app.py

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)