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| from icevision.all import * | |
| import icedata | |
| import PIL, requests | |
| import torch | |
| from torchvision import transforms | |
| import gradio as gr | |
| # Download the dataset | |
| url = "https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip" | |
| dest_dir = "fridge" | |
| data_dir = icedata.load_data(url, dest_dir) | |
| # Create the parser | |
| parser = parsers.VOCBBoxParser(annotations_dir=data_dir / "odFridgeObjects/annotations", images_dir=data_dir / "odFridgeObjects/images") | |
| # Parse annotations to create records | |
| train_records, valid_records = parser.parse() | |
| class_map = parser.class_map | |
| extra_args = {} | |
| model_type = models.torchvision.retinanet | |
| backbone = model_type.backbones.resnet50_fpn | |
| # Instantiate the model | |
| model = model_type.model(backbone=backbone(pretrained=True), num_classes=len(parser.class_map), **extra_args) | |
| # Transforms | |
| # size is set to 384 because EfficientDet requires its inputs to be divisible by 128 | |
| image_size = 384 | |
| train_tfms = tfms.A.Adapter([*tfms.A.aug_tfms(size=image_size, presize=512), tfms.A.Normalize()]) | |
| valid_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(image_size), tfms.A.Normalize()]) | |
| # Datasets | |
| train_ds = Dataset(train_records, train_tfms) | |
| valid_ds = Dataset(valid_records, valid_tfms) | |
| # Data Loaders | |
| train_dl = model_type.train_dl(train_ds, batch_size=8, num_workers=4, shuffle=True) | |
| valid_dl = model_type.valid_dl(valid_ds, batch_size=8, num_workers=4, shuffle=False) | |
| metrics = [COCOMetric(metric_type=COCOMetricType.bbox)] | |
| learn = model_type.fastai.learner(dls=[train_dl, valid_dl], model=model, metrics=metrics) | |
| learn = learn.load('model') | |
| def show_preds(input_image, display_label, display_bbox, detection_threshold): | |
| if detection_threshold==0: detection_threshold=0.5 | |
| img = PIL.Image.fromarray(input_image, 'RGB') | |
| pred_dict = model_type.end2end_detect(img, valid_tfms, model, class_map=class_map, detection_threshold=detection_threshold, | |
| display_label=display_label, display_bbox=display_bbox, return_img=True, | |
| font_size=16, label_color="#FF59D6") | |
| return pred_dict['img'] | |
| # display_chkbox = gr.inputs.CheckboxGroup(["Label", "BBox"], label="Display", default=True) | |
| display_chkbox_label = gr.inputs.Checkbox(label="Label", default=True) | |
| display_chkbox_box = gr.inputs.Checkbox(label="Box", default=True) | |
| detection_threshold_slider = gr.inputs.Slider(minimum=0, maximum=1, step=0.1, default=0.5, label="Detection Threshold") | |
| outputs = gr.outputs.Image(type="pil") | |
| # Option 1: Get an image from local drive | |
| gr_interface = gr.Interface(fn=show_preds, inputs=["image", display_chkbox_label, display_chkbox_box, detection_threshold_slider], outputs=outputs, title='IceApp - Fridge Object') | |
| # # Option 2: Grab an image from a webcam | |
| # gr_interface = gr.Interface(fn=show_preds, inputs=["webcam", display_chkbox_label, display_chkbox_box, detection_threshold_slider], outputs=outputs, title='IceApp - COCO', live=False) | |
| # # Option 3: Continuous image stream from the webcam | |
| # gr_interface = gr.Interface(fn=show_preds, inputs=["webcam", display_chkbox_label, display_chkbox_box, detection_threshold_slider], outputs=outputs, title='IceApp - COCO', live=True) | |
| gr_interface.launch(inline=False, share=True, debug=True) | |