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# 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) |