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| import gradio as gr | |
| import argparse | |
| import time | |
| from pathlib import Path | |
| import torch | |
| import torch.backends.cudnn as cudnn | |
| from numpy import random | |
| from models.experimental import attempt_load | |
| from utils.datasets import LoadStreams, LoadImages | |
| from utils.general import ( | |
| check_img_size, | |
| non_max_suppression, | |
| apply_classifier, | |
| scale_coords, | |
| xyxy2xywh, | |
| set_logging, | |
| increment_path, | |
| ) | |
| from utils.plots import plot_one_box | |
| from utils.torch_utils import ( | |
| select_device, | |
| load_classifier, | |
| TracedModel, | |
| ) | |
| from PIL import Image | |
| from huggingface_hub import hf_hub_download | |
| def load_model(model_name): | |
| model_path = hf_hub_download( | |
| repo_id=f"Yolov7/{model_name}", filename=f"{model_name}.pt" | |
| ) | |
| return model_path | |
| loaded_model = load_model("yolov7") | |
| def detect(img): | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument( | |
| "--weights", nargs="+", type=str, default=loaded_model, 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 # save inference images | |
| # 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 | |
| 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 | |
| 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) | |
| # Inference | |
| pred = model(img, augment=opt.augment)[0] | |
| # Apply NMS | |
| pred = non_max_suppression( | |
| pred, | |
| opt.conf_thres, | |
| opt.iou_thres, | |
| classes=opt.classes, | |
| agnostic=opt.agnostic_nms, | |
| ) | |
| # Apply Classifier | |
| if classify: | |
| pred = apply_classifier(pred, modelc, img, im0s) | |
| # Process detections | |
| for i, det in enumerate(pred): # detections per image | |
| p, s, im0, frame = path, "", im0s, getattr(dataset, "frame", 0) | |
| p = Path(p) # to Path | |
| 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 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=3, | |
| ) | |
| print(f"Done. ({time.time() - t0:.3f}s)") | |
| return [Image.fromarray(im0[:, :, ::-1]), s] | |
| css_code = ".border{border-width: 0;}.gr-button-primary{--tw-gradient-stops: rgb(11 143 235 / 70%), rgb(192 53 208 / 80%);color:black;border-color:black;}.gr-button-secondary{color:black;border-color:black;--tw-gradient-stops: white;}.gr-panel{background-color: white;}.gr-text-input{border-width: 0;padding: 0;text-align: center;margin-left: -8px;font-size: 28px;color: black;margin-top: -12px;}.font-semibold,.shadow-sm,.h-5,.text-xl,.text-xs{display:none;}.gr-box{box-shadow:none;border-radius:0;}.object-contain{background-color: white;}.gr-prose h1{font-family: Helvetica; font-weight: 400 !important;}" | |
| gr.Interface( | |
| fn=detect, | |
| title="Anything Counter", | |
| inputs=gr.Image(type="pil"), | |
| outputs=[gr.Image(label="detection", type="pil"), gr.Textbox(label="")], | |
| css=css_code, | |
| allow_flagging="never", | |
| examples=[ | |
| ["Examples/apples.jpeg"], | |
| ["Examples/birds.jpeg"], | |
| ["Examples/bottles.jpeg"], | |
| ], | |
| ).launch(debug=True) | |