Counting Objects continued

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:

1. On the left is the Median-Filtered Image, which has been smoothed using a 9x9 median filter.
2. In the middle is the Thresholded and Labeled Image, where each object detected within the specified thresholds is labeled with a unique number.
3. 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.