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| import gradio as gr | |
| import os | |
| import torch | |
| import pytorch_lightning as pl | |
| import cv2 | |
| import numpy | |
| from transformers import AutoFeatureExtractor, AutoModelForObjectDetection | |
| from PIL import Image | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| feature_extractor = AutoFeatureExtractor.from_pretrained("hustvl/yolos-small", size=512, max_size=864) | |
| id2label = {1: 'person', 2: 'rider', 3: 'car', 4: 'bus', 5: 'truck', 6: 'bike', 7: 'motor', 8: 'traffic light', 9: 'traffic sign', 10: 'train'} | |
| # colors for visualization | |
| colors = [ | |
| [ 0, 113, 188,], | |
| [216, 82, 24,], | |
| [236, 176, 31,], | |
| [255, 255, 0,], | |
| [118, 171, 47,], | |
| [ 76, 189, 237,], | |
| [ 46, 155, 188,], | |
| [125, 171, 141,], | |
| [125, 76, 237,], | |
| [ 0, 82, 216,], | |
| [189, 76, 47,]] | |
| class Detr(pl.LightningModule): | |
| def __init__(self, lr, weight_decay): | |
| super().__init__() | |
| # replace COCO classification head with custom head | |
| self.model = AutoModelForObjectDetection.from_pretrained("hustvl/yolos-small", | |
| num_labels=len(id2label), | |
| ignore_mismatched_sizes=True) | |
| # see https://github.com/PyTorchLightning/pytorch-lightning/pull/1896 | |
| self.lr = lr | |
| self.weight_decay = weight_decay | |
| def forward(self, pixel_values): | |
| outputs = self.model(pixel_values=pixel_values) | |
| return outputs | |
| def common_step(self, batch, batch_idx): | |
| pixel_values = batch["pixel_values"] | |
| labels = [{k: v.to(self.device) for k, v in t.items()} for t in batch["labels"]] | |
| outputs = self.model(pixel_values=pixel_values, labels=labels) | |
| loss = outputs.loss | |
| loss_dict = outputs.loss_dict | |
| return loss, loss_dict | |
| def training_step(self, batch, batch_idx): | |
| loss, loss_dict = self.common_step(batch, batch_idx) | |
| # logs metrics for each training_step, | |
| # and the average across the epoch | |
| self.log("training_loss", loss) | |
| for k,v in loss_dict.items(): | |
| self.log("train_" + k, v.item()) | |
| return loss | |
| def validation_step(self, batch, batch_idx): | |
| loss, loss_dict = self.common_step(batch, batch_idx) | |
| self.log("validation_loss", loss) | |
| for k,v in loss_dict.items(): | |
| self.log("validation_" + k, v.item()) | |
| return loss | |
| def configure_optimizers(self): | |
| optimizer = torch.optim.AdamW(self.parameters(), lr=self.lr, | |
| weight_decay=self.weight_decay) | |
| return optimizer | |
| # Build model and load checkpoint | |
| checkpoint = './checkpoints/epoch=1-step=2184.ckpt' | |
| model_yolos = Detr.load_from_checkpoint(checkpoint, lr=2.5e-5, weight_decay=1e-4) | |
| model_yolos.to(device) | |
| model_yolos.eval() | |
| # for output bounding box post-processing | |
| def box_cxcywh_to_xyxy(x): | |
| x_c, y_c, w, h = x.unbind(1) | |
| b = [(x_c - 0.5 * w), (y_c - 0.5 * h), | |
| (x_c + 0.5 * w), (y_c + 0.5 * h)] | |
| return torch.stack(b, dim=1) | |
| def rescale_bboxes(out_bbox, size): | |
| img_w, img_h = size | |
| b = box_cxcywh_to_xyxy(out_bbox) | |
| b = b * torch.tensor([img_w, img_h, img_w, img_h], dtype=torch.float32) | |
| return b | |
| def plot_results(pil_img, prob, boxes): | |
| img = numpy.asarray(pil_img) | |
| for p, (xmin, ymin, xmax, ymax) in zip(prob, boxes.tolist()): | |
| cl = p.argmax() | |
| c = colors[cl] | |
| c1, c2 = (int(xmin), int(ymin)), (int(xmax), int(ymax)) | |
| cv2.rectangle(img, c1, c2, c, thickness=2, lineType=cv2.LINE_AA) | |
| cv2.putText(img, f'{id2label[cl.item()]}: {p[cl]:0.2f}', [int(xmin), int(ymin)-5], cv2.FONT_HERSHEY_SIMPLEX, 0.7, c, 2) | |
| return Image.fromarray(img) | |
| def generate_preds(processor, model, image): | |
| inputs = processor(images=image, return_tensors="pt").to(device) | |
| preds = model(pixel_values=inputs.pixel_values) | |
| return preds | |
| def visualize_preds(image, preds, threshold=0.9): | |
| # keep only predictions with confidence >= threshold | |
| probas = preds.logits.softmax(-1)[0, :, :-1] | |
| keep = probas.max(-1).values > threshold | |
| # convert predicted boxes from [0; 1] to image scales | |
| bboxes_scaled = rescale_bboxes(preds.pred_boxes[0, keep].cpu(), image.size) | |
| return plot_results(image, probas[keep], bboxes_scaled) | |
| def detect(img): | |
| # Run inference | |
| preds = generate_preds(feature_extractor, model_yolos, img) | |
| return visualize_preds(img, preds) | |
| description = "Welcome to this space! 🤗this is a traffic object detector based on <a href='https://huggingface.co/docs/transformers/model_doc/yolos' style='text-decoration: underline' target='_blank'>YOLOS</a>. \n\n" + \ | |
| "The model can detect following targets: 🚶♂️person, 🚴♀️rider, 🚗car, 🚌bus, 🚚truck, 🚲bike, 🏍️motor, 🚦traffic light, ⛔traffic sign, 🚄train." | |
| interface = gr.Interface( | |
| fn=detect, | |
| inputs=[gr.Image(type="pil")], | |
| outputs=gr.Image(type="pil"), | |
| examples=[["./imgs/example1.jpg"], ["./imgs/example2.jpg"], ["./imgs/example3.png"]], | |
| title="YOLOS for traffic object detection", | |
| description=description) | |
| interface.launch() | |