Digital Images Processing

Practical 2: Impact of Sampling Rate on Spatial Resolution

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

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

# Convert BGR to Grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
print(f"Original shape: {gray.shape}")

# Establish baseline resolution: 1024 x 1024
baseline = cv2.resize(gray, (1024, 1024), interpolation=cv2.INTER_LINEAR)
print(f"Baseline resolution: {baseline.shape}")

plt.figure(figsize=(6, 6))
plt.imshow(baseline, cmap='gray')
plt.title(f"Baseline: 1024 x 1024")
plt.axis('off')
plt.show()

# Progressive downsampling
current = baseline.copy()
resolutions = [(1024, 1024)]  # Store all resolutions
downsampled_images = [baseline.copy()]  # Store all images

for i in range(5):
    h, w = current.shape
    new_h = h // 2
    new_w = w // 2
    current = cv2.resize(current, (new_w, new_h), interpolation=cv2.INTER_AREA)
    resolutions.append((new_h, new_w))
    downsampled_images.append(current.copy())
    
    plt.figure(figsize=(5, 5))
    plt.imshow(current, cmap='gray')
    plt.title(f"Step {i+1}: {new_w} x {new_h}")
    plt.axis('off')
    plt.show()
    print(f"Step {i+1}: Resolution = {new_w} x {new_h}")

fig, axes = plt.subplots(2, 3, figsize=(15, 10))
labels = ["1024x1024", "512x512", "256x256", "128x128", "64x64", "32x32"]

for ax, img_ds, label in zip(axes.flatten(), downsampled_images, labels):
    ax.imshow(img_ds, cmap='gray')
    ax.set_title(label, fontsize=12, fontweight='bold')
    ax.axis('off')

plt.suptitle(f"Progressive Downsampling: {selected_image}", fontsize=14, fontweight='bold')
plt.tight_layout()
plt.show()

fig, axes = plt.subplots(2, 3, figsize=(15, 10))

for ax, img_ds, label in zip(axes.flatten(), downsampled_images, labels):
    # Resize back to 1024x1024 to see quality loss
    upscaled = cv2.resize(img_ds, (1024, 1024), interpolation=cv2.INTER_LINEAR)
    ax.imshow(upscaled, cmap='gray')
    ax.set_title(f"From {label} → 1024x1024", fontsize=11)
    ax.axis('off')

plt.suptitle("Quality Loss: Downsampled then Upscaled Back to 1024x1024", fontsize=14, fontweight='bold')
plt.tight_layout()
plt.show()