Digital Images Processing

Practical 1: Loading & Displaying Images

Run this practical in Google Colab

# ----------------------------------------------------------------------

# Install dependencies (uncomment for Google Colab)
# !pip install opencv-python-headless matplotlib numpy

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

# === AUTO-DOWNLOAD DATASET (works in Google Colab and locally) ===
import os, urllib.request, zipfile

# Choose which chapter to download (CH01-CH12)
CHAPTER = "CH02"  # Chapter 2: Digital Image Fundamentals
DATASET_PATH = f"datasets/{CHAPTER}/"
DOWNLOAD_BASE = "https://www.imageprocessingplace.com/downloads_V3/dip3e_downloads/dip3e_book_images"

if not os.path.exists(DATASET_PATH) or not any(f.endswith('.tif') for f in os.listdir(DATASET_PATH)):
    zip_name = f"DIP3E_{CHAPTER}_Original_Images.zip"
    url = f"{DOWNLOAD_BASE}/{zip_name}"
    print(f"Downloading {CHAPTER} dataset from imageprocessingplace.com...")
    urllib.request.urlretrieve(url, "chapter.zip")
    os.makedirs(DATASET_PATH, exist_ok=True)
    with zipfile.ZipFile("chapter.zip", "r") as z:
        for f in z.namelist():
            if f.lower().endswith(".tif"):
                fname = os.path.basename(f)
                if fname:
                    with z.open(f) as src, open(os.path.join(DATASET_PATH, fname), "wb") as dst:
                        dst.write(src.read())
    os.remove("chapter.zip")
    print(f"Downloaded {len([f for f in os.listdir(DATASET_PATH) if f.endswith('.tif')])} images")
else:
    print(f"Dataset ready: {len([f for f in os.listdir(DATASET_PATH) if f.endswith('.tif')])} images")

# List all available images
images = sorted([f for f in os.listdir(DATASET_PATH) if f.endswith('.tif')])
print(f"\nAvailable images ({len(images)}):")
for i, name in enumerate(images, 1):
    print(f"  {i}. {name}")

# === SELECT YOUR IMAGE HERE ===
selected_image = "Fig0222(b)(cameraman).tif"  # Change this to any image from the list above

# Load the image in grayscale
img = cv2.imread(os.path.join(DATASET_PATH, selected_image), cv2.IMREAD_GRAYSCALE)

# Display image properties
print(f"Image: {selected_image}")
print(f"Shape: {img.shape}")
print(f"Data type: {img.dtype}")
print(f"Min pixel value: {img.min()}")
print(f"Max pixel value: {img.max()}")
print(f"Mean pixel value: {img.mean():.2f}")

plt.figure(figsize=(6, 6))
plt.imshow(img, cmap='gray')
plt.title(f"{selected_image} ({img.shape[0]}x{img.shape[1]})")
plt.axis('off')
plt.tight_layout()
plt.show()

# === SELECT 4 IMAGES TO COMPARE ===
image_names = [
    "Fig0222(b)(cameraman).tif",
    "Fig0221(a)(ctskull-256).tif",
    "Fig0230(a)(dental_xray).tif",
    "Fig0226(galaxy_pair_original).tif"
]

fig, axes = plt.subplots(2, 2, figsize=(10, 10))
for ax, name in zip(axes.flatten(), image_names):
    image = cv2.imread(os.path.join(DATASET_PATH, name), cv2.IMREAD_GRAYSCALE)
    ax.imshow(image, cmap='gray')
    ax.set_title(f"{name.split('(')[-1].replace(').tif','')}\n{image.shape[0]}x{image.shape[1]}")
    ax.axis('off')
plt.suptitle("Multiple Images from Dataset", fontsize=14, fontweight='bold')
plt.tight_layout()
plt.show()

# Detailed properties
print(f"{'Property':<20} {'Value'}")
print("-" * 40)
print(f"{'Filename':<20} {selected_image}")
print(f"{'Dimensions':<20} {img.shape[0]} x {img.shape[1]}")
print(f"{'Total pixels':<20} {img.size}")
print(f"{'Data type':<20} {img.dtype}")
print(f"{'Min intensity':<20} {img.min()}")
print(f"{'Max intensity':<20} {img.max()}")
print(f"{'Mean intensity':<20} {img.mean():.2f}")
print(f"{'Std deviation':<20} {img.std():.2f}")

# Display image alongside its histogram
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))

ax1.imshow(img, cmap='gray')
ax1.set_title(f"{selected_image}")
ax1.axis('off')

ax2.hist(img.ravel(), bins=256, range=(0, 256), color='black', alpha=0.7)
ax2.set_title("Pixel Intensity Histogram")
ax2.set_xlabel("Intensity Value (0-255)")
ax2.set_ylabel("Frequency")
ax2.set_xlim(0, 256)

plt.tight_layout()
plt.show()