so it will work with images which have different widths. But if 795 is not ideally in the middle then use half_width = 795
import cv2
image_1 = cv2.imread('image.jpg')
image_2 = image_1.copy()
height, width, depth = image_1.shape # it gives `height,width`, not `width,height`
half_width = width//2
#half_width = 795
cv2.rectangle(img=image_1, pt1=(0, 0), pt2=(half_width, height), color=(255, 255, 255), thickness=-1)
cv2.rectangle(img=image_2, pt1=(half_width, 0), pt2=(width, height), color=(255, 255, 255), thickness=-1)
cv2.imwrite("image_1.jpg", image_1)
cv2.imwrite("image_2.jpg", image_2)
cv2.imshow("image 1", image_1)
cv2.imshow("image 2", image_2)
cv2.waitKey(0)
cv2.destroyAllWindows()

Cropping Concave polygon from Image using Opencv python

How can I crop a concave polygon from an image. My Input image look like
.
and the coordinates of closed polygon are
[10,150],[150,100],[300,150],[350,100],[310,20],[35,10]. I want region bounded by concave polygon to be cropped using opencv. I searched for other similar questions but I did not able to find correct answer. That's why I am asking it ? Can you help me.
Any help would be highly appreciated.!!!

Steps
find region using the poly points
create mask using the poly points
do mask op to crop
add white bg if needed
The code:
# 2018.01.17 20:39:17 CST
# 2018.01.17 20:50:35 CST
import numpy as np
import cv2
img = cv2.imread("test.png")
pts = np.array([[10,150],[150,100],[300,150],[350,100],[310,20],[35,10]])
## (1) Crop the bounding rect
rect = cv2.boundingRect(pts)
x,y,w,h = rect
croped = img[y:y+h, x:x+w].copy()
## (2) make mask
pts = pts - pts.min(axis=0)
mask = np.zeros(croped.shape[:2], np.uint8)
cv2.drawContours(mask, [pts], -1, (255, 255, 255), -1, cv2.LINE_AA)
## (3) do bit-op
dst = cv2.bitwise_and(croped, croped, mask=mask)
## (4) add the white background
bg = np.ones_like(croped, np.uint8)*255
cv2.bitwise_not(bg,bg, mask=mask)
dst2 = bg+ dst
cv2.imwrite("croped.png", croped)
cv2.imwrite("mask.png", mask)
cv2.imwrite("dst.png", dst)
cv2.imwrite("dst2.png", dst2)
Source image:
Result:

You can do it in 3 steps:
Create a mask out of the image
mask = np.zeros((height, width))
points = np.array([[[10,150],[150,100],[300,150],[350,100],[310,20],[35,10]]])
cv2.fillPoly(mask, points, (255))
Apply mask to original image
res = cv2.bitwise_and(img,img,mask = mask)
Optionally you can remove the crop the image to have a smaller one
rect = cv2.boundingRect(points) # returns (x,y,w,h) of the rect
cropped = res[rect[1]: rect[1] + rect[3], rect[0]: rect[0] + rect[2]]
With this you should have at the end the image cropped
UPDATE
For the sake of completeness here is the complete code:
import numpy as np
import cv2
img = cv2.imread("test.png")
height = img.shape[0]
width = img.shape[1]
mask = np.zeros((height, width), dtype=np.uint8)
points = np.array([[[10,150],[150,100],[300,150],[350,100],[310,20],[35,10]]])
cv2.fillPoly(mask, points, (255))
res = cv2.bitwise_and(img,img,mask = mask)
rect = cv2.boundingRect(points) # returns (x,y,w,h) of the rect
cropped = res[rect[1]: rect[1] + rect[3], rect[0]: rect[0] + rect[2]]
cv2.imshow("cropped" , cropped )
cv2.imshow("same size" , res)
cv2.waitKey(0)
For the colored background version use the code like this:
import numpy as np
import cv2
img = cv2.imread("test.png")
height = img.shape[0]
width = img.shape[1]
mask = np.zeros((height, width), dtype=np.uint8)
points = np.array([[[10,150],[150,100],[300,150],[350,100],[310,20],[35,10]]])
cv2.fillPoly(mask, points, (255))
res = cv2.bitwise_and(img,img,mask = mask)
rect = cv2.boundingRect(points) # returns (x,y,w,h) of the rect
im2 = np.full((res.shape[0], res.shape[1], 3), (0, 255, 0), dtype=np.uint8 ) # you can also use other colors or simply load another image of the same size
maskInv = cv2.bitwise_not(mask)
colorCrop = cv2.bitwise_or(im2,im2,mask = maskInv)
finalIm = res + colorCrop
cropped = finalIm[rect[1]: rect[1] + rect[3], rect[0]: rect[0] + rect[2]]
cv2.imshow("cropped" , cropped )
cv2.imshow("same size" , res)
cv2.waitKey(0)

For the blured image background version use the code like this:
img = cv2.imread(img_path)
box = <box points>
# -- background
blur_bg = cv2.blur(img, (h, w))
mask1 = np.zeros((h, w, 3), np.uint8)
mask2 = np.ones((h, w, 3), np.uint8) * 255
cv2.fillPoly(mask1, box, (255, 255, 255))
# -- indexing
img_idx = np.where(mask1 == mask2)
bg_idx = np.where(mask1 != mask2)
# -- fill box
res = np.zeros((h, w, 3), np.int64)
res[img_idx] = img[img_idx]
res[bg_idx] = blur_bg[bg_idx]
res = res[y1:y2, x1:x2, :]

Create transparent image in opencv python

I am trying to make a transparent image and draw on it, and after I will addWeighted over the base image.
How can I initialize fully transparent image with width and hight in openCV python?
EDIT: I want to make a effect like in Photoshop, having stack of the layers, all stacked layers are initially transparent and drawing is performed on fully transparent layer. On the end I will merge all layers to get final image

For creating a transparent image you need a 4 channel matrix, 3 of which would represent RGB colors and the 4th channel would represent Alpha channel, To create a transparent image, you can ignore the RGB values and directly set the alpha channel to be 0. In Python OpenCV uses numpy to manipulate matrices, so a transparent image can be created as
import numpy as np
import cv2
img_height, img_width = 300, 300
n_channels = 4
transparent_img = np.zeros((img_height, img_width, n_channels), dtype=np.uint8)
# Save the image for visualization
cv2.imwrite("./transparent_img.png", transparent_img)

If you want to draw on several "layers" and then stack the drawings together, then how about this:
import cv2
import numpy as np
#create 3 separate BGRA images as our "layers"
layer1 = np.zeros((500, 500, 4))
layer2 = np.zeros((500, 500, 4))
layer3 = np.zeros((500, 500, 4))
#draw a red circle on the first "layer",
#a green rectangle on the second "layer",
#a blue line on the third "layer"
red_color = (0, 0, 255, 255)
green_color = (0, 255, 0, 255)
blue_color = (255, 0, 0, 255)
cv2.circle(layer1, (255, 255), 100, red_color, 5)
cv2.rectangle(layer2, (175, 175), (335, 335), green_color, 5)
cv2.line(layer3, (170, 170), (340, 340), blue_color, 5)
res = layer1[:] #copy the first layer into the resulting image
#copy only the pixels we were drawing on from the 2nd and 3rd layers
#(if you don't do this, the black background will also be copied)
cnd = layer2[:, :, 3] > 0
res[cnd] = layer2[cnd]
cnd = layer3[:, :, 3] > 0
res[cnd] = layer3[cnd]
cv2.imwrite("out.png", res)

To convert an image's white parts to transparent:
import cv2
import numpy as np
img = cv2.imread("image.png", cv2.IMREAD_UNCHANGED)
img[np.where(np.all(img[..., :3] == 255, -1))] = 0
cv2.imwrite("transparent.png", img)