Histogram
=========

This example shows how to calculate histogram of an image.
In addition to the bin counts, the :ref:`hist` command outputs also bin starts.

In the end of the example the histogram is plotted using PyPlot.

::

	def histogram():
		"""
		Demonstrates histogram calculation.
		"""

		# Read image
		img = pi.read(input_file())

		# Calculate histogram
		hist_min = 0        # Start of the first bin
		hist_max = 1000     # End of the last bin
		bin_count = 100     # Count of bins
		hist = pi.newimage(ImageDataType.FLOAT32)
		bins = pi.newimage(ImageDataType.FLOAT32)
		pi.hist(img, hist, bins, hist_min, hist_max, bin_count)
		

		# Get histogram data as NumPy array
		data = hist.get_data()
		bin_starts = bins.get_data()

		# Most of the background pixels are in the first bin.
		# We set it to zero so that the output image becomes nicely scaled
		# automatically.
		data[0] = 0

		# Bin edges can be generated like this if they have not been grabbed
		# from hist(...) command
		#bin_edges = np.linspace(hist_min, hist_max, bin_count + 1)

		# Convert bin starts to bin centers
		bin_size = bin_starts[1] - bin_starts[0]
		bin_centers = bin_starts + bin_size / 2

		
		# Plot the histogram
		import matplotlib.pyplot as plt
		plt.bar(bin_centers, data, 0.8*bin_size)
		plt.xlabel('Gray value')
		plt.ylabel('Count')
		plt.tight_layout()
		plt.show(block=False)
		plt.savefig(output_file('histogram.png'))


.. figure:: figures/t1-head_slice.png
   :scale: 100 %
   :alt: Input image

   Slice of the input image.


.. figure:: figures/histogram.png
   :scale: 75 %
   :alt: Histogram

   Histogram of the input image. The peak corresponding to the background has been erased.