clipcrop 2.4.6

Extract sections from your image by using OpenAI CLIP and Facebooks Detr implemented on HuggingFace Transformers

clipcrop

• Extract sections of images from your image by using OpenAI's CLIP and YoloSmall implemented on HuggingFace Transformers
• Added new capability for segmentation using CLIP and Detr segmentation models

Installation

pip install clipcrop

Clip Crop

Extract sections of images from your image by using OpenAI's CLIP and YoloSmall implemented on HuggingFace Transformers

Implementation

from clipcrop import clipcrop
cc = clipcrop.ClipCrop("/content/sample.jpg")
DFE, DM, CLIPM, CLIPP = cc.load_models()
result = cc.extract_image(DFE, DM, CLIPM, CLIPP, "text content", num=2)
# gives a list of dicitonary of top images and its relative similarity score and you can override this by setting num = 5  to get top 5 etc while initiating the class

Captcha

Solve captacha images using CLIP and Object detection models.

from clipcrop import clipcrop
# second arguement is the text prompt eg:image of cars
cc = clipcrop.ClipCrop(image_path)
#loading models, processors, feature extractors
DFE, DM, CLIPM, CLIPP = cc.load_models()
#generally keep high threshold to avoid noises
result = cc.captcha(CLIPM, CLIPP, 4)

Clip Segmentation

Segment out images using Detr Panoptic segmentation pipeline and leverage CLIP models to derive the most probable one for your query

Implementation

from clipcrop import clipcrop
clipseg = clipcrop.ClipSeg("/content/input.png", "black colored car")
segmentor, clipmodel, clipprocessor = clipseg.load_models()
result = clipseg.segment_image(segmentor, clipmodel, clipprocessor)
# gives a list of dicitonary of top images and its relative similarity score and you can override this by setting num = 5  to get top 5 etc

Remove Background

from clipcrop import clipcrop
clipseg = clipcrop.ClipSeg("/content/input.png", "black colored car")
result = clipseg.remove_background()

Other projects

• SnapCode : Extract code blocks from images mixed with normal text
• HuggingFaceInference: Inference of different uses cases of finetued models