app.py

import os
import sys
import argparse
import time
from pathlib import Path
import pandas as pd

import gradio as gr
import cv2
from PIL import Image
import torch
import torch.backends.cudnn as cudnn
from numpy import random

BASE_DIR = "/home/user/app"
os.chdir(BASE_DIR)
os.makedirs(f"{BASE_DIR}/input",exist_ok=True)
os.system(f"git clone https://github.com/WongKinYiu/yolov7.git {BASE_DIR}/yolov7")
sys.path.append(f'{BASE_DIR}/yolov7')

def detect(opt, save_img=False):    
    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
    
    bbox = {}
    source, weights, view_img, save_txt, imgsz, trace = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size, not opt.no_trace
    save_img = not opt.nosave and not source.endswith('.txt')  # save inference images
    webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
        ('rtsp://', 'rtmp://', 'http://', 'https://'))

    # Directories
    save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))  # increment run
    (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir

    # Initialize
    set_logging()
    device = select_device(opt.device)
    half = device.type != 'cpu'  # half precision only supported on CUDA

    # Load model
    model = attempt_load(weights, map_location=device)  # load FP32 model
    stride = int(model.stride.max())  # model stride
    imgsz = check_img_size(imgsz, s=stride)  # check img_size

    if trace:
        model = TracedModel(model, device, opt.img_size)

    if half:
        model.half()  # to FP16

    # Second-stage classifier
    classify = False
    if classify:
        modelc = load_classifier(name='resnet101', n=2)  # initialize
        modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()

    # Set Dataloader
    vid_path, vid_writer = None, None
    if webcam:
        view_img = check_imshow()
        cudnn.benchmark = True  # set True to speed up constant image size inference
        dataset = LoadStreams(source, img_size=imgsz, stride=stride)
    else:
        dataset = LoadImages(source, img_size=imgsz, stride=stride)

    # Get names and colors
    names = model.module.names if hasattr(model, 'module') else model.names
    colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]

    # Run inference
    if device.type != 'cpu':
        model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once
    old_img_w = old_img_h = imgsz
    old_img_b = 1

    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()  # uint8 to fp16/32
        img /= 255.0  # 0 - 255 to 0.0 - 1.0
        if img.ndimension() == 3:
            img = img.unsqueeze(0)

        # Warmup
        if device.type != 'cpu' and (old_img_b != img.shape[0] or old_img_h != img.shape[2] or old_img_w != img.shape[3]):
            old_img_b = img.shape[0]
            old_img_h = img.shape[2]
            old_img_w = img.shape[3]
            for i in range(3):
                model(img, augment=opt.augment)[0]

        # Inference
        t1 = time_synchronized()
        with torch.no_grad():   # Calculating gradients would cause a GPU memory leak
            pred = model(img, augment=opt.augment)[0]
        t2 = time_synchronized()

        # Apply NMS
        pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
        t3 = time_synchronized()

        # Apply Classifier
        if classify:
            pred = apply_classifier(pred, modelc, img, im0s)

        # Process detections
        for i, det in enumerate(pred):  # detections per image
            if webcam:  # batch_size >= 1
                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)  # to Path
            save_path = str(save_dir / p.name)  # img.jpg
            txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # img.txt
            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
            if len(det):
                # Rescale boxes from img_size to im0 size
                det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
#                 print(f"BOXES ---->>>> {det[:, :4]}")
                bbox[f"{txt_path.split('/')[4]}"]=(det[:, :4]).numpy()

               # Print results
                for c in det[:, -1].unique():
                    n = (det[:, -1] == c).sum()  # detections per class
                    s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string

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

                    if save_img or view_img:  # Add bbox to image
                        label = f'{names[int(cls)]} {conf:.2f}'
                        plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=1)

            # Print time (inference + NMS)
            print(f'{s}Done. ({(1E3 * (t2 - t1)):.1f}ms) Inference, ({(1E3 * (t3 - t2)):.1f}ms) NMS')

            # Stream results
            # if view_img:
            #     cv2.imshow(str(p), im0)
            #     cv2.waitKey(1)  # 1 millisecond

            # Save results (image with detections)
            if save_img:
                if dataset.mode == 'image':
                    # Image.fromarray(im0).show()
                    cv2.imwrite(save_path, im0) 
                    print(f" The image with the result is saved in: {save_path}")
                # else:  # 'video' or 'stream'
                #     if vid_path != save_path:  # new video
                #         vid_path = save_path
                #         if isinstance(vid_writer, cv2.VideoWriter):
                #             vid_writer.release()  # release previous video writer
                #         if vid_cap:  # video
                #             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:  # stream
                #             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"Results saved to {save_dir}{s}")

    print(f'Done. ({time.time() - t0:.3f}s)')
    return bbox,save_path

class options:
    def __init__(self, weights, source, img_size=640, conf_thres=0.56, iou_thres=0.4, device='', 
                 view_img=False, save_txt=False, save_conf=False, nosave=False, classes=None, 
                 agnostic_nms=False, augment=False, update=False, project='runs/detect', name='exp', 
                 exist_ok=False, no_trace=False):
        self.weights=weights
        self.source=source
        self.img_size=img_size
        self.conf_thres=conf_thres
        self.iou_thres=iou_thres
        self.device=device
        self.view_img=view_img
        self.save_txt=save_txt
        self.save_conf=save_conf
        self.nosave=nosave
        self.classes=classes
        self.agnostic_nms=agnostic_nms
        self.augment=augment
        self.update=update
        self.project=project
        self.name=name
        self.exist_ok=exist_ok
        self.no_trace=no_trace

def get_output(image):
    image.save(f"{BASE_DIR}/input/image.jpg")
    source = f"{BASE_DIR}/input"
    opt = options(weights='best.pt',source=source)
    bbox = None
    with torch.no_grad():
        # if opt.update:  # update all models (to fix SourceChangeWarning)
        #     for opt.weights in ['yolov7.pt']:
        #         bbox,output_path = detect(opt)
        #         strip_optimizer(opt.weights)
        # else:
        bbox,output_path = detect(opt)
    if os.path.exists(output_path):
        return Image.open(output_path)
    else:
        return image

gr.Interface(fn=get_output, 
         inputs=gr.Image(type = "pil", label="Your image"),
         outputs="image"
         ).launch(debug=True)