app.py

import os
import cv2
import time
import torch
import argparse
import gradio as gr
from PIL import Image
from numpy import random
from pathlib import Path
import torch.backends.cudnn as cudnn
from models.experimental import attempt_load

from utils.datasets import LoadStreams, LoadImages
from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \
    scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path
from utils.plots import plot_one_box
from utils.torch_utils import select_device, load_classifier, time_synchronized, TracedModel
os.system("wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt")
os.system("wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6.pt")

def detect_Custom(img,model):
    if model =='Yolo_v7_Custom_trained_By_Owais':
        model='best' # Naming Convention for yolov7 See output file of https://www.kaggle.com/code/owaiskhan9654/training-yolov7-on-kaggle-on-custom-dataset/data
    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', nargs='+', type=str, default=model+".pt", help='model.pt path(s)')
    parser.add_argument('--source', type=str, default='Inference/', help='source') 
    parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
    parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')
    parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
    parser.add_argument('--view-img', action='store_true', help='display results')
    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
    parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
    parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
    parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
    parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
    parser.add_argument('--augment', action='store_true', help='augmented inference')
    parser.add_argument('--update', action='store_true', help='update all models')
    parser.add_argument('--project', default='runs/detect', help='save results to project/name')
    parser.add_argument('--name', default='exp', help='save results to project/name')
    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
    parser.add_argument('--trace', action='store_true', help='trace model')
    opt = parser.parse_args()
    img.save("Inference/test.jpg")
    source, weights, view_img, save_txt, imgsz, trace = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size, opt.trace
    save_img = True 
    webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
        ('rtsp://', 'rtmp://', 'http://', 'https://'))
    save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) 
    (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  
    set_logging()
    device = select_device(opt.device)
    half = device.type != 'cpu' 
    model = attempt_load(weights, map_location=device) 
    stride = int(model.stride.max())  
    imgsz = check_img_size(imgsz, s=stride)
    if trace:
        model = TracedModel(model, device, opt.img_size)
    if half:
        model.half() 
        
    classify = False
    if classify:
        modelc = load_classifier(name='resnet101', n=2) 
        modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()
    vid_path, vid_writer = None, None
    if webcam:
        view_img = check_imshow()
        cudnn.benchmark = True 
        dataset = LoadStreams(source, img_size=imgsz, stride=stride)
    else:
        dataset = LoadImages(source, img_size=imgsz, stride=stride)
    names = model.module.names if hasattr(model, 'module') else model.names
    colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
    if device.type != 'cpu':
        model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) 
    t0 = time.time()
    for path, img, im0s, vid_cap in dataset:
        img = torch.from_numpy(img).to(device)
        img = img.half() if half else img.float() 
        img /= 255.0  
        if img.ndimension() == 3:
            img = img.unsqueeze(0)

        # Inference
        t1 = time_synchronized()
        pred = model(img, augment=opt.augment)[0]


        pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
        t2 = time_synchronized()
        
        
        if classify:
            pred = apply_classifier(pred, modelc, img, im0s)
    
        for i, det in enumerate(pred): 
            if webcam: 
                p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count
            else:
                p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)

            p = Path(p)  
            save_path = str(save_dir / p.name)
            txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # img.txt
            s += '%gx%g ' % img.shape[2:]  
            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  
            if len(det):
                det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()

                
                for c in det[:, -1].unique():
                    n = (det[:, -1] == c).sum()  
                    s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  

                
                for *xyxy, conf, cls in reversed(det):
                    if save_txt:  
                        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() 
                        line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) 
                        with open(txt_path + '.txt', 'a') as f:
                            f.write(('%g ' * len(line)).rstrip() % line + '\n')

                    if save_img or view_img:
                        label = f'{names[int(cls)]} {conf:.2f}'
                        plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
            if view_img:
                cv2.imshow(str(p), im0)
                cv2.waitKey(1)  

            if save_img:
                if dataset.mode == 'image':
                    cv2.imwrite(save_path, im0)
                else: 
                    if vid_path != save_path: 
                        vid_path = save_path
                        if isinstance(vid_writer, cv2.VideoWriter):
                            vid_writer.release()
                        if vid_cap: 
                            fps = vid_cap.get(cv2.CAP_PROP_FPS)
                            w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
                            h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                        else: 
                            fps, w, h = 30, im0.shape[1], im0.shape[0]
                            save_path += '.mp4'
                        vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
                    vid_writer.write(im0)

    if save_txt or save_img:
        s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''

    print(f'Done. ({time.time() - t0:.3f}s)')

    return Image.fromarray(im0[:,:,::-1])

   

Custom_description="<center>Custom Training Performed on Kaggle <a href='https://www.kaggle.com/code/owaiskhan9654/training-yolov7-on-kaggle-on-custom-dataset/notebook' style='text-decoration: underline' target='_blank'>Link</a> </center><br> <center>Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors </center> <br> <b>1st</b> class is for Person Detected<br><b>2nd</b> class is for Car Detected"

Footer = (
    "<center>Model Trained by: Owais Ahmad Data Scientist at <b> Thoucentric </b> <a href=\"https://www.linkedin.com/in/owaiskhan9654/\">Visit Profile</a> <br></center>"
    
    "<center> Model Trained Kaggle Kernel <a href=\"https://www.kaggle.com/code/owaiskhan9654/training-yolov7-on-kaggle-on-custom-dataset/notebook\">Link</a> <br></center>"
        
    "<center> Kaggle Profile <a href=\"https://www.kaggle.com/owaiskhan9654\">Link</a> <br> </center>"
    
    "<center> HuggingFace🤗 Model Deployed Repository <a href=\"https://huggingface.co/owaiskha9654/Yolov7_Custom_Object_Detection\">Link</a> <br></center>"
)

examples1=[["Image1.jpeg", "Yolo_v7_Custom_trained_By_Owais"],["Image2.jpeg", "Yolo_v7_Custom_trained_By_Owais"],["Image3.jpeg", "Yolo_v7_Custom_trained_By_Owais",],["Image4.jpeg", "Yolo_v7_Custom_trained_By_Owais"],["Image5.jpeg", "Yolo_v7_Custom_trained_By_Owais"],["Image6.jpeg", "Yolo_v7_Custom_trained_By_Owais"],["horses.jpeg", "yolov7"],["horses.jpeg", "yolov7-e6"]]

Top_Title="<center>Yolov7 🚀 Custom Trained by <a href='https://www.linkedin.com/in/owaiskhan9654/' style='text-decoration: underline' target='_blank'>Owais Ahmad </center></a>🚗Car and 👦Person Detection Class"

css = ".output_image {height: 40rem !important; width: 100% !important;}"

gr.Interface(detect_Custom,[gr.Image(type="pil"),gr.Dropdown(default="Yolo_v7_Custom_trained_By_Owais",choices=["Yolo_v7_Custom_trained_By_Owais","yolov7","yolov7-e6"])],gr.Image(type="pil"),css=css,title=Top_Title,examples=examples1,description=Custom_description,article=Footer,cache_examples=False).launch()