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Documentation Index

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Function Signature

def fetch_image(
    ele: Dict[str, Union[str, Image.Image]], 
    image_patch_size: int = 14
) -> Image.Image

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Description

Loads an image from various sources (local file, URL, base64 string, or PIL.Image object) and applies smart resizing based on the model’s requirements. The function automatically handles different image formats and converts them to RGB.

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Parameters

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ele
Dict[str, Union[str, Image.Image]]
required
Dictionary containing image information and optional resize parameters.Required keys:
  • image or image_url: The image source (file path, URL, base64 string, or PIL.Image)
Optional keys:
  • resized_height: Target height for resizing
  • resized_width: Target width for resizing
  • min_pixels: Minimum number of pixels (default: 4 * patch_factor²)
  • max_pixels: Maximum number of pixels (default: 16384 * patch_factor²)
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image_patch_size
int
default:"14"
The patch size used by the vision encoder. Affects the resizing factor calculation.Common values:
  • 14 for Qwen2VL and Qwen2.5VL
  • 16 for Qwen3VL
The actual resize factor is image_patch_size * 2 (spatial merge size).

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Returns

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image
PIL.Image.Image
Processed RGB PIL Image resized to dimensions divisible by the patch factor.The image dimensions are calculated using smart_resize to maintain aspect ratio while staying within min/max pixel constraints.

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Supported Image Sources

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Local File Path

from qwen_vl_utils import fetch_image

# Absolute path
image = fetch_image({"image": "/path/to/image.jpg"})

# File URI
image = fetch_image({"image": "file:///path/to/image.jpg"})

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HTTP/HTTPS URL

image = fetch_image({
    "image": "https://example.com/photo.jpg"
})

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Base64 Encoded

image = fetch_image({
    "image": "data:image;base64,/9j/4AAQSkZJRgABAQEA..."
})

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PIL.Image Object

from PIL import Image

pil_image = Image.open("photo.jpg")
processed_image = fetch_image({"image": pil_image})

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Custom Resize Parameters

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Specify Exact Dimensions

image = fetch_image({
    "image": "file:///path/to/image.jpg",
    "resized_height": 280,
    "resized_width": 420
})
Note: The actual dimensions will be adjusted to be divisible by the patch factor.

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Control Pixel Range

image = fetch_image({
    "image": "file:///path/to/image.jpg",
    "min_pixels": 56 * 56 * 4,      # Minimum 56x56 patches
    "max_pixels": 56 * 56 * 16384   # Maximum 16384 patches
})

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Image Processing Steps

  1. Load Image: Detects source type (file, URL, base64, PIL) and loads the image
  2. Convert to RGB: Handles RGBA images by compositing on white background
  3. Calculate Resize Dimensions: Uses smart_resize to determine optimal dimensions
  4. Resize: Applies resize maintaining aspect ratio within constraints

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Error Handling

try:
    image = fetch_image({"image": "invalid_source"})
except ValueError as e:
    print(f"Error: {e}")
    # Output: Unrecognized image input, support local path, http url, base64 and PIL.Image

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Usage with process_vision_info

While you can use fetch_image directly, it’s typically called internally by process_vision_info: 
from qwen_vl_utils import process_vision_info

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "file:///path/to/image.jpg"},
            {"type": "text", "text": "Describe this image."}
        ]
    }
]

images, videos = process_vision_info(messages)
# fetch_image is called automatically for each image

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RGBA Image Handling

Images with alpha channels (RGBA) are automatically composited onto a white background: 
from PIL import Image

# Load PNG with transparency
rgba_image = Image.open("logo.png")  # RGBA mode

# Automatically converted to RGB with white background
rgb_image = fetch_image({"image": rgba_image})

assert rgb_image.mode == "RGB"

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See Also

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