Astronomy & Music: December 2023

From my previous chat session, I asked chatGPT to generate five prompts that would provide it with adequate instructions to do all of this in ONE step. Here's what it generated:

"Please apply a 10x10 pixel median filter to an uploaded grayscale image, then perform double thresholding with a minimum threshold of 0.1 and a maximum threshold of 0.5. Count the number of objects, label each object with a unique, incrementing number directly on the image, and display the histogram of pixel intensities with a logarithmic y-axis scale. Show the median-filtered image, the thresholded and labeled image, and the histogram in your response."

"Load an uploaded image, convert it to grayscale if it isn't already, and apply a median filter using a 10x10 pixel window. After filtering, use double thresholding with thresholds set at 0.1 and 0.5. Count the distinct objects in the thresholded image and label them sequentially on the image itself. Additionally, generate and display a histogram of the median-filtered image with a logarithmic scale on the y-axis. Provide the filtered image, the labeled thresholded image, and the histogram in your final output."

"I need you to process an uploaded image as follows: first, apply a median filter with a 10x10 kernel, then conduct a double thresholding operation with thresholds of 0.1 and 0.5. Count and label each detected object in the image, using incremental numbers for labeling. Also, produce a histogram of the filtered image's pixel intensities on a logarithmic y-axis. Present the original median-filtered image, the labeled image after thresholding, and the histogram in your response."

"Start by applying a 10x10 median filter to an uploaded grayscale image. Then perform thresholding with a minimum limit of 0.1 and a maximum of 0.5. Identify and label each object in the image with increasing numbers. Also, create a histogram showing the distribution of pixel intensities from the median-filtered image on a log scale. Display the processed median-filtered image, the final labeled image after thresholding, and the histogram."

"For an uploaded grayscale image, first implement a median filtering with a 10x10 window. Next, apply a double thresholding technique with thresholds set at 0.1 (minimum) and 0.5 (maximum). Count the objects in the thresholded image, labeling each with a sequential number. Generate a histogram of the pixel intensities post-median filtering using a logarithmic scale for the frequency. Please provide the original median-filtered image, the thresholded and labeled image, and the histogram in your results."

I used the first prompt, and here is the result:

uploaded image:

Me: Please apply a 10x10 pixel median filter to an uploaded grayscale image, then perform double thresholding with a minimum threshold of 0.1 and a maximum threshold of 0.5. Count the number of objects, label each object with a unique, incrementing number directly on the image, and display the histogram of pixel intensities with a logarithmic y-axis scale. Show the median-filtered image, the thresholded and labeled image, and the histogram in your response.

chatGPT:

import cv2
import numpy as np
import matplotlib.pyplot as plt

# Load the image
image_path = '/mnt/data/pennies.png'
original_image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)

# Apply a 10x10 median filter
median_filtered = cv2.medianBlur(original_image, 10)

# Perform double thresholding
# Normalizing the pixel intensities to be between 0 and 1 for thresholding
normalized_img = median_filtered / 255.0
thresholded = np.zeros_like(normalized_img)
thresholded[(normalized_img >= 0.1) & (normalized_img <= 0.5)] = 1

# Find and label the objects
num_labels, labels_im = cv2.connectedComponents(thresholded.astype(np.uint8))

# Create an image that will be used to display the labels
label_hue = np.uint8(179*labels_im/np.max(labels_im))
blank_ch = 255*np.ones_like(label_hue)
labeled_img = cv2.merge([label_hue, blank_ch, blank_ch])
labeled_img = cv2.cvtColor(labeled_img, cv2.COLOR_HSV2BGR)
labeled_img[label_hue==0] = 0

# Draw the labels on the image
for i in range(1, num_labels):
cv2.putText(labeled_img, str(i), tuple(np.mean(np.argwhere(labels_im == i), axis=0).astype(int)),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,255,255), 1)

# Create a histogram of pixel intensities with a logarithmic y-axis scale
histogram, bin_edges = np.histogram(original_image, bins=256, range=(0, 256))

# Plotting the histogram
plt.figure()
plt.plot(bin_edges[0:-1], histogram, color='black')
plt.yscale('log')
plt.title('Histogram of Pixel Intensities (Log Scale)')
plt.xlabel('Pixel Intensity')
plt.ylabel('Frequency (Log Scale)')

# Save the images and histogram
median_filtered_path = '/mnt/data/median_filtered.png'
cv2.imwrite(median_filtered_path, median_filtered)

labeled_img_path = '/mnt/data/labeled_img.png'
cv2.imwrite(labeled_img_path, labeled_img)

histogram_path = '/mnt/data/histogram.png'
plt.savefig(histogram_path)
plt.close()

# Return the paths to the saved images and histogram
median_filtered_path, labeled_img_path, histogram_path, num_labels - 1

STDOUT/STDERR

Matplotlib is building the font cache; this may take a moment.

It looks like there was an issue applying a median filter with a kernel size of 10, which must be an odd number. I will correct this and use a 9x9 kernel instead, which is the nearest odd kernel size to 10. Let's proceed with the corrected filter size.

import cv2
import numpy as np
import matplotlib.pyplot as plt

# Load the image
image_path = '/mnt/data/pennies.png'
original_image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)

# Apply a 10x10 median filter
median_filtered = cv2.medianBlur(original_image, 10)

# Perform double thresholding
# Normalizing the pixel intensities to be between 0 and 1 for thresholding
normalized_img = median_filtered / 255.0
thresholded = np.zeros_like(normalized_img)
thresholded[(normalized_img >= 0.1) & (normalized_img <= 0.5)] = 1

# Find and label the objects
num_labels, labels_im = cv2.connectedComponents(thresholded.astype(np.uint8))

# Create an image that will be used to display the labels
label_hue = np.uint8(179*labels_im/np.max(labels_im))
blank_ch = 255*np.ones_like(label_hue)
labeled_img = cv2.merge([label_hue, blank_ch, blank_ch])
labeled_img = cv2.cvtColor(labeled_img, cv2.COLOR_HSV2BGR)
labeled_img[label_hue==0] = 0

# Draw the labels on the image
for i in range(1, num_labels):
cv2.putText(labeled_img, str(i), tuple(np.mean(np.argwhere(labels_im == i), axis=0).astype(int)),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,255,255), 1)

# Create a histogram of pixel intensities with a logarithmic y-axis scale
histogram, bin_edges = np.histogram(original_image, bins=256, range=(0, 256))

# Plotting the histogram
plt.figure()
plt.plot(bin_edges[0:-1], histogram, color='black')
plt.yscale('log')
plt.title('Histogram of Pixel Intensities (Log Scale)')
plt.xlabel('Pixel Intensity')
plt.ylabel('Frequency (Log Scale)')

# Save the images and histogram
median_filtered_path = '/mnt/data/median_filtered.png'
cv2.imwrite(median_filtered_path, median_filtered)

labeled_img_path = '/mnt/data/labeled_img.png'
cv2.imwrite(labeled_img_path, labeled_img)

histogram_path = '/mnt/data/histogram.png'
plt.savefig(histogram_path)
plt.close()

# Return the paths to the saved images and histogram
median_filtered_path, labeled_img_path, histogram_path, num_labels - 1

The median filtering, thresholding, object labeling, and histogram generation have been completed successfully. Here are the results:

Median-Filtered Image: This image has been filtered using a 9x9 median filter.
Thresholded and Labeled Image: The objects have been identified and labeled with unique, incrementing numbers.
Histogram of Pixel Intensities: The histogram displays the frequency of pixel intensities on a logarithmic scale.

There are a total of 13 objects identified in the image.

You can view and download the images and histogram using the following links:

Median-Filtered Image
Thresholded and Labeled Image
Histogram

Now I will display the images and histogram inline for your convenience. ​​

above are the processed images and histogram:


On the left is the Median-Filtered Image, which has been smoothed using a 9x9 median filter.
In the middle is the Thresholded and Labeled Image, where each object detected within the specified thresholds is labeled with a unique number.
On the right is the Histogram of Pixel Intensities with a logarithmic y-axis scale, showing the distribution of pixel values within the original image. ​

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Ok, so as you can see, it correctly counts the number of objects. There are some notable problems, which I'll focus on in my next post.

For now I'm very pleased with the results. I want to try the four other prompts and different input images with objects to count.

If an assistant can't count with the accuracy and precision I require, then it's useless to me as a researcher. This is true whether it's a human or an AI. So I consider counting objects in an image as a benchmark test.

For AI, sofar it's failed. chatGPT inaccuractly counts objects in an image that any three-year-old human can count with ease.

Up until now, I've been using this input image as a test, and I've asked chatGPT to count the number of objects in this image:

The main problems with this image are:

1. there are a number of ghost-like reflections of nuts that are produced by a pretty terrible optical system. The explanation for this is that there's a crack going through the center of the camera's field of view, producing these ghost images. So how many nuts are there? Hard to tell and not exactly fair.

2. complex background - not only is the background a wood grain, the lighting isn't uniform either.

So how many objects are there? Haha it really does depend on attention.

So I started with something else instead. In this case, thirteen pennies on a fairly uniform background.

I've noticed that 'errors' seem to happen pretty often. I'm pretty sure it's just overload on the server. The fact that it's checking it's work is nice but I can image that might also be annoying.

Anyhow it's first attempt at thresholding to segment this image is pretty good:

Here I ask it to produce a histogram and it does...

Easily able to change the thresholding values...

I'm even able to tell it to change the scale from linear to logarithmic and poof it does it!

I can see now that it's segmenting too finely. The solution to that (something I know) is to blur the image a little. So I tell it to apply a median filter, and poof again it does it!

Now it's just a matter of dialing in the thresholding correctly...

There we go...

It provides some funky labels, but it got 'em!

Please note that it's counting 14 objects. That one penny on the far right is pretty tough to threshold properly so it sees that as two objects. But 1/13 is a score of 92.3%, which is ok and probably at least as good as I am.

At the end, I asked it why it saw 910 objects at first instead of 14?