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from transformers import BlipProcessor, BlipForConditionalGeneration, DetrImageProcessor, DetrForObjectDetection
from PIL import Image
import torch
def get_image_caption(image_path):
"""
Generates a short caption for the provided image.
Args:
image_path (str): The path to the image file.
Returns:
str: A string representing the caption for the image.
"""
image = Image.open(image_path).convert('RGB')
model_name = "Salesforce/blip-image-captioning-large"
device = "cpu" # cuda
processor = BlipProcessor.from_pretrained(model_name)
model = BlipForConditionalGeneration.from_pretrained(model_name).to(device)
inputs = processor(image, return_tensors='pt').to(device)
output = model.generate(**inputs, max_new_tokens=20)
caption = processor.decode(output[0], skip_special_tokens=True)
return caption
def detect_objects(image_path):
"""
Detects objects in the provided image.
Args:
image_path (str): The path to the image file.
Returns:
str: A string with all the detected objects. Each object as '[x1, x2, y1, y2, class_name, confindence_score]'.
"""
image = Image.open(image_path).convert('RGB')
processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
# convert outputs (bounding boxes and class logits) to COCO API
# let's only keep detections with score > 0.9
target_sizes = torch.tensor([image.size[::-1]])
results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
detections = ""
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
detections += '[{}, {}, {}, {}]'.format(int(box[0]), int(box[1]), int(box[2]), int(box[3]))
detections += ' {}'.format(model.config.id2label[int(label)])
detections += ' {}\n'.format(float(score))
return detections
if __name__ == '__main__':
image_path = '/home/phillip/Desktop/todays_tutorial/52_langchain_ask_questions_video/code/test.jpg'
detections = detect_objects(image_path)
print(detections)
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