scikit-image

My image is here: i'm looking for a better solution or algorithm to detect the ellipse part (dish) in this photo and mask it in an other photo in Opencv. could you please give me some advice or solution. and my code is : circles = cv2.HoughCircles(img, cv2.HOUGH_GRADIENT, 1.2, 1, param1=128, minRadius=200, maxRadius=600) # draw detected circles on image circles = circles.tolist() for cir in circles: for x, y, r in cir: x, y, r = int(x), int(y), int(r) cv2.circle(img, (x, y), r, (0, 255, 0), 4) # show the output image cv2.imshow("output", cv2.resize(img, (500, 500))) There is an alternative for

I'm using HoG features for object detection via classification. I'm confused about how to deal with HoG feature vectors of different lengths. I've trained my classifier using training images that all have the same size. Now, I'm extracting regions from my image on which to run the classifier - say, using the sliding windows approach. Some of the windows that I extract are a lot bigger than the size of images the classifier was trained on. (It was trained on the smallest possible size of the object that might be expected in test images). The problem is, when the windows I need to classify are

I have started learning Tensorflow. I am using Pycharm and my environment is Ubuntu 16.04. I am following the tutorial . I cross check the nump. It is up-to-date. I don't know the reason of this error. from numpy.lib.arraypad import _validate_lengths ImportError: cannot import name '_validate_lengths' Need hint to resolve this error. Thank you. import tensorflow as tf from skimage import transform from skimage import data import matplotlib.pyplot as plt import os import numpy as np from skimage.color import rgb2gray import random #listdir: This method returns a list containing the names of the

I am getting a Zero Division Error with some images (Even though a lot of them work just fine) : Here's the code : image = skimage.io.imread('test.png', False) image_gray = skimage.io.imread('test.png', True) blurred = cv2.GaussianBlur(img_as_ubyte(image_gray), (5, 5), 0) thresh = threshold_li(blurred) binary = blurred > thresh binary_cv2 = img_as_ubyte(binary) # find contours in the thresholded image cnts = cv2.findContours(binary_cv2.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) cnts = cnts[0] if imutils.is_cv2() else cnts[1] # loop over the contours for c in cnts: # compute the center