2754 words

14 minutes

Building a Python Tool to Batch Compress Images for the Web

2025-06-30

Project

Python

/

Image Compression

/

Web Optimization

/

Pillow

/

Automation

Building a Python Tool to Batch Compress Images for the Web#

Image optimization stands as a critical task in web development. Large image files significantly impact page load times, consuming bandwidth and degrading user experience. Data indicates that images often constitute the largest portion of data downloaded when visiting a webpage. Slow loading times correlate directly with higher bounce rates and lower search engine rankings, as page speed is a known ranking factor for search engines like Google. Automating the process of optimizing images, particularly for projects involving numerous visual assets, offers substantial efficiency gains. Building a tool in Python for batch compress images python tasks provides a flexible and customizable solution for web image optimization.

Why Image Compression Matters for the Web#

The size of image files directly affects website performance. When a user visits a webpage, their browser downloads all the necessary assets, including images. Larger image files take longer to download, leading to increased page load times.

Improved User Experience: Faster websites are more enjoyable to use. Reduced load times decrease frustration and keep users engaged.
Lower Bandwidth Consumption: Optimized images require less data to transfer, benefiting both website owners (reduced hosting costs if bandwidth is charged) and users (especially those on limited data plans).
Enhanced Search Engine Optimization (SEO): Page speed is a ranking signal for search engines. Faster sites tend to rank higher, improving visibility. Core Web Vitals metrics, which include Largest Contentful Paint (LCP) often impacted by image loading, are also relevant for SEO.
Reduced Storage Space: Smaller image files require less disk space on servers or content delivery networks (CDNs).

Manual image compression for a large number of files is impractical and time-consuming. This is where a batch compress images python tool becomes invaluable. It allows processing entire directories of images automatically according to predefined settings.

Essential Concepts in Web Image Optimization#

Understanding fundamental image concepts is crucial for effective compression.

Lossy vs. Lossless Compression#

Image compression techniques fall into two main categories:

Lossy Compression: This method achieves smaller file sizes by permanently discarding some image data. The result is a file that is significantly smaller but with a potential reduction in image quality. This is acceptable for many web images where a slight loss in fidelity is imperceptible or tolerable for the benefit of speed. JPEG is the most common lossy format.
Lossless Compression: This method compresses the image data without losing any information. The original image can be perfectly reconstructed from the compressed file. File sizes are generally larger than with lossy compression but smaller than the uncompressed version. PNG and GIF formats use lossless compression.

Choosing between lossy and lossless depends on the image content and purpose. Photographs typically benefit from lossy compression (JPEG, WebP lossy), while images with sharp lines, text, or transparency (logos, icons, screenshots) are better suited for lossless compression (PNG, GIF, WebP lossless, AVIF lossless).

Common Web Image Formats#

Different image formats are optimized for different types of images and compression methods.

Format	Description	Compression Type	Typical Use Cases	Browser Support	Notes
JPEG	Joint Photographic Experts Group	Lossy	Photographs, complex images	Excellent	Widely supported, good for gradients
PNG	Portable Network Graphics	Lossless	Graphics, images with transparency	Excellent	Can result in large files for photos
GIF	Graphics Interchange Format	Lossless	Simple animations, small graphics	Excellent	Limited color palette (256 colors)
WebP	Google’s WebP format	Lossy & Lossless	Versatile (photos, graphics, animation)	Very Good	Offers superior compression over JPEG/PNG
AVIF	AV1 Image File Format	Lossy & Lossless	Versatile	Growing	Newer, potentially better compression than WebP

Modern formats like WebP and AVIF often provide better compression ratios than older formats like JPEG and PNG while maintaining comparable visual quality. Converting images to these formats can be part of a batch optimization process.

Key Compression Techniques#

Beyond choosing a format and compression type, specific techniques can be applied:

Reducing Quality (Lossy): For formats like JPEG or WebP lossy, the “quality” setting (often a number from 0-100) controls the degree of compression. Lower quality means smaller files but more artifacts. A quality setting between 70-85 often provides a good balance for web use.
Optimizing File Structure: Removing unnecessary metadata (like camera information, geotags) from image files can reduce their size slightly.
Resizing: While not strictly compression, reducing the dimensions of an image to match its display size on the web significantly reduces file size and is a crucial optimization step often performed alongside compression. A 2000px wide image displayed in a 500px container is wasting bandwidth.

Python Libraries for Image Manipulation#

Python offers robust libraries for working with images. The Pillow library (a fork of the original PIL - Python Imaging Library) is the de facto standard for image processing in Python. It provides functionalities for opening, manipulating, and saving images in various formats, making it ideal for building a batch compression tool.

Installation of Pillow is typically done via pip:

1
pip install Pillow

Building the Batch Compression Tool with Python#

This section outlines the steps to create a simple Python script for batch image compression using the Pillow library. The script will iterate through a specified input directory, compress supported image files, and save them to an output directory.

Tool Requirements#

Accept input and output directory paths.
Support common web image formats (JPEG, PNG).
Apply lossy compression (quality setting) for applicable formats (like JPEG).
Save compressed images to the output directory, preserving original filenames.
Handle potential errors (e.g., non-image files, directory issues).

Step-by-Step Implementation#

The core of the script involves traversing a directory and processing each file.

Step 1: Set up the basic script structure#

Import the necessary libraries: os for interacting with the file system and PIL (Pillow) for image manipulation.

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import os
2
from PIL import Image
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# Define supported image extensions
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SUPPORTED_FORMATS = ['.jpg', '.jpeg', '.png']

Step 2: Create a function to compress a single image#

This function will take the input file path, output directory, and compression parameters as arguments. It will handle opening the image, compressing it, and saving it.

1
def compress_image(input_path, output_dir, quality=85):
2
    """Compresses a single image and saves it to the output directory."""
3
    try:
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        img = Image.open(input_path)
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        # Determine output path and format
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        filename = os.path.basename(input_path)
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        name, ext = os.path.splitext(filename)
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        ext = ext.lower() # Convert extension to lowercase
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        output_path = os.path.join(output_dir, filename)
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        # Handle different formats and apply compression
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        if ext in ['.jpg', '.jpeg']:
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            # JPEG compression uses the 'quality' parameter
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            if img.mode == 'RGBA':
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                 # Convert RGBA to RGB before saving as JPEG to avoid errors
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                 img = img.convert('RGB')
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            img.save(output_path, 'JPEG', quality=quality, optimize=True)
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            print(f"Compressed {filename} (JPEG, quality={quality})")
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        elif ext == '.png':
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            # PNG compression is lossless, optimize=True helps
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            # Consider converting to WebP for better compression if transparency isn't essential
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            img.save(output_path, 'PNG', optimize=True)
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            print(f"Optimized {filename} (PNG, lossless)")
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        # Add support for other formats if needed (e.g., convert to WebP)
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        # elif ext in ['.gif', '.webp', '.tiff', '.bmp']:
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        #     # Example: Convert any supported image to WebP lossy
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        #     output_webp_path = os.path.join(output_dir, f"{name}.webp")
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        #     if img.mode == 'RGBA':
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        #         img.save(output_webp_path, 'WebP', quality=quality, lossless=False)
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        #     else:
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        #          img.save(output_webp_path, 'WebP', quality=quality, lossless=False)
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        #     print(f"Converted and compressed {filename} to WebP (quality={quality})")
37

38

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        else:
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            print(f"Skipping {filename}: Unsupported format {ext}")
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            return # Skip if format is not supported by compression logic
42

43
    except FileNotFoundError:
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        print(f"Error: File not found at {input_path}")
45
    except PermissionError:
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        print(f"Error: Permission denied for {input_path} or {output_path}")
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    except Exception as e:
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        print(f"Error processing {filename}: {e}")

Explanation:

The function takes the input_path, output_dir, and a quality parameter (defaulting to 85).
It opens the image using Image.open().
It extracts the filename and extension to determine the output path and format.
It uses img.save() to write the image to the output directory.
For JPEG, it explicitly sets the quality parameter. optimize=True is also passed, which performs additional lossless optimization steps. A check for RGBA mode is added as saving RGBA directly to JPEG can cause errors; converting to RGB resolves this.
For PNG, compression is lossless, so optimize=True is used for minor size reductions without quality loss.
Basic error handling is included.

Step 3: Create the main function to process the directory#

This function will iterate through the input directory and call the compress_image function for each supported file.

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def batch_compress_images(input_dir, output_dir, quality=85):
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    """Batches compress images from an input directory to an output directory."""
3

4
    if not os.path.isdir(input_dir):
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        print(f"Error: Input directory not found at {input_dir}")
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        return
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    # Create output directory if it doesn't exist
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    if not os.path.exists(output_dir):
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        os.makedirs(output_dir)
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        print(f"Created output directory: {output_dir}")
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    elif not os.path.isdir(output_dir):
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         print(f"Error: Output path {output_dir} exists but is not a directory.")
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         return
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16

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    print(f"Starting batch compression from '{input_dir}' to '{output_dir}'...")
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    processed_count = 0
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    skipped_count = 0
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    # Walk through the input directory
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    for root, _, files in os.walk(input_dir):
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        for filename in files:
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            input_path = os.path.join(root, filename)
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            _, ext = os.path.splitext(filename)
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            # Check if the file is a supported image format
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            if ext.lower() in SUPPORTED_FORMATS:
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                # Calculate the relative path from input_dir to maintain structure
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                relative_path = os.path.relpath(root, input_dir)
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                current_output_dir = os.path.join(output_dir, relative_path)
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                # Ensure the corresponding output subdirectory exists
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                if not os.path.exists(current_output_dir):
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                    os.makedirs(current_output_dir)
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                # Compress the image
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                compress_image(input_path, current_output_dir, quality)
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                processed_count += 1
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            else:
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                print(f"Skipping {filename}: Not a supported image format.")
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                skipped_count += 1
44

45

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    print("-" * 20)
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    print("Batch compression finished.")
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    print(f"Processed: {processed_count}")
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    print(f"Skipped: {skipped_count}")

Explanation:

The function checks if the input directory exists and creates the output directory if needed.
os.walk(input_dir) is used to traverse the directory recursively, including subdirectories.
For each file, it checks if the extension is in the SUPPORTED_FORMATS list.
It calculates the relative_path from the input directory to the current file’s directory (root). This ensures that the directory structure is preserved in the output directory.
It creates the corresponding subdirectory in the output path if it doesn’t exist.
It calls compress_image for supported files.
Counts are maintained for processed and skipped files.

Step 4: Add entry point and potentially command-line arguments#

This makes the script runnable and allows specifying input/output directories and quality from the command line.

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import argparse
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if __name__ == "__main__":
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    parser = argparse.ArgumentParser(description="Batch compress images for web.")
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    parser.add_argument("input_dir", help="Directory containing images to compress.")
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    parser.add_argument("output_dir", help="Directory to save compressed images.")
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    parser.add_argument("-q", "--quality", type=int, default=85,
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                        help="JPEG/WebP compression quality (0-100). Default is 85.")
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    args = parser.parse_args()
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    batch_compress_images(args.input_dir, args.output_dir, args.quality)

Explanation:

argparse is used to handle command-line arguments.
input_dir and output_dir are required positional arguments.
-q or --quality is an optional argument to specify the compression quality.
The if __name__ == "__main__": block ensures the code runs only when the script is executed directly.

Complete Script#

1
import os
2
from PIL import Image
3
import argparse
4

5
# Define supported image extensions (case-insensitive comparison will be used)
6
SUPPORTED_FORMATS = ['.jpg', '.jpeg', '.png']
7

8
def compress_image(input_path, output_dir, quality=85):
9
    """Compresses a single image and saves it to the output directory."""
10
    try:
11
        img = Image.open(input_path)
12

13
        # Determine output path and format
14
        filename = os.path.basename(input_path)
15
        name, ext = os.path.splitext(filename)
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        ext = ext.lower() # Convert extension to lowercase
17

18
        output_path = os.path.join(output_dir, filename)
19

20
        # Handle different formats and apply compression
21
        if ext in ['.jpg', '.jpeg']:
22
            # JPEG compression uses the 'quality' parameter
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            # Convert RGBA to RGB before saving as JPEG to avoid errors with alpha channel
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            if img.mode == 'RGBA':
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                 img = img.convert('RGB')
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            img.save(output_path, 'JPEG', quality=quality, optimize=True)
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            print(f"Compressed {filename} (JPEG, quality={quality})")
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29
        elif ext == '.png':
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            # PNG compression is lossless, optimize=True helps
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            # Consider converting to WebP for better compression if transparency isn't essential
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            img.save(output_path, 'PNG', optimize=True)
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            print(f"Optimized {filename} (PNG, lossless)")
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        # Example: Add support for converting any supported image to WebP lossy
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        # This part is commented out but shows how to add format conversion
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        # elif ext in ['.gif', '.webp', '.tiff', '.bmp', '.jpg', '.jpeg', '.png']: # Add other source formats
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        #     output_webp_path = os.path.join(output_dir, f"{name}.webp")
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        #     # Convert RGBA to RGB for WebP if lossless=False
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        #     if img.mode == 'RGBA' and quality < 100:
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        #         img.save(output_webp_path, 'WebP', quality=quality, lossless=False)
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        #     else:
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        #          # For lossless or non-RGBA, save directly
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        #          img.save(output_webp_path, 'WebP', quality=quality, lossless=(quality==100)) # Use lossless=True for quality 100
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        #     print(f"Converted and compressed {filename} to WebP (quality={quality})")
46

47
        else:
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            # This case should ideally not be reached if filtering by SUPPORTED_FORMATS is effective,
49
            # but serves as a safeguard.
50
            print(f"Skipping {filename}: Format {ext} not handled by compression logic.")
51

52

53
    except FileNotFoundError:
54
        print(f"Error: File not found at {input_path}")
55
    except PermissionError:
56
        print(f"Error: Permission denied for {input_path} or {output_path}")
57
    except Exception as e:
58
        print(f"Error processing {filename}: {e}")
59

60
def batch_compress_images(input_dir, output_dir, quality=85):
61
    """Batches compress images from an input directory to an output directory."""
62

63
    if not os.path.isdir(input_dir):
64
        print(f"Error: Input directory not found at {input_dir}")
65
        return
66

67
    # Create output directory if it doesn't exist
68
    if not os.path.exists(output_dir):
69
        try:
70
            os.makedirs(output_dir)
71
            print(f"Created output directory: {output_dir}")
72
        except OSError as e:
73
             print(f"Error creating output directory {output_dir}: {e}")
74
             return
75
    elif not os.path.isdir(output_dir):
76
         print(f"Error: Output path {output_dir} exists but is not a directory.")
77
         return
78

79

80
    print(f"Starting batch compression from '{input_dir}' to '{output_dir}'...")
81

82
    processed_count = 0
83
    skipped_count = 0
84

85
    # Walk through the input directory, including subdirectories
86
    for root, _, files in os.walk(input_dir):
87
        for filename in files:
88
            input_path = os.path.join(root, filename)
89
            _, ext = os.path.splitext(filename)
90

91
            # Check if the file has a supported image format extension
92
            if ext.lower() in SUPPORTED_FORMATS:
93
                # Calculate the corresponding output path, preserving directory structure
94
                relative_path = os.path.relpath(root, input_dir)
95
                current_output_dir = os.path.join(output_dir, relative_path)
96

97
                # Ensure the corresponding output subdirectory exists
98
                if not os.path.exists(current_output_dir):
99
                    try:
100
                       os.makedirs(current_output_dir)
101
                    except OSError as e:
102
                        print(f"Error creating output subdirectory {current_output_dir}: {e}")
103
                        continue # Skip processing files in this subdirectory
104

105

106
                # Compress the image
107
                compress_image(input_path, current_output_dir, quality)
108
                processed_count += 1
109
            else:
110
                print(f"Skipping {filename} in {root}: Not a supported image format ({ext}).")
111
                skipped_count += 1
112

113

114
    print("-" * 20)
115
    print("Batch compression finished.")
116
    print(f"Processed: {processed_count}")
117
    print(f"Skipped: {skipped_count}")
118

119

120
if __name__ == "__main__":
121
    parser = argparse.ArgumentParser(description="Batch compress images for web.")
122
    parser.add_argument("input_dir", help="Directory containing images to compress (recursive scan).")
123
    parser.add_argument("output_dir", help="Directory to save compressed images (directory structure preserved).")
124
    parser.add_argument("-q", "--quality", type=int, default=85,
125
                        help="JPEG/WebP compression quality (0-100). PNG is lossless.")
126

127
    args = parser.parse_args()
128

129
    batch_compress_images(args.input_dir, args.output_dir, args.quality)

How to Run the Tool#

Save the code as a Python file (e.g., compressor.py).
Make sure Pillow is installed (pip install Pillow).
Open a terminal or command prompt.
Run the script using the following command syntax:
Terminal window
```
1
python compressor.py /path/to/your/input/images /path/to/save/compressed/images -q 80
```
Replace /path/to/your/input/images with the directory containing the images to compress and /path/to/save/compressed/images with the desired output directory. The -q 80 is optional; if omitted, the default quality of 85 will be used for JPEGs.

Real-World Applications and Extensions#

This basic Python script provides a solid foundation for automating web image optimization. Its practical use cases are numerous:

Website Deployment: Before deploying a website, run the script on the project’s image asset folder to ensure all images are reasonably optimized.
Blog or Portfolio Updates: Optimize batches of photos for new articles or portfolio entries.
E-commerce Product Images: Process large sets of product photos provided by manufacturers.
Content Management Systems (CMS): Integrate the script into a CMS workflow to automatically optimize uploaded images.

The script can be extended in various ways:

Format Conversion: Add logic to automatically convert images to modern formats like WebP or AVIF if the source format is JPEG or PNG, potentially creating both original format (for compatibility) and new format versions.
Resizing: Incorporate image resizing based on specified dimensions or breakpoints.
Metadata Removal: Explicitly remove EXIF metadata using img.info and saving options to further reduce file size.
Recursive Processing Control: Add an option to enable/disable recursive processing of subdirectories.
Logging: Implement logging to record which files were processed, skipped, or resulted in errors.
GUI or Web Interface: Wrap the script in a simple graphical user interface or a web service for easier use by non-technical users.

For instance, adding WebP conversion could involve modifying the compress_image function to check the original extension and save a .webp version, potentially alongside or instead of the original format, based on configuration.

1
# Example snippet for adding WebP conversion logic
2
def compress_image_with_webp(input_path, output_dir, quality=85):
3
    try:
4
        img = Image.open(input_path)
5
        filename = os.path.basename(input_path)
6
        name, ext = os.path.splitext(filename)
7
        ext = ext.lower()
8

9
        # Save original format with compression (JPEG/PNG)
10
        if ext in ['.jpg', '.jpeg']:
11
             if img.mode == 'RGBA': img = img.convert('RGB')
12
             output_path_orig = os.path.join(output_dir, filename)
13
             img.save(output_path_orig, 'JPEG', quality=quality, optimize=True)
14
             print(f"Compressed {filename} (JPEG, quality={quality})")
15
        elif ext == '.png':
16
             output_path_orig = os.path.join(output_dir, filename)
17
             img.save(output_path_orig, 'PNG', optimize=True)
18
             print(f"Optimized {filename} (PNG, lossless)")
19
        # Add other original formats here
20

21
        # Always attempt to save a WebP version (lossy unless quality is 100)
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        output_path_webp = os.path.join(output_dir, f"{name}.webp")
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        # Convert RGBA to RGB for WebP if lossless=False
24
        if img.mode == 'RGBA' and quality < 100:
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             img.save(output_path_webp, 'WebP', quality=quality, lossless=False)
26
        else:
27
             img.save(output_path_webp, 'WebP', quality=quality, lossless=(quality==100)) # Use lossless=True for quality 100
28

29
        print(f"Created WebP for {filename} (quality={quality})")
30

31
    except Exception as e:
32
        print(f"Error processing {filename}: {e}")
33

34
# The batch function would call compress_image_with_webp instead
35
# batch_compress_images(..., compress_func=compress_image_with_webp) # Pass function as argument

This demonstrates the flexibility of using Python and Pillow to build custom image optimization workflows tailored to specific needs, such as implementing modern format adoption strategies.

Key Takeaways#

Image compression is essential for web performance, impacting page load times, user experience, and SEO.
Large collections of images require batch compress images python tools for efficient optimization.
Python’s Pillow library provides the necessary capabilities to manipulate and save images with compression settings.
Understanding lossy vs. lossless compression and common web formats (JPEG, PNG, WebP, AVIF) informs effective optimization strategies.
A basic Python script can iterate through directories, apply compression (like setting JPEG quality or using PNG optimization), and save processed files while preserving directory structure.
The provided script serves as a foundation that can be extended to include format conversion (e.g., to WebP), resizing, metadata removal, and more sophisticated features.
Implementing a python image compression tool allows for fine-grained control over the optimization process compared to general-purpose tools.