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| import os | |
| os.system( | |
| "wget https://upload.wikimedia.org/wikipedia/commons/thumb/e/ea/Van_Gogh_-_Starry_Night_-_Google_Art_Project.jpg/1920px-Van_Gogh_-_Starry_Night_-_Google_Art_Project.jpg -O starry.jpg") | |
| from PIL import Image | |
| import requests | |
| import torch | |
| from torchvision import transforms | |
| from torchvision.transforms.functional import InterpolationMode | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| # MDETR Code | |
| import torchvision.transforms as T | |
| import matplotlib.pyplot as plt | |
| from collections import defaultdict | |
| import torch.nn.functional as F | |
| import numpy as np | |
| from skimage.measure import find_contours | |
| from matplotlib import patches, lines | |
| from matplotlib.patches import Polygon | |
| import gradio as gr | |
| torch.hub.download_url_to_file('https://cdn.pixabay.com/photo/2014/03/04/15/10/elephants-279505_1280.jpg', | |
| 'elephant.jpg') | |
| model2, postprocessor = torch.hub.load('ashkamath/mdetr:main', 'mdetr_efficientnetB5', pretrained=True, | |
| return_postprocessor=True) | |
| model2 = model2.cpu() | |
| model2.eval() | |
| torch.set_grad_enabled(False); | |
| # standard PyTorch mean-std input image normalization | |
| transform = T.Compose([ | |
| T.Resize(800), | |
| T.ToTensor(), | |
| T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) | |
| ]) | |
| # 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 | |
| # colors for visualization | |
| COLORS = [[0.000, 0.447, 0.741], [0.850, 0.325, 0.098], [0.929, 0.694, 0.125], | |
| [0.494, 0.184, 0.556], [0.466, 0.674, 0.188], [0.301, 0.745, 0.933]] | |
| def apply_mask(image, mask, color, alpha=0.5): | |
| """Apply the given mask to the image. | |
| """ | |
| for c in range(3): | |
| image[:, :, c] = np.where(mask == 1, | |
| image[:, :, c] * | |
| (1 - alpha) + alpha * color[c] * 255, | |
| image[:, :, c]) | |
| return image | |
| def plot_results(pil_img, scores, boxes, labels, masks=None): | |
| plt.figure(figsize=(16, 10)) | |
| np_image = np.array(pil_img) | |
| ax = plt.gca() | |
| colors = COLORS * 100 | |
| if masks is None: | |
| masks = [None for _ in range(len(scores))] | |
| assert len(scores) == len(boxes) == len(labels) == len(masks) | |
| for s, (xmin, ymin, xmax, ymax), l, mask, c in zip(scores, boxes.tolist(), labels, masks, colors): | |
| ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, | |
| fill=False, color=c, linewidth=3)) | |
| text = f'{l}: {s:0.2f}' | |
| ax.text(xmin, ymin, text, fontsize=15, bbox=dict(facecolor='white', alpha=0.8)) | |
| if mask is None: | |
| continue | |
| np_image = apply_mask(np_image, mask, c) | |
| padded_mask = np.zeros((mask.shape[0] + 2, mask.shape[1] + 2), dtype=np.uint8) | |
| padded_mask[1:-1, 1:-1] = mask | |
| contours = find_contours(padded_mask, 0.5) | |
| for verts in contours: | |
| # Subtract the padding and flip (y, x) to (x, y) | |
| verts = np.fliplr(verts) - 1 | |
| p = Polygon(verts, facecolor="none", edgecolor=c) | |
| ax.add_patch(p) | |
| plt.imshow(np_image) | |
| plt.axis('off') | |
| plt.savefig('foo.png', bbox_inches='tight') | |
| return 'foo.png' | |
| def add_res(results, ax, color='green'): | |
| # for tt in results.values(): | |
| if True: | |
| bboxes = results['boxes'] | |
| labels = results['labels'] | |
| scores = results['scores'] | |
| # keep = scores >= 0.0 | |
| # bboxes = bboxes[keep].tolist() | |
| # labels = labels[keep].tolist() | |
| # scores = scores[keep].tolist() | |
| # print(torchvision.ops.box_iou(tt['boxes'].cpu().detach(), torch.as_tensor([[xmin, ymin, xmax, ymax]]))) | |
| colors = ['purple', 'yellow', 'red', 'green', 'orange', 'pink'] | |
| for i, (b, ll, ss) in enumerate(zip(bboxes, labels, scores)): | |
| ax.add_patch(plt.Rectangle((b[0], b[1]), b[2] - b[0], b[3] - b[1], fill=False, color=colors[i], linewidth=3)) | |
| cls_name = ll if isinstance(ll, str) else CLASSES[ll] | |
| text = f'{cls_name}: {ss:.2f}' | |
| print(text) | |
| ax.text(b[0], b[1], text, fontsize=15, bbox=dict(facecolor='white', alpha=0.8)) | |
| def plot_inference(im, caption, approaches): | |
| choices = {"Worker Helmet Separately": 1, "Worker Helmet Vest": 2, "Workers only": 3} | |
| # mean-std normalize the input image (batch-size: 1) | |
| img = transform(im).unsqueeze(0).cpu() | |
| # propagate through the model | |
| memory_cache = model2(img, [caption], encode_and_save=True) | |
| outputs = model2(img, [caption], encode_and_save=False, memory_cache=memory_cache) | |
| # keep only predictions with 0.7+ confidence | |
| probas = 1 - outputs['pred_logits'].softmax(-1)[0, :, -1].cpu() | |
| keep = (probas > 0.7).cpu() | |
| # convert boxes from [0; 1] to image scales | |
| bboxes_scaled = rescale_bboxes(outputs['pred_boxes'].cpu()[0, keep], im.size) | |
| # Extract the text spans predicted by each box | |
| positive_tokens = (outputs["pred_logits"].cpu()[0, keep].softmax(-1) > 0.1).nonzero().tolist() | |
| predicted_spans = defaultdict(str) | |
| for tok in positive_tokens: | |
| item, pos = tok | |
| if pos < 255: | |
| span = memory_cache["tokenized"].token_to_chars(0, pos) | |
| predicted_spans[item] += " " + caption[span.start:span.end] | |
| labels = [predicted_spans[k] for k in sorted(list(predicted_spans.keys()))] | |
| caption = 'Caption: ' + caption | |
| return (sepia_call(caption, im, plot_results(im, probas[keep], bboxes_scaled, labels), choices[approaches])) | |
| # BLIP Code | |
| from modelsn.blip import blip_decoder | |
| image_size = 384 | |
| transform = transforms.Compose([ | |
| transforms.Resize((image_size, image_size), interpolation=InterpolationMode.BICUBIC), | |
| transforms.ToTensor(), | |
| transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)) | |
| ]) | |
| model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_base_caption.pth' | |
| model = blip_decoder(pretrained=model_url, image_size=384, vit='base') | |
| model.eval() | |
| model = model.to(device) | |
| from modelsn.blip_vqa import blip_vqa | |
| image_size_vq = 480 | |
| transform_vq = transforms.Compose([ | |
| transforms.Resize((image_size_vq, image_size_vq), interpolation=InterpolationMode.BICUBIC), | |
| transforms.ToTensor(), | |
| transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)) | |
| ]) | |
| model_url_vq = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_vqa.pth' | |
| model_vq = blip_vqa(pretrained=model_url_vq, image_size=480, vit='base') | |
| model_vq.eval() | |
| model_vq = model_vq.to(device) | |
| def inference(raw_image, approaches, question): | |
| image = transform(raw_image).unsqueeze(0).to(device) | |
| with torch.no_grad(): | |
| caption = model.generate(image, sample=False, num_beams=3, max_length=20, min_length=5) | |
| return (plot_inference(raw_image, caption[0], approaches)) | |
| # return 'caption: '+caption[0] | |
| # PPE Detection code | |
| import numpy as np | |
| import run_code | |
| import gradio as gr | |
| def sepia_call(caption, Input_Image, MDETR_im, Approach): | |
| pil_image = Input_Image | |
| open_cv_image = np.asarray(pil_image) | |
| sepia_img = run_code.run(open_cv_image, Approach) | |
| images = sepia_img['img'] | |
| texts = sepia_img['text'] | |
| return (caption, MDETR_im, images, texts) | |
| inputs = [gr.inputs.Image(type='pil'), | |
| gr.inputs.Radio(choices=["Worker Helmet Separately", "Worker Helmet Vest", "Workers only"], type="value", | |
| default="Worker Helmet Vest", label="Model"), "textbox"] | |
| outputs = [gr.outputs.Textbox(label="Output"), "image", "image", gr.outputs.Textbox(label="Output")] | |
| title = "BLIP + MDETR + PPE Detection" | |
| description = "Gradio demo for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation by Salesforce Research. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below." | |
| article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2201.12086' target='_blank'>BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation</a> | <a href='https://github.com/salesforce/BLIP' target='_blank'>Github Repo</a></p>" | |
| gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, | |
| examples=[['starry.jpg', "Image Captioning", "None"]]).launch(share=True, enable_queue=True, | |
| cache_examples=False) |