Typically, an object detection mannequin is educated with a hard and fast vocabulary, which means it may possibly solely acknowledge a predefined set of object classes. Nevertheless, in our pipeline, since we will’t predict prematurely which objects will seem within the picture, we want an object detection mannequin that’s versatile and able to recognizing a variety of object lessons. To attain this, I exploit the OWL-ViT mannequin [11], an open-vocabulary object detection mannequin. This mannequin requires textual content prompts that specifies the objects to be detected.
One other problem that must be addressed is acquiring a high-level concept of the objects current within the picture earlier than using the OWL-ViT mannequin, because it requires a textual content immediate describing the objects. That is the place VLMs come to the rescue! First, we cross the picture to the VLM with a immediate to determine the high-level objects within the picture. These detected objects are then used as textual content prompts, together with the picture, for the OWL-ViT mannequin to generate detections. Subsequent, we plot the detections as bounding containers on the identical picture and cross this up to date picture to the VLM, prompting it to generate a caption. The code for inference is partially tailored from [12].
# Load mannequin instantly
from transformers import AutoProcessor, AutoModelForZeroShotObjectDetectionprocessor = AutoProcessor.from_pretrained("google/owlvit-base-patch32")
mannequin = AutoModelForZeroShotObjectDetection.from_pretrained("google/owlvit-base-patch32")
I detect the objects current in every picture utilizing the VLM:
IMAGE_QUALITY = "excessive"
system_prompt_object_detection = """You might be supplied with a picture. You could determine all necessary objects within the picture, and supply a standardized checklist of objects within the picture.
Return your output as follows:
Output: object_1, object_2"""user_prompt = "Extract the objects from the offered picture:"
detected_objects = process_images_in_parallel(image_paths, system_prompt=system_prompt_object_detection, user_prompt=user_prompt, mannequin = "gpt-4o-mini", few_shot_prompt= None, element=IMAGE_QUALITY, max_workers=5)
detected_objects_cleaned = {}for key, worth in detected_objects.objects():
detected_objects_cleaned[key] = checklist(set([x.strip() for x in value.replace("Output: ", "").split(",")]))
The detected objects are actually handed as textual content prompts to the OWL-ViT mannequin to acquire the predictions for the photographs. I implement a helper perform that predicts the bounding containers for the photographs, after which plots the bounding field on the unique picture.
from PIL import Picture, ImageDraw, ImageFont
import numpy as np
import torchdef detect_and_draw_bounding_boxes(
image_path,
text_queries,
mannequin,
processor,
output_path,
score_threshold=0.2
):
"""
Detect objects in a picture and draw bounding containers over the unique picture utilizing PIL.
Parameters:
- image_path (str): Path to the picture file.
- text_queries (checklist of str): Listing of textual content queries to course of.
- mannequin: Pretrained mannequin to make use of for detection.
- processor: Processor to preprocess picture and textual content queries.
- output_path (str): Path to avoid wasting the output picture with bounding containers.
- score_threshold (float): Threshold to filter out low-confidence predictions.
Returns:
- output_image_pil: A PIL Picture object with bounding containers and labels drawn.
"""
img = Picture.open(image_path).convert("RGB")
orig_w, orig_h = img.measurement # authentic width, peak
inputs = processor(
textual content=text_queries,
photos=img,
return_tensors="pt",
padding=True,
truncation=True
).to("cpu")
mannequin.eval()
with torch.no_grad():
outputs = mannequin(**inputs)
logits = torch.max(outputs["logits"][0], dim=-1) # form (num_boxes,)
scores = torch.sigmoid(logits.values).cpu().numpy() # convert to chances
labels = logits.indices.cpu().numpy() # class indices
boxes_norm = outputs["pred_boxes"][0].cpu().numpy() # form (num_boxes, 4)
converted_boxes = []
for field in boxes_norm:
cx, cy, w, h = field
cx_abs = cx * orig_w
cy_abs = cy * orig_h
w_abs = w * orig_w
h_abs = h * orig_h
x1 = cx_abs - w_abs / 2.0
y1 = cy_abs - h_abs / 2.0
x2 = cx_abs + w_abs / 2.0
y2 = cy_abs + h_abs / 2.0
converted_boxes.append((x1, y1, x2, y2))
draw = ImageDraw.Draw(img)
for rating, (x1, y1, x2, y2), label_idx in zip(scores, converted_boxes, labels):
if rating < score_threshold:
proceed
draw.rectangle([x1, y1, x2, y2], define="crimson", width=3)
label_text = text_queries[label_idx].exchange("A picture of ", "")
text_str = f"{label_text}: {rating:.2f}"
text_size = draw.textsize(text_str) # If no font used, take away "font=font"
text_x, text_y = x1, max(0, y1 - text_size[1]) # place textual content barely above field
draw.rectangle(
[text_x, text_y, text_x + text_size[0], text_y + text_size[1]],
fill="white"
)
draw.textual content((text_x, text_y), text_str, fill="crimson") # , font=font)
img.save(output_path, "JPEG")
return img
for key, worth in tqdm(detected_objects_cleaned.objects()):
worth = ["An image of " + x for x in value]
detect_and_draw_bounding_boxes(key, worth, mannequin, processor, "images_with_bounding_boxes/" + key.cut up("/")[-1], score_threshold=0.15)
The pictures with the detected objects plotted are actually handed to the VLM for captioning:
IMAGE_QUALITY = "excessive"
image_paths_obj_detected_guided = [x.replace("downloaded_images", "images_with_bounding_boxes") for x in image_paths] system_prompt="""You're a useful assistant that may analyze photos and supply captions. You might be supplied with photos that additionally comprise bounding field annotations of the necessary objects in them, together with their labels.
Analyze the general picture and the offered bounding field info and supply an acceptable caption for the picture.""",
user_prompt="Please analyze the next picture:",
obj_det_zero_shot_high_quality_captions = process_images_in_parallel(image_paths_obj_detected_guided, mannequin = "gpt-4o-mini", few_shot_prompt= None, element=IMAGE_QUALITY, max_workers=5)
On this activity, given the easy nature of the photographs we use, the situation of the objects doesn’t add any important info to the VLM. Nevertheless, Object Detection Guided Prompting could be a highly effective device for extra complicated duties, reminiscent of Doc Understanding, the place structure info will be successfully offered via object detection to the VLM for additional processing. Moreover, Semantic Segmentation will be employed as a way to information prompting by offering segmentation masks to the VLM.
VLMs are a strong device within the arsenal of AI engineers and scientists for fixing quite a lot of issues that require a mixture of imaginative and prescient and textual content expertise. On this article, I discover prompting methods within the context of VLMs to successfully use these fashions for duties reminiscent of picture captioning. That is under no circumstances an exhaustive or complete checklist of prompting methods. One factor that has turn into more and more clear with the developments in GenAI is the limitless potential for artistic and revolutionary approaches to immediate and information LLMs and VLMs in fixing duties.
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