Create app.py

app.py

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+import gradio as gr
+import numpy as np
+from PIL import Image
+from transformers import CLIPProcessor, CLIPModel, DetrFeatureExtractor, DetrForObjectDetection, AutoFeatureExtractor, AutoModelForObjectDetection
+import torch
+
+feature_extractor = AutoFeatureExtractor.from_pretrained("nielsr/detr-resnet-50")
+dmodel = AutoModelForObjectDetection.from_pretrained("nielsr/detr-resnet-50")
+
+model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+
+i1 = gr.inputs.Image(type="pil", label="Input image")
+i2 = gr.inputs.Textbox(label="Input text")
+i3 = gr.inputs.Number(default=0.96, label="Threshold percentage score")
+o1 = gr.outputs.Image(type="pil", label="Cropped part")
+o2 = gr.outputs.Textbox(label="Similarity score")
+
+def extract_image(image, text, prob, num=1):
+    
+    inputs = feature_extractor(images=image, return_tensors="pt")
+    outputs = dmodel(**inputs)
+    
+    # model predicts bounding boxes and corresponding COCO classes
+    logits = outputs.logits
+    bboxes = outputs.pred_boxes
+    probas = outputs.logits.softmax(-1)[0, :, :-1] #removing no class as detr maps 
+    
+    keep = probas.max(-1).values > prob
+    outs = feature_extractor.post_process(outputs, torch.tensor(image.size[::-1]).unsqueeze(0))
+    bboxes_scaled = outs[0]['boxes'][keep].detach().numpy()
+    labels = outs[0]['labels'][keep].detach().numpy()
+    scores = outs[0]['scores'][keep].detach().numpy()
+    
+    images_list = []
+    for i,j in enumerate(bboxes_scaled):
+      
+      xmin = int(j[0])
+      ymin = int(j[1])
+      xmax = int(j[2])
+      ymax = int(j[3])
+    
+      im_arr = np.array(image)
+      roi = im_arr[ymin:ymax, xmin:xmax]
+      roi_im = Image.fromarray(roi)
+    
+      images_list.append(roi_im)
+    
+    inpu = processor(text = [text], images=images_list , return_tensors="pt", padding=True)
+    output = model(**inpu)
+    logits_per_image = output.logits_per_text
+    probs = logits_per_image.softmax(-1)
+    l_idx = np.argsort(probs[-1].detach().numpy())[::-1][0:num]
+    
+    final_ims = []
+    for i,j in enumerate(images_list):
+      json_dict = {}
+      if i in l_idx:
+        json_dict['image'] = images_list[i]
+        json_dict['score'] = probs[-1].detach().numpy()[i]
+    
+        final_ims.append(json_dict)
+    
+    fi = sorted(final_ims, key=lambda item: item.get("score"), reverse=True)
+    return fi[0]['image'], fi[0]['score']
+
+title = "ClipnCrop"
+description = "Extract sections of images from your image by using OpenAI's CLIP and Facebooks Detr implemented on HuggingFace Transformers" 
+examples=[['ex3.jpg', 'black bag', 0.96],['ex2.jpg', 'man in red dress', 0.85]]
+article = "<p style='text-align: center'><a href='https://github.com/Vishnunkumar/clipcrop' target='_blank'>clipcrop</a></p>"
+gr.Interface(fn=extract_image, inputs=[i1, i2, i3], outputs=[o1, o2], title=title, description=description, article=article, examples=examples, enable_queue=True).launch()