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| from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation | |
| #from datasets import load_dataset | |
| from transformers import pipeline | |
| from PIL import Image, ImageDraw, ImageFont | |
| import gradio as gr | |
| #ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test") | |
| def object_classify(img1,img2): | |
| feature_extractor = SegformerFeatureExtractor.from_pretrained("nvidia/segformer-b2-finetuned-ade-512-512") | |
| model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b2-finetuned-ade-512-512") | |
| object_detector = pipeline(task="image-segmentation", model = model, feature_extractor = feature_extractor) | |
| #list of dictionaries | |
| dict_obj1 = object_detector(img1) | |
| dict_obj2 = object_detector(img2) | |
| #list of object labels present in the image | |
| objects_1=[] | |
| objects_2=[] | |
| #gets the label from each dictionary | |
| for i in dict_obj1: | |
| objects_1.append(i['label']) | |
| for j in dict_obj2: | |
| objects_2.append(j['label']) | |
| #gets the uncommon elements from the 2 lists | |
| missing_objects= list(set(objects_1)-set(objects_2)) | |
| missing_objects= *missing_objects, sep = ", " | |
| return missing_objects | |
| TITLE = 'Missing Items' | |
| DESCRIPTION = 'Input two indoor pictures. First image being the original and second is one with the missing item/s' | |
| EXAMPLES = [['Bedroom_1.jpg'],['Bedroom_2.jpg']] | |
| INPUTS=[gr.inputs.Image(type = 'pil', label='Original Image'),gr.inputs.Image(type = 'pil', label='Second Image')] | |
| OUTPUTS=gr.outputs.Textbox("Missing Item/s is/are: ") | |
| interface=gr.Interface(object_classify, | |
| INPUTS, | |
| OUTPUTS, | |
| examples = EXAMPLES, | |
| title = TITLE, | |
| description=DESCRIPTION, allow_flagging="never") | |
| interface.launch() |