Why is my output just a blank white image? [Python PIL] - python-3.x

I am getting a blank and white image instead of the desired output, help?
from PIL import Image
def draw_nested_rectangles():
height = int(input('Please enter the overall height: '))
width = int(input('Please enter the overall width: '))
size = (width,height)
pic = Image.new('RGB',size,'white')
firstRec(width,height)
secondRec(width,height)
thirdRec(width,height)
fourthRec(width,height)
pic.show()
def firstRec(width,height):
size = (width,height)
pic = Image.new('RGB', size, 'white')
blueProgression = 0
for x in range(width,width):
color = (0,0,blueProgression)
for y in range(height,height):
pic.putpixel((x,y),color)
blueProgression += 5
def secondRec(width,height):
size = (width,height)
pic = Image.new('RGB', size, 'white')
greenProgression = 255
for x in range(int(width*0.15),int(width*0.85)):
color = (0,greenProgression,0)
for y in range(int(height*0.15),int(height*0.85)):
pic.putpixel((x,y),color)
greenProgression -= 5
def thirdRec(width,height):
size = (width,height)
pic = Image.new('RGB', size, 'white')
greenProgression = 255
for x in range(int(width*0.30),int(width*0.70)):
color = (255,0,0)
for y in range(int(height*0.30),int(height*0.70)):
pic.putpixel((x,y),color)
def fourthRec(width,height):
size = (width,height)
pic = Image.new('RGB', size, 'white')
greenProgression = 255
for x in range(int(width*0.45),int(width*0.55)):
color = (255,255,255)
for y in range(int(height*0.45),int(height*0.55)):
pic.putpixel((x,y),color)
I want my output to be four nested triangles the first one being 100% of the width and height (starting off black on the right side and gradually increasing to blue) and the second being nested in the first being 70% of the original width and height (starting off black on the left side and gradually increasing to green) and the third being nested in and being 40% the original (pure red) while the last is nested in and being 10% (pure white)

TRY:-
from PIL import Image
def draw_nested_rectangles():
global pic
height = int(input('Please enter the overall height: '))
width = int(input('Please enter the overall width: '))
size = (width,height)
pic = Image.new('RGB', size, 'white')
firstRec(width, height)
secondRec(width, height)
thirdRec(width, height)
fourthRec(width, height)
pic.show()
def firstRec(width,height):
global pic
size = (width,height)
blueProgression = 0
for x in range(width,width):
color = (0,0,blueProgression)
for y in range(height,height):
pic.putpixel((x,y),color)
blueProgression += 5
def secondRec(width,height):
global pic
size = (width,height)
greenProgression = 255
for x in range(int(width*0.15),int(width*0.85)):
color = (0,greenProgression,0)
for y in range(int(height*0.15),int(height*0.85)):
pic.putpixel((x,y), color)
greenProgression -= 5
def thirdRec(width,height):
global pic
size = (width,height)
greenProgression = 255
for x in range(int(width*0.30),int(width*0.70)):
color = (255,0,0)
for y in range(int(height*0.30),int(height*0.70)):
pic.putpixel((x,y),color)
def fourthRec(width,height):
global pic
size = (width,height)
greenProgression = 255
for x in range(int(width*0.45),int(width*0.55)):
color = (255,255,255)
for y in range(int(height*0.45),int(height*0.55)):
pic.putpixel((x,y),color)
pic = 0
draw_nested_rectangles()
REASON FOR UNDESIRABLE OUTPUT:-
The reason why you were getting a blank image, rather then the one you expected is because, you were creating a separate pic image object inside each function, due to which all the function were working on separate image objects rather then a single one.
FIX:-
In order to get the desired output, you have to define the pic variable as global, inside each function that want to use it, by using the syntax global pic
SAMPLE INPUT:-
Please enter the overall height: 500
Please enter the overall width: 500
SAMPLE OUTPUT:-

Related

How to crop a square image from normalized vertices

I'm using this code to identify tops and bottoms of photographs:
( as of now I only have it working for tops. one thing at a time ;) )
def get_file(path):
client = vision.ImageAnnotatorClient()
for images in os.listdir(path):
# # Loads the image into memory
with io.open(images, "rb") as image_file:
content = image_file.read()
image = types.Image(content=content)
objects = client.object_localization(image=image).localized_object_annotations
im = Image.open(images)
width, height = im.size
print("Number of objects found: {}".format(len(objects)))
for object_ in objects:
if object_.name == "Top":
print("Top")
l1 = object_.bounding_poly.normalized_vertices[0].x
l2 = object_.bounding_poly.normalized_vertices[0].y
l3 = object_.bounding_poly.normalized_vertices[2].x
l4 = object_.bounding_poly.normalized_vertices[3].y
left = l1 * width
top = l2 * height
right = l3 * width
bottom = l4 * height
im = im.crop((left, top, right, bottom))
im.save('new_test_cropped.tif', 'tiff')
im.show()
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="Script to automatically crop images based on google vision predictions of 'tops' and 'bottoms'")
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get_file(args.path)
The images are opened, clothing is identified, and then the images are cropped and saved to a new file. (granted as of now they are being overwritten within the loop, but I'll fix that later)
What I cant figure out, is how to make the crop a 1:1 ratio. I need to save them out as square-cropped to be put on our website.
I'll be honest, the normalized_vertices make no sense to me. Hence why I'm having trouble.
Starting image:
Output:
Desired Output:
"Normalized" means the coordinates are divided by the width or height of the image, so normalized coordinates [1, 0.5] would indicate all the way (1) across the image and halfway down (0.5).
For a 1:1 aspect ratio you want right - left to be equal to top - bottom. So you want to find out which dimension (width or height) you need to increase, and by how much.
height = abs(top - bottom)
width = abs(right - left)
extrawidth = max(0, height - width)
extraheight = max(0, width - height)
If height > width, we want to increase width but not height. Since height - width > 0, the correct value will go into extrawidth. But because width - height < 0, extraheight will be 0.
Now let's say we want to increase the dimensions of our image symmetrically around the original crop rectangle.
top -= extraheight // 2
bottom += extraheight // 2
left -= extrawidth // 2
right += extrawidth // 2
And finally, do the crop:
im = im.crop((left, top, right, bottom))
For your image, let's say you get left = 93, right = 215, top = 49, and bottom = 205
Before:
After:

Mapping pixel value to temperature value on a thermal image

I have a thermal image (with a color bar) from an IR camera. My goal is to get the temperature of any point by clicking on it.
I have already written a script that retrieves the RBG values of any pixel by right-clicking on it.
I figure that using the max and min temperatures of the color bar, I can map pixel values to temperature values.
Is this possible or is there a better way to approach this?
Thank you very much.
from PIL import Image
import cv2
from win32api import GetSystemMetrics
counter = 0
max_value = input('Max Temp Value: ')
min_value = input('Min Temp Value: ')
def mouse_callback(event, x, y, flags, params): # Tracks the pixel the mouse it hovering on. When right click it prints the pixel location and its RBG values.
global counter
if event == 2:
counter += 1
r, g, b = rgb_img.getpixel((x, y))
print(f'{counter}: {[x, y]} value {r} {g} {b}')
else:
print([x, y], end='\t\r', flush=True)
path_image = 'colors.jpg'
img = cv2.imread(path_image)
im = Image.open(path_image)
rgb_img = im.convert('RGB')
width = GetSystemMetrics(0)
height = GetSystemMetrics(1)
scale_width = width / im.size[0]
scale_height = height / im.size[1]
scale = min(scale_width, scale_height)
window_width = int((im.size[0] * scale) * 0.5)
window_height = int((im.size[1] * scale) * 0.5)
cv2.namedWindow('image', cv2.WINDOW_NORMAL)
cv2.resizeWindow('image', window_width, window_height)
cv2.setMouseCallback('image', mouse_callback)
cv2.imshow('image', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

OpenCV: Segment each digit from the given image. Digits are written in each cell of a row matrix. Each cell is bounded by margins

I have been trying to recognise handwritten letters (digits/alphabet) from a form-document. As it is known that form-documents have 1d row cells, where the applicant has to fill their information within those bounded cells. However, I'm unable to segment the digits(currently my input consists only digits) from the bounding boxes.
I went through the following steps:
Reading the image (as a grayscale image) via "imread" method of opencv2. Initial Image size:19 x 209(in pixels).
pic = "crop/cropped000.jpg"
newImg = cv2.imread(pic, 0)
Resizing the image 200% its original size via "resize" method of opencv2. I used INTER_AREA Interpolation. Resized Image size: 38 x 418(in pixels)
h,w = newImg.shape
resizedImg = cv2.resize(newImg, (2*w,2*h), interpolation=cv2.INTER_AREA)
Applied Canny edge detection.
v = np.median(resizedImg)
sigma = 0.33
lower = int(max(0, (1.0 - sigma) * v))
upper = int(min(255, (1.0 + sigma) * v))
edgedImg = cv2.Canny(resizedImg, lower, upper)
Cropped the contours and saved them as images in 'BB' directory.
im2, contours, hierarchy = cv2.findContours(edgedImg.copy(),cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
num = 0
for c in contours:
x, y, w, h = cv2.boundingRect(c)
num += 1
new_img = resizedImg[y:y+h, x:x+w]
cv2.imwrite('BB/'+str(num).zfill(3) + '.jpg', new_img)
Entire code in summary:
pic = "crop/cropped000.jpg"
newImg = cv2.imread(pic, 0)
h,w = newImg.shape
print(newImg.shape)
resizedImg = cv2.resize(newImg, (2*w,2*h), interpolation=cv2.INTER_AREA)
print(resizedImg.shape)
v = np.median(resizedImg)
sigma = 0.33
lower = int(max(0, (1.0 - sigma) * v))
upper = int(min(255, (1.0 + sigma) * v))
edgedImg = cv2.Canny(resizedImg, lower, upper)
im2, contours, hierarchy = cv2.findContours(edgedImg.copy(),cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
num = 0
for c in contours:
x, y, w, h = cv2.boundingRect(c)
num += 1
new_img = resizedImg[y:y+h, x:x+w]
cv2.imwrite('BB/'+str(num).zfill(3) + '.jpg', new_img)
Images produced are posted here:
https://imgur.com/a/GStIcdj
I had to double the image size because Canny edge detection was producing double-edges for an object (However, it still does). I have also played with other openCV functionalities like Thresholding, Gaussian Blur, Dilate, Erode but all in vain.
# we need one more parameter for Date cell width : as this could be different for diff bank
def crop_image_data_from_date_field(image, new_start_h, new_end_h, new_start_w, new_end_w, cell_width):
#for date each cell has same height and width : here width: 25 px so cord will be changed based on width
cropped_image_list = []
starting_width = new_start_w
for i in range(1,9): # as date has only 8 fields: DD/MM/YYYY
cropped_img = image[new_start_h:new_end_h, new_start_w + 1 :new_start_w+22]
new_start_w = starting_width + (i*cell_width)
cropped_img = cv2.resize(cropped_img, (28, 28))
image_name = 'cropped_date/cropped_'+ str(i) + '.png'
cv2.imwrite(image_name, cropped_img)
cropped_image_list.append(image_name)
# print('cropped_image_list : ',cropped_image_list,len(cropped_image_list))
# rec_value = handwritten_digit_recog.recog_digits(cropped_image_list)
recvd_value = custom_predict.predict_digit(cropped_image_list)
# print('recvd val : ',recvd_value)
return recvd_value
you need to specify each cell width and it's x,y,w,h.
I think this will help you.

How to clear numbers from the image using openCV?

I'm trying to remove numbers which are laying inside the circular part of image, numbers are in black in color and background varies between red,yellow, blue and green.
I am using opencv to remove those numbers. I used a mask which extracts numbers from image, with help of cv2.inpaint tried to remove those numbers from images.
For my further analysis I required to have clear image. But my current approach gives distorted image and numbers are not completely removed.
I tried changing the threshold values, lowering will neglect numbers from dark shaded area such as from green and red.
import cv2
img = cv2.imread('scan_1.jpg')
mask = cv2.threshold(img,50,255,cv2.THRESH_BINARY_INV)[1][:,:,0]
cv2.imshow('mask', mask)
cv2.waitKey(0)
cv2.destroyAllWindows()
dst = cv2.inpaint(img, mask, 5, cv2.INPAINT_TELEA)
cv2.imshow('dst',dst)
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.imwrite('ost_1.jpg',dst)
Input images: a) scan_1.jpg
b) scan_2.jpg
Output images: a) ost_1.jpg
b) ost_2.jpg
Expected Image: Circles can ignored, but something similar to it is required.
Here is my attempt, a better/easier solution might be acquired if you do not care about preserving texts outside of your circle.
import cv2
import numpy as np
# connectivity method used for finding connected components, 4 vs 8
CONNECTIVITY = 4
# HSV threshold for finding black pixels
H_THRESHOLD = 179
S_THRESHOLD = 255
V_THRESHOLD = 150
# read image
img = cv2.imread("a1.jpg")
img_height = img.shape[0]
img_width = img.shape[1]
# save a copy for creating resulting image
result = img.copy()
# convert image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# found the circle in the image
circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1.7, minDist= 100, param1 = 48, param2 = 100, minRadius=70, maxRadius=100)
# draw found circle, for visual only
circle_output = img.copy()
# check if we found exactly 1 circle
num_circles = len(circles)
print("Number of found circles:{}".format(num_circles))
if (num_circles != 1):
print("invalid number of circles found ({}), should be 1".format(num_circles))
exit(0)
# save center position and radius of found circle
circle_x = 0
circle_y = 0
circle_radius = 0
if circles is not None:
# convert the (x, y) coordinates and radius of the circles to integers
circles = np.round(circles[0, :]).astype("int")
for (x, y, radius) in circles:
circle_x, circle_y, circle_radius = (x, y, radius)
cv2.circle(circle_output, (circle_x, circle_y), circle_radius, (255, 0, 0), 4)
print("circle center:({},{}), radius:{}".format(x,y,radius))
# keep a median filtered version of image, will be used later
median_filtered = cv2.medianBlur(img, 21)
# Convert BGR to HSV
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# define range of black color in HSV
lower_val = np.array([0,0,0])
upper_val = np.array([H_THRESHOLD,S_THRESHOLD,V_THRESHOLD])
# Threshold the HSV image to get only black colors
mask = cv2.inRange(hsv, lower_val, upper_val)
# find connected components
components = cv2.connectedComponentsWithStats(mask, CONNECTIVITY, cv2.CV_32S)
# apply median filtering to found components
#centers = components[3]
num_components = components[0]
print("Number of found connected components:{}".format(num_components))
labels = components[1]
stats = components[2]
for i in range(1, num_components):
left = stats[i, cv2.CC_STAT_LEFT] - 10
top = stats[i, cv2.CC_STAT_TOP] - 10
width = stats[i, cv2.CC_STAT_WIDTH] + 10
height = stats[i, cv2.CC_STAT_HEIGHT] + 10
# iterate each pixel and replace them if
#they are inside circle
for row in range(top, top+height+1):
for col in range(left, left+width+1):
dx = col - circle_x
dy = row - circle_y
if (dx*dx + dy*dy <= circle_radius * circle_radius):
result[row, col] = median_filtered[row, col]
# smooth the image, may be necessary?
#result = cv2.blur(result, (3,3))
# display image(s)
cv2.imshow("img", img)
cv2.imshow("gray", gray)
cv2.imshow("found circle:", circle_output)
cv2.imshow("mask", mask)
cv2.imshow("result", result)
cv2.waitKey(0)
cv2.destroyAllWindows()
Result for a1:

Resizing image and its bounding box

I have an image with bounding box in it, and I want to resize the image.
img = cv2.imread("img.jpg",3)
x_ = img.shape[0]
y_ = img.shape[1]
img = cv2.resize(img,(416,416));
Now I want to calculate the scale factor:
x_scale = ( 416 / x_)
y_scale = ( 416 / y_ )
And draw an image, this is the code for the original bounding box:
( 128, 25, 447, 375 ) = ( xmin,ymin,xmax,ymax)
x = int(np.round(128*x_scale))
y = int(np.round(25*y_scale))
xmax= int(np.round (447*(x_scale)))
ymax= int(np.round(375*y_scale))
However using this I get:
While the original is:
I don't see any flag in this logic, what's wrong?
Whole code:
imageToPredict = cv2.imread("img.jpg",3)
print(imageToPredict.shape)
x_ = imageToPredict.shape[0]
y_ = imageToPredict.shape[1]
x_scale = 416/x_
y_scale = 416/y_
print(x_scale,y_scale)
img = cv2.resize(imageToPredict,(416,416));
img = np.array(img);
x = int(np.round(128*x_scale))
y = int(np.round(25*y_scale))
xmax= int(np.round (447*(x_scale)))
ymax= int(np.round(375*y_scale))
Box.drawBox([[1,0, x,y,xmax,ymax]],img)
and drawbox
def drawBox(boxes, image):
for i in range (0, len(boxes)):
cv2.rectangle(image,(boxes[i][2],boxes[i][3]),(boxes[i][4],boxes[i][5]),(0,0,120),3)
cv2.imshow("img",image)
cv2.waitKey(0)
cv2.destroyAllWindows()
The image and the data for the bounding box are loaded separately. I am drawing the bounding box inside the image. The image does not contain the box itself.
I believe there are two issues:
You should swap x_ and y_ because shape[0] is actually y-dimension and shape[1] is the x-dimension
You should use the same coordinates on the original and scaled image. On your original image the rectangle is (160, 35) - (555, 470) rather than (128,25) - (447,375) that you use in the code.
If I use the following code:
import cv2
import numpy as np
def drawBox(boxes, image):
for i in range(0, len(boxes)):
# changed color and width to make it visible
cv2.rectangle(image, (boxes[i][2], boxes[i][3]), (boxes[i][4], boxes[i][5]), (255, 0, 0), 1)
cv2.imshow("img", image)
cv2.waitKey(0)
cv2.destroyAllWindows()
def cvTest():
# imageToPredict = cv2.imread("img.jpg", 3)
imageToPredict = cv2.imread("49466033\\img.png ", 3)
print(imageToPredict.shape)
# Note: flipped comparing to your original code!
# x_ = imageToPredict.shape[0]
# y_ = imageToPredict.shape[1]
y_ = imageToPredict.shape[0]
x_ = imageToPredict.shape[1]
targetSize = 416
x_scale = targetSize / x_
y_scale = targetSize / y_
print(x_scale, y_scale)
img = cv2.resize(imageToPredict, (targetSize, targetSize));
print(img.shape)
img = np.array(img);
# original frame as named values
(origLeft, origTop, origRight, origBottom) = (160, 35, 555, 470)
x = int(np.round(origLeft * x_scale))
y = int(np.round(origTop * y_scale))
xmax = int(np.round(origRight * x_scale))
ymax = int(np.round(origBottom * y_scale))
# Box.drawBox([[1, 0, x, y, xmax, ymax]], img)
drawBox([[1, 0, x, y, xmax, ymax]], img)
cvTest()
and use your "original" image as "49466033\img.png",
I get the following image
And as you can see my thinner blue line lies exactly inside your original red line and it stays there whatever targetSize you chose (so the scaling actually works correctly).
Another way of doing this is to use CHITRA
image = Chitra(img_path, box, label)
# Chitra can rescale your bounding box automatically based on the new image size.
image.resize_image_with_bbox((224, 224))
print('rescaled bbox:', image.bounding_boxes)
plt.imshow(image.draw_boxes())
https://chitra.readthedocs.io/en/latest/
pip install chitra
I encountered an issue with bounding box coordinates in Angular when using TensorFlow.js and MobileNet-v2 for prediction. The coordinates were based on the resolution of the video frame.
but I was displaying the video on a canvas with a fixed height and width. I resolved the issue by dividing the coordinates by the ratio of the original video resolution to the resolution of the canvas.
const x = prediction.bbox[0] / (this.Owidth / 300);
const y = prediction.bbox[1] / (this.Oheight / 300);
const width = prediction.bbox[2] / (this.Owidth / 300);
const height = prediction.bbox[3] / (this.Oheight / 300);
// Draw the bounding box.
ctx.strokeStyle = '#99ff00';
ctx.lineWidth = 2;
ctx.strokeRect(x, y, width, height);
this.Owidth & this.Oheight are original resolution of video. it is obtained by.
this.video.addEventListener(
'loadedmetadata',
(e: any) => {
this.Owidth = this.video.videoWidth;
this.Oheight = this.video.videoHeight;
console.log(this.Owidth, this.Oheight, ' pixels ');
},
false
);
300 X 300 is my static canvas width and height.
you can use the resize_dataset_pascalvoc
it's easy to use python3 main.py -p <IMAGES_&_XML_PATH> --output <IMAGES_&_XML> --new_x <NEW_X_SIZE> --new_y <NEW_X_SIZE> --save_box_images <FLAG>"
It resize all your dataset and rewrite new annotations files to resized images

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