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| #app7.py | |
| import gradio as gr | |
| import torch | |
| from PIL import Image | |
| from torchvision import transforms | |
| from ultralyticsplus import YOLO, render_result | |
| # import matplotlib.pyplot as plt | |
| import numpy as np | |
| torch.hub.download_url_to_file( | |
| 'https://i.postimg.cc/g2xGJ4Qs/NSTA-Test-IMG-3276.jpg', 'NSTA.jpg') | |
| torch.hub.download_url_to_file( | |
| 'https://i.postimg.cc/BZCSwj2T/NSTB-Test-IMG-1472.jpg', 'NSTB.jpg') | |
| torch.hub.download_url_to_file( | |
| 'https://i.postimg.cc/yYY1q7Tw/NSTC-Test-IMG-0118.jpg', 'NSTC.jpg') | |
| torch.hub.download_url_to_file( | |
| 'https://i.postimg.cc/zD9ZQX6z/KCCA-Test-IMG-3555.jpg', 'KCCA.jpg') | |
| torch.hub.download_url_to_file( | |
| 'https://i.postimg.cc/vZLPXP7L/KCCB-Test-IMG-3733.jpg', 'KCCB.jpg') | |
| torch.hub.download_url_to_file( | |
| 'https://i.postimg.cc/BZFYqFmF/KCCC-Test-IMG-3892.jpg', 'KCCC.jpg') | |
| def detect_objects(image_path, selected_model): | |
| # Open the image file and resize it | |
| image = Image.open(image_path) | |
| resized_image = image.resize((1024, 768)) | |
| #default | |
| # model_path = ('MvitHYF/v8mvitcocoaseed2024') | |
| # Load the model | |
| nstcurrentmodel = "NST Model" | |
| kcccurrentmodel = "KCC Model" | |
| currentmodel = str(selected_model) | |
| nstcurrentmodel = "NST Model" | |
| if currentmodel == nstcurrentmodel: | |
| print("this is nst model") | |
| #best of NST Model | |
| #model_path = ('code/runs/train45/best.pt') | |
| model_path = ('MvitHYF/v8mvitcocoaseed2024') | |
| elif currentmodel == kcccurrentmodel: | |
| print("this is kcc model") | |
| #model_path = ('code/runs/kcc/v8/train83/best.pt') | |
| model_path = ('MvitHYF/kccv8mvitcocoaseed2024') | |
| #model_path = ('code/runs/train45/best.pt') | |
| # model_path = ('MvitHYF/v8mvitcocoaseed2024') | |
| model = YOLO(model_path) | |
| # model = YOLO('MvitHYF/v8mvitcocoaseed2024') | |
| # Set model parameters | |
| model.overrides['conf'] = 'null' # NMS confidence threshold | |
| model.overrides['iou'] = 0.70 # NMS IoU threshold | |
| model.overrides['agnostic_nms'] = True # NMS class-agnostic | |
| model.overrides['max_det'] = 1000 # maximum number of detections per image | |
| # Perform inference | |
| results = model.predict(resized_image) | |
| #debug check count | |
| # print("see") | |
| # print(results) | |
| cls = results[0].boxes.cls | |
| # print(cls) | |
| strcls = str(cls) | |
| # print(type(strcls)) | |
| # print(strcls) | |
| count_classa = strcls.count('0') | |
| # print('Count of classA:', count_classa) | |
| count_classb = strcls.count('1') | |
| # print('Count of classB', count_classb) | |
| count_classc = strcls.count('2') | |
| # print('Count of classC:', count_classc) | |
| intcount_classa = int(count_classa) | |
| intcount_classb = int(count_classb) | |
| intcount_classc = int(count_classc) | |
| total = intcount_classa + intcount_classb + intcount_classc | |
| # print("end see") | |
| # gr.Image(label="Pie Graph") | |
| # Format the output to print the counts | |
| output_counts = f"Totoal cocoa seeds: {total}\nClass A: {count_classa} seeds\nClass B: {count_classb} seeds\nClass C: {count_classc} seeds" | |
| # Render results | |
| render = render_result(model=model, image=resized_image, result=results[0]) | |
| print("Selected model:", selected_model) | |
| #return render, output_counts, plotbar | |
| return render, output_counts, "You have selected the " + str(selected_model) | |
| #csspath = 'code/yolov8newultlt/gradio.css' | |
| with gr.Blocks(theme='ParityError/LimeFace') as demo: | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Interface(fn=detect_objects, | |
| inputs=[gr.Image(type="filepath", label="Upload an Image"), gr.Dropdown(choices=["NST Model", "KCC Model"])], | |
| outputs=[gr.Image(type="filepath", label="Result"), gr.Textbox(label="Detection Counts"), gr.Textbox(label="Selected Model")], | |
| title="YOLOv8 Cocoa Seed Classification", | |
| description="Upload an image to detect objects using YOLO.", | |
| #html = gr.HTML(value="<p>This is another paragraph123.</p>"), | |
| examples = [["NSTA.jpg"], | |
| ["NSTB.jpg"], | |
| ["NSTC.jpg"], | |
| ["KCCA.jpg"], | |
| ["KCCB.jpg"], | |
| ["KCCC.jpg"]], | |
| cache_examples = bool(False) | |
| #css=csspath, | |
| ) | |
| # with gr.Row(): #original Column | |
| # with gr.Row(): #original Column | |
| # example = [[gr.Image(value="NSTA.jpg", interactive = bool(True)), gr.Markdown(value='**label 5**')], | |
| # [gr.Image(value="NSTB.jpg", interactive = bool(True)), gr.Markdown(value='**label 5**')], | |
| # [gr.Image(value="NSTC.jpg", interactive = bool(True)), gr.Markdown(value='**label 5**')], | |
| # [gr.Image(value="KCCA.jpg", interactive = bool(True)), gr.Markdown(value='**label 5**')], | |
| # [gr.Image(value="KCCB.jpg", interactive = bool(True)), gr.Markdown(value='**label 5**')], | |
| # [gr.Image(value="KCCC.jpg", interactive = bool(True)), gr.Markdown(value='**label 5**')]] | |
| with gr.Row(): | |
| with gr.Row(): | |
| # gr.HTML(value="<b>Class A</b> <p>Class A is the best from all 3 classes. It have the best of physical appreance eg. shape, size, texture</p>"), | |
| gr.HTML(value="<p> NSTA NSTB NTSC KCCA KCCB KCCC</p> <dl> <dt><b>Class A</b></dt> <dd>Class A is the best from all 3 classes. It have the best of physical appreance eg. shape, size, texture</dd> </dl> <dt><b>Class B</b></dt> <dd>Class B most of the cocoa seed have physical appreance similar to class A. <br> But the size must me smaller and texture is not smmoth as class A</dd> <dt><b>Class C</b></dt> <dd>Class C is the worst from all 3 classes. Its the smallest, rough texter and have a irregular shape </dd> </dl></dl>") | |
| if __name__ == "__main__": | |
| demo.queue().launch(share=True) | |