import gradio as gr import matplotlib.pyplot as plt from PIL import Image from ultralyticsplus import YOLO import cv2 import numpy as np from transformers import pipeline import requests from io import BytesIO import os model = YOLO('corn20epoch.pt') name = ['Corn Rust','Grey Leaf Spot','Leaf Blight'] # image_directory = "/home/user/app/image" # video_directory = "/home/user/app/video" # url_example="https://drive.google.com/file/d/1bBq0bNmJ5X83tDWCzdzHSYCdg-aUL4xO/view?usp=drive_link" # url_example='https://drive.google.com/uc?id=' + url_example.split('/')[-2] # r = requests.get(url_example) # im1 = Image.open(BytesIO(r.content)) # url_example="https://drive.google.com/file/d/16Z7QzvZ99fbEPj1sls_jOCJBsC0h_dYZ/view?usp=drive_link" # url_example='https://drive.google.com/uc?id=' + url_example.split('/')[-2] # r = requests.get(url_example) # im2 = Image.open(BytesIO(r.content)) # url_example="https://drive.google.com/file/d/13mjTMS3eR0AKYSbV-Fpb3fTBno_T42JN/view?usp=drive_link" # url_example='https://drive.google.com/uc?id=' + url_example.split('/')[-2] # r = requests.get(url_example) # im3 = Image.open(BytesIO(r.content)) # url_example="https://drive.google.com/file/d/1-XpFsa_nz506Ul6grKElVJDu_Jl3KZIF/view?usp=drive_link" # url_example='https://drive.google.com/uc?id=' + url_example.split('/')[-2] # r = requests.get(url_example) # im4 = Image.open(BytesIO(r.content)) # for i, r in enumerate(results): # # Plot results image # im_bgr = r.plot() # im_rgb = im_bgr[..., ::-1] # Convert BGR to RGB def response2(image: gr.Image = None,image_size: gr.Slider = 640, conf_threshold: gr.Slider = 0.3, iou_threshold: gr.Slider = 0.6): results = model.predict(image, conf=conf_threshold, iou=iou_threshold, imgsz=image_size) text = "" name_weap = "" box = results[0].boxes for r in results: im_array = r.plot() im = Image.fromarray(im_array[..., ::-1]) for r in results: conf = np.array(r.boxes.conf.cpu()) cls = np.array(r.boxes.cls.cpu()) cls = cls.astype(int) xywh = np.array(r.boxes.xywh.cpu()) xywh = xywh.astype(int) for con, cl, xy in zip(conf, cls, xywh): cone = con.astype(float) conef = round(cone,3) conef = conef * 100 text += (f"Detected {name[cl]} with confidence {round(conef,1)}% at ({xy[0]},{xy[1]})\n") # xywh = int(results.boxes.xywh) # x = xywh[0] # y = xywh[1] return im, text inputs = [ gr.Image(type="pil", label="Input Image"), gr.Slider(minimum=320, maximum=1280, value=640, step=32, label="Image Size"), gr.Slider(minimum=0.0, maximum=1.0, value=0.3, step=0.05, label="Confidence Threshold"), gr.Slider(minimum=0.0, maximum=1.0, value=0.6, step=0.05, label="IOU Threshold"), ] outputs = [gr.Image( type="pil", label="Output Image"), gr.Textbox(label="Result") ] # examples = [[os.path.join(image_directory, "th (5).jpg"),640, 0.3, 0.6], # [os.path.join(image_directory, "th (8).jpg"),640, 0.3, 0.6], # [os.path.join(image_directory, "th (11).jpg"),640, 0.3, 0.6], # [os.path.join(image_directory, "th (3).jpg"),640, 0.3, 0.6], # [os.path.join(image_directory, "th.jpg"),640, 0.3, 0.6] # ] title = """Corn Deseases Detection Finetuned YOLOv8

Colab """ description = 'Image Size: Defines the image size for inference.\nConfidence Treshold: Sets the minimum confidence threshold for detections.\nIOU Treshold: Intersection Over Union (IoU) threshold for Non-Maximum Suppression (NMS). Useful for reducing duplicates.' def pil_to_cv2(pil_image): open_cv_image = cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2BGR) return open_cv_image def process_video(video_path): cap = cv2.VideoCapture(video_path) while cap.isOpened(): ret, frame = cap.read() if not ret: break pil_img = Image.fromarray(frame[..., ::-1]) result = model.predict(source=pil_img) for r in result: im_array = r.plot() processed_frame = Image.fromarray(im_array[..., ::-1]) yield processed_frame cap.release() video_iface = gr.Interface( fn=process_video, inputs=[ gr.Video(label="Upload Video", interactive=True) ], outputs=gr.Image(type="pil",label="Result"), title=title, description="Upload video for inference.", # examples=[[os.path.join(video_directory, "ExampleRifle.mp4")], # [os.path.join(video_directory, "Knife.mp4")], # ] ) image_iface = gr.Interface(fn=response2, inputs=inputs, outputs=outputs, # examples=examples title=title, description=description) demo = gr.TabbedInterface([image_iface, video_iface], ["Image Inference", "Video Inference"]) if __name__ == '__main__': demo.launch()