Matplotlib contour plot does not extract exact contours-CodePudding

The

However, when I try to extract the contours using:

plt.contour(data, levels = np.unique(data))

As you can see the contour does not follow the sharp corner of the actual data. If I overlay both plots:

Here is the complete code:

import numpy as np import matplotlib.pyplot as plt data = np.array([[ 1.46184395, 1.46184395, 1.46184395, 1. , 1. ], [ 1.46184395, 1.46184395, 1.46184395, 1. , 1. ], [ 1.46184395, 1.46184395, 1.46184395, 1. , 1. ], [ 1.46184395, 1.46184395, 1.46184395, 1. , 1. ], [ 1.46184395, 1.46184395, 1.46184395, 4.24552277, 4.24552277], [ 1.46184395, 1.46184395, 1.46184395, 4.24552277, 4.24552277], [ 1.46184395, 1.46184395, 1.46184395, 4.24552277, 4.24552277], [ 1.46184395, 1.46184395, 1.46184395, 4.24552277, 4.24552277], [ 1.46184395, 1.46184395, 1.46184395, 4.24552277, 4.24552277], [ 1.46184395, 1.46184395, 1.46184395, 4.24552277, 4.24552277]]) plt.imshow(data) plt.show() plt.contour(data, levels=np.unique(data), cmap="jet") plt.colorbar()

CodePudding user response：

The contours are drawn using a

CodePudding user response：

Here is how I am solving it (there is still a slight offset between contour and color)

import numpy as np import matplotlib.pyplot as plt import matplotlib as mpl import cv2 def change_into_int(data): unique = np.unique(data) int_data = np.zeros_like(data) for i, unique_value in enumerate(unique): int_data[data == unique_value] = i 1 return int_data.astype(np.uint8) def find_contours(int_data): # For every "color" in the dataset find the contour # by eliminating all the other colors one after each other and finding the # individual contours contours = [] for color in np.unique(int_data): # Create temporary canvas, and # set all entries equal to 1 that have the same postion as the # original color mask = np.zeros_like(int_data) mask[int_data == color] = 1 # white object on black background c, hierarchy = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) contours.append(c) return contours data = np.array([ [ 1.46184395, 1.46184395, 1.46184395, 1. , 1. ], [ 1.46184395, 1.46184395, 1.46184395, 1. , 1. ], [ 1.46184395, 1.46184395, 1.46184395, 1. , 1. ], [ 1.46184395, 1.46184395, 1.46184395, 1. , 1. ], [ 1.46184395, 1.46184395, 1.46184395, 4.24552277, 4.24552277], [ 1.46184395, 1.46184395, 1.46184395, 4.24552277, 4.24552277], [ 1.46184395, 1.46184395, 1.46184395, 4.24552277, 4.24552277], [ 1.46184395, 1.46184395, 1.46184395, 4.24552277, 4.24552277], [ 1.46184395, 1.46184395, 1.46184395, 4.24552277, 4.24552277], [ 1.46184395, 1.46184395, 1.46184395, 4.24552277, 4.24552277]]) ## Main int_data = change_into_int(data) contours = find_contours(int_data) ## Draw canvas = np.zeros_like(int_data) for contour in contours: coords = np.reshape(contour[0].flatten(), (-1,2)) x = list(coords[:,0]) x.append(x[0]) y = list(coords[:,1]) y.append(y[0]) plt.plot(x,y) plt.imshow(data, origin="lower")