I have these polygons that are either rectangles or "connected rectangles", meaning polygons that could be separated into rectangles, so it’s all "kinda orthogonal". They sometimes have "dog-ears" which need to be filled because they are masks that mustn’t lack those parts of the content once they’re applied to it. Black is what I have, ..
If the title isn’t clear let’s say I have a list of images (10k+), and I have a target image I am searching for. Here’s an example of the target image: Here’s an example of images I will want to be searching to find something ‘similar’ (ex1, ex2, and ex3): Here’s the matching I do ..
I doing some contour detection on a image and i want to find a contour based on a area that i will fix in this case i want the contour marked in red. So i want a bounding box around the red contour Following is the code for that: import cv2 import numpy as np ..
I have an image like this: after I applied some processings e.g. cv2.Canny(), it looks like this now: As you can see that the black lines become hollow. I have tried erosion and dilation, but if I do them many times, the 2 entrances will be closed(meaning become connected line or closed contour). How could ..
i have data set of 200 images, and i have calculated color related features and texture related features of those images. now i have color related features of shape (54,), and texture related features of shape (48,). I have to feed these values to a SVM. But i am new to python and machine learning ..
Intento extraer el texto del captcha esta es la imagen Aqui la imagen del captcha Este es el code que intente: image = cv2.resize(image, (300,120)) image = cv2.dilate(image, None, iterations=1) image = cv2.GaussianBlur(image,(1,9),0) image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) image = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU) image = cv2.medianBlur(image,5) cv2.imshow("Image", image) cv2.imwrite("im.jpg",image) text =pytesseract.image_to_string(image,config=’–psm 8 -c ..
I’m curretnly implementing the segmentation method proposed by Tsai for the trilevel case in an 8-bit image in Python 3.6. This is the code, considering the mathematical relations explained in the paper Annex: ### THRESHOLDS CALCULATION BEGIN ### # 0 Moment = 1 m0 = 1.0 # 1st Moment m1 = np.cumsum((histogram[:,0])*histogram[:,2])[-1] # 2nd Moment ..
I have a task of watermarking the image using python pillow. The function is supposed to take in two parameters: 1. Name of an image file and 2.Message – This is text that should be watermarked onto the image The function should save the watermarked image with the name of the original file with a ..
I am trying to make YOLO architecture with an input of gray scaled image. In general, input of YOLO is RGB image (3-dim tensor), whose size is (N, N, 3), where N is size of an image and 3 represents R,G and B channel. When reading an image with pillow, the code below gives me ..
I am currently taking an image processing course and am confused about applying the linear heat equation to smooth an image. I was told that the application of the heat equation on an image produces the same effect as convolving an image with the gaussian. I am being asked to apply the linear heat equation ..