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
import numpy as np

# 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=0.1, iou_thres=0.45, 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(input_image):
    # image.save(f"{BASE_DIR}/input/image.jpg")
    # source = f"{BASE_DIR}/input"
    # opt = options(weights='logo_detection.pt',source=source)
    # bbox = None
    # with torch.no_grad():
    #     bbox,output_path = detect(opt)
    # if os.path.exists(output_path):
    #     return np.array(Image.open(output_path))
    # else:
    return input_image


demo = gr.Interface(fn=get_output, inputs="image", outputs="image")
demo.launch(debug=True)