import io import gradio as gr import matplotlib.pyplot as plt import requests, validators import torch import pathlib from PIL import Image from transformers import AutoFeatureExtractor, DetrForObjectDetection, YolosForObjectDetection import os # colors for visualization COLORS = [ [0.000, 0.447, 0.741], [0.850, 0.325, 0.098], [0.929, 0.694, 0.125], [0.494, 0.184, 0.556], [0.466, 0.674, 0.188], [0.301, 0.745, 0.933] ] def make_prediction(img, feature_extractor, model): inputs = feature_extractor(img, return_tensors="pt") outputs = model(**inputs) img_size = torch.tensor([tuple(reversed(img.size))]) processed_outputs = feature_extractor.post_process(outputs, img_size) return processed_outputs[0] def fig2img(fig): buf = io.BytesIO() fig.savefig(buf) buf.seek(0) img = Image.open(buf) return img def visualize_prediction(pil_img, output_dict, threshold=0.8, id2label=None): keep = output_dict["scores"] > threshold boxes = output_dict["boxes"][keep].tolist() scores = output_dict["scores"][keep].tolist() labels = output_dict["labels"][keep].tolist() if id2label is not None: labels = [id2label[x] for x in labels] plt.figure(figsize=(16, 10)) plt.imshow(pil_img) ax = plt.gca() colors = COLORS * 100 for score, (xmin, ymin, xmax, ymax), label, color in zip(scores, boxes, labels, colors): ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, fill=False, color=color, linewidth=3)) ax.text(xmin, ymin, f"{label}: {score:0.2f}", fontsize=15, bbox=dict(facecolor="yellow", alpha=0.5)) plt.axis("off") return fig2img(plt.gcf()) def detect_objects(model_name,url_input,image_input,threshold): #Extract model and feature extractor feature_extractor = AutoFeatureExtractor.from_pretrained(model_name) model = DetrForObjectDetection.from_pretrained(model_name) image = image_input #Make prediction processed_outputs = make_prediction(image, feature_extractor, model) print(processed_outputs) #Visualize prediction viz_img = visualize_prediction(image, processed_outputs, threshold, model.config.id2label) return viz_img xxresult=0 def detect_objects2(model_name,url_input,image_input,threshold,type2): #Extract model and feature extractor feature_extractor = AutoFeatureExtractor.from_pretrained(model_name) model = DetrForObjectDetection.from_pretrained(model_name) image = image_input #Make prediction processed_outputs = make_prediction(image, feature_extractor, model) print(processed_outputs) #Visualize prediction viz_img = visualize_prediction(image, processed_outputs, threshold, model.config.id2label) keep = processed_outputs["scores"] > threshold det_lab = processed_outputs["labels"][keep].tolist() det_lab.count(6) if det_lab.count(6) > 0: total_text="Trench is Detected \n Image is Not Blurry \n" else: total_text="Trench is NOT Detected \n Image is Blurry \n" print(type2) print(type(type2)) if det_lab.count(4) > 0: total_text+="Measuring Tape (Vertical) for measuring Depth is Detected \n" else: total_text+="Measuring Tape (Vertical) for measuring Depth is NOT Detected \n" if det_lab.count(5) > 0: total_text+="Measuring Tape (Horizontal) for measuring Width is Detected \n" else: total_text+="Measuring Tape (Horizontal) for measuring Width is NOT Detected \n" return total_text def tott(model_name,url_input,image_input,threshold,type2): #Extract model and feature extractor feature_extractor = AutoFeatureExtractor.from_pretrained(model_name) model = DetrForObjectDetection.from_pretrained(model_name) image = image_input #Make prediction processed_outputs = make_prediction(image, feature_extractor, model) keep = processed_outputs["scores"] > threshold det_lab = processed_outputs["labels"][keep].tolist() xxresult=0 if det_lab.count(6) == 0: xxresult=1 if det_lab.count(4) == 0: if type2=="Trench Depth Measurement": xxresult=1 if det_lab.count(5) == 0: if type2=="Trench Width Measurement": xxresult=1 if xxresult==0: return "The photo is ACCEPTED" else: return "The photo is NOT ACCEPTED" def set_example_image(example: list) -> dict: return gr.Image.update(value=example[0]) def set_example_url(example: list) -> dict: return gr.Textbox.update(value=example[0]) title = """

Object Detection App for POC

""" description = """ This application can be used as follows: - Select the model - Select the type of classification - Select the photo - Press Detect - Press Results """ models = ["omarhkh/CutLER-finetuned-11" ,"omarhkh/CutLER-finetuned-12","omarhkh/detr-finetuned-omar8" , "omarhkh/CutLER-finetuned-omar3"] types_class = ["Trench Depth Measurement", "Trench Width Measurement"] css = ''' h1#title { text-align: center; } ''' demo = gr.Blocks(css=css) with demo: gr.Markdown(title) gr.Markdown(description) #gr.Markdown(detect_objects2) options = gr.Dropdown(value="omarhkh/CutLER-finetuned-11",choices=models,label='Select Object Detection Model',show_label=True) options2 = gr.Dropdown(value="Trench Depth Measurement",choices=types_class,label='Select Classification Type',show_label=True) slider_input = gr.Slider(minimum=0.1,maximum=1,value=0.8,label='Prediction Threshold') with gr.Tabs(): with gr.TabItem('Image Upload'): with gr.Row(): img_input = gr.Image(type='pil') img_output_from_upload= gr.Image(shape=(650,650)) with gr.Row(): example_images = gr.Dataset(components=[img_input], samples=[[path.as_posix()] for path in sorted(pathlib.Path('images').rglob('*.jpg'))]) img_but = gr.Button('Detect') with gr.Blocks(): name = gr.Textbox(label="Final Result") output = gr.Textbox(label="Reason for the results") greet_btn = gr.Button("Results") greet_btn.click(fn=detect_objects2, inputs=[options,img_input,img_input,slider_input,options2], outputs=output, queue=True) greet_btn.click(fn=tott, inputs=[options,img_input,img_input,slider_input,options2], outputs=name, queue=True) img_but.click(detect_objects,inputs=[options,img_input,img_input,slider_input],outputs=img_output_from_upload,queue=True) example_images.click(fn=set_example_image,inputs=[example_images],outputs=[img_input]) demo.launch(enable_queue=True)