Spaces:
Runtime error
Runtime error
| 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 = """<h1 id="title">Object Detection App for POC</h1>""" | |
| 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) |