How to create heatmap over an image using coordinate points?

Question

I have a pathology image of size 1024 x 1360. I have probability values of some areas and coordinate points. How can I write a code for heatmap generation using the coordinate points and probability values over an image. The details of the probability values (.csv) file format are given below. Any help will be highly appreciated.

Edit:

CSV file format Download .csv file

Content of CSV file:

(x,y)        (x, y+y1)    (x+x1, y)    (x+x1, y+y1) Probability value
(0,0)        (0, 5)       (10, 0)      (10, 5)      0.5
(50,45)      (50, 65)     (55, 45)     (55, 65)     0.9
(100, 150)   (100, 200)   (120, 150)   (120, 200)   0.3
(1000, 1005) (1000, 1010) (1005, 1005) (1005, 1010) 1

Sample imageover which Heatmap should be generated [![Download the image here][2]][2]

Expected Heatmap type [![The generated Heatmap over images should be like this][3]][3]

Generated Results after applying @Paradox's code [![generated output][4]][4]

Additional clarification:

"p" is the probability value of having cancer or not in that particular area. I have extracted all the patches of size 256 x 256 from whole slide images and computed "probability values" of each patch. Now based on that value I'm planning to generate the heatmap. But by using your code I'm getting output like above. Even the color bar is missing. Please help.

Answer 1

Clean your data generating the heatmap

First, if you are not comfortable with deep-nested data, you should clean your data in your CSV file (they are not uniform and have duplicates - they are also error-prone if you want rectangles).

The simplest example being the following:

 x, y, x1, y1, Probability value
 0, 0, 5, 10, 0.5
 50, 45, 55, 65, 0.9
 100, 150, 120, 200, 0.3
 1000, 1005, 1005, 1010, 1

The answer below has been written with this clean CSV dataset in mind.

Use Pandas to process CSV data files

Seeing what your use case is, I recommend using pandas in order to process your CSV data files.

You can store data from a CSV file in a pandas DataFrame this way:

df = pd.read_csv("data.csv")

and iterate over rows, using the first row as keys for each column value, as following:

for index, row in df.iterrows():
    print(row["x"], row["y"], row["x1"], row["y1"], 
      row["Probability value"]         

Full working snippet

This snippet is not very pretty but it works for the dummy dataset you've provided and is intended to be pretty self-explanatory with the above introduction. Some tweaking might be needed, especially for the plotting part.

#!/usr/bin/python3
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from skimage import io
from skimage.color import rgb2gray
import matplotlib as mpl
# Read original image
img = io.imread('img.jpg')

# Get the dimensions of the original image
x_dim, y_dim, z_dim = np.shape(img)

# Create heatmap
heatmap = np.zeros((x_dim, y_dim), dtype=float)

# Read CSV with a Pandas DataFrame
df = pd.read_csv("data.csv")

# Set probabilities values to specific indexes in the heatmap
for index, row in df.iterrows():
    x = np.int(row["x"])
    y = np.int(row["y"])
    x1 = np.int(row["x1"])
    y1 = np.int(row["y1"])
    p = row["Probability value"]
    heatmap[x:x1,y:y1] = p

# Plot images
fig, axes = plt.subplots(1, 2, figsize=(8, 4))
ax = axes.ravel()

ax[0].imshow(img)
ax[0].set_title("Original")
fig.colorbar(ax[0].imshow(img), ax=ax[0])

ax[1].imshow(img, vmin=0, vmax=1)
ax[1].imshow(heatmap, alpha=.5, cmap='jet')
ax[1].set_title("Original + heatmap")

# Specific colorbar
norm = mpl.colors.Normalize(vmin=0,vmax=2)
N = 11
cmap = plt.get_cmap('jet',N)
sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
sm.set_array([])
plt.colorbar(sm, ticks=np.linspace(0,1,N), 
             boundaries=np.arange(0,1.1,0.1)) 

fig.tight_layout()
plt.show()