ignore

.github/workflows/main.yml

@@ -1,21 +0,0 @@
-name: Sync to Hugging Face hub
-on:
-  push:
-    branches: [main]
-
-  # to run this workflow manually from the Actions tab
-  workflow_dispatch:
-
-jobs:
-  sync-to-hub:
-    runs-on: ubuntu-latest
-    steps:
-
-        
-      - uses: actions/checkout@v2
-        with:
-          fetch-depth: 0
-      - name: Push to hub
-        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
-        run: git push --force https://dnth:$HF_TOKEN@huggingface.co/spaces/dnth/webdemo-fridge-detection main

.gitignore

@@ -1,3 +1,3 @@
 .ipynb_checkpoints
 .ipynb_checkpoints/*
-models/*.pth
+models/**

README.md

@@ -1,37 +0,0 @@
----
-title: webdemo-fridge-detection
-emoji: 🍿
-colorFrom: red
-colorTo: purple
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_  
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.

app.py

@@ -1,73 +0,0 @@
-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)

gradioapp.ipynb

@@ -1,262 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "ee7e0c23-3fa5-4547-8598-7df27a3876c5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from icevision.all import *\n",
-    "import icedata\n",
-    "import PIL, requests\n",
-    "import torch\n",
-    "from torchvision import transforms\n",
-    "import gradio as gr"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "646cc218-f7de-4f32-a3d9-fccdc9b54592",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Download the dataset\n",
-    "url = \"https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip\"\n",
-    "dest_dir = \"fridge\"\n",
-    "data_dir = icedata.load_data(url, dest_dir)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "96184ca0-0b0a-4a20-8ab9-30dee6096588",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create the parser\n",
-    "parser = parsers.VOCBBoxParser(annotations_dir=data_dir / \"odFridgeObjects/annotations\", images_dir=data_dir / \"odFridgeObjects/images\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "dfa20f76-4970-479a-9497-871fe4cfd170",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fc8e676815314038a40c884c8c7f5b67",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/128 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1m\u001b[1mINFO    \u001b[0m\u001b[1m\u001b[0m - \u001b[1m\u001b[34m\u001b[1mAutofixing records\u001b[0m\u001b[1m\u001b[34m\u001b[0m\u001b[1m\u001b[0m | \u001b[36micevision.parsers.parser\u001b[0m:\u001b[36mparse\u001b[0m:\u001b[36m122\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3db4bc1ae388495eb3b62289459a5c00",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/128 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<ClassMap: {'background': 0, 'carton': 1, 'milk_bottle': 2, 'can': 3, 'water_bottle': 4}>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Parse annotations to create records\n",
-    "train_records, valid_records = parser.parse()\n",
-    "parser.class_map"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "26d4f2f7-db51-413c-838f-f80c5898ab52",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class_map = parser.class_map"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "007b2e97-d546-4178-84e7-d4fe597f3731",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "extra_args = {}\n",
-    "model_type = models.torchvision.retinanet\n",
-    "backbone = model_type.backbones.resnet50_fpn\n",
-    "# Instantiate the model\n",
-    "model = model_type.model(backbone=backbone(pretrained=True), num_classes=len(parser.class_map), **extra_args) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "7b664cbf-3ab0-46df-a9d0-c4eb5c3c026d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Transforms\n",
-    "# size is set to 384 because EfficientDet requires its inputs to be divisible by 128\n",
-    "image_size = 384\n",
-    "train_tfms = tfms.A.Adapter([*tfms.A.aug_tfms(size=image_size, presize=512), tfms.A.Normalize()])\n",
-    "valid_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(image_size), tfms.A.Normalize()])\n",
-    "# Datasets\n",
-    "train_ds = Dataset(train_records, train_tfms)\n",
-    "valid_ds = Dataset(valid_records, valid_tfms)\n",
-    "# Data Loaders\n",
-    "train_dl = model_type.train_dl(train_ds, batch_size=8, num_workers=4, shuffle=True)\n",
-    "valid_dl = model_type.valid_dl(valid_ds, batch_size=8, num_workers=4, shuffle=False)\n",
-    "metrics = [COCOMetric(metric_type=COCOMetricType.bbox)]\n",
-    "learn = model_type.fastai.learner(dls=[train_dl, valid_dl], model=model, metrics=metrics)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "7bddb248-215d-4998-9d90-14ea6989c236",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/dnth/anaconda3/envs/icevision-gradio/lib/python3.8/site-packages/fastai/learner.py:56: UserWarning: Saved filed doesn't contain an optimizer state.\n",
-      "  elif with_opt: warn(\"Saved filed doesn't contain an optimizer state.\")\n"
-     ]
-    }
-   ],
-   "source": [
-    "learn = learn.load('model')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "745315f6-8aa5-486e-a7bc-e11348bec6a6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def show_preds(input_image, display_label, display_bbox, detection_threshold):\n",
-    "\n",
-    "    if detection_threshold==0: detection_threshold=0.5\n",
-    "\n",
-    "    img = PIL.Image.fromarray(input_image, 'RGB')\n",
-    "\n",
-    "    pred_dict  = model_type.end2end_detect(img, valid_tfms, model, class_map=class_map, detection_threshold=detection_threshold,\n",
-    "                                           display_label=display_label, display_bbox=display_bbox, return_img=True, \n",
-    "                                           font_size=16, label_color=\"#FF59D6\")\n",
-    "\n",
-    "    return pred_dict['img']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "63ac7fab-2068-4dbc-a464-0551b6fc12b2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running on local URL:  http://127.0.0.1:7860/\n",
-      "Running on public URL: https://11839.gradio.app\n",
-      "\n",
-      "This share link will expire in 72 hours. To get longer links, send an email to: support@gradio.app\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/dnth/anaconda3/envs/icevision-gradio/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2157.)\n",
-      "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# display_chkbox = gr.inputs.CheckboxGroup([\"Label\", \"BBox\"], label=\"Display\", default=True)\n",
-    "display_chkbox_label = gr.inputs.Checkbox(label=\"Label\", default=True)\n",
-    "display_chkbox_box = gr.inputs.Checkbox(label=\"Box\", default=True)\n",
-    "\n",
-    "detection_threshold_slider = gr.inputs.Slider(minimum=0, maximum=1, step=0.1, default=0.5, label=\"Detection Threshold\")\n",
-    "\n",
-    "outputs = gr.outputs.Image(type=\"pil\")\n",
-    "\n",
-    "# Option 1: Get an image from local drive\n",
-    "gr_interface = gr.Interface(fn=show_preds, inputs=[\"image\", display_chkbox_label, display_chkbox_box,  detection_threshold_slider], outputs=outputs, title='IceApp - COCO')\n",
-    "\n",
-    "# #  Option 2: Grab an image from a webcam\n",
-    "# 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)\n",
-    "\n",
-    "# #  Option 3: Continuous image stream from the webcam\n",
-    "# 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)\n",
-    "\n",
-    "\n",
-    "gr_interface.launch(inline=False, share=True, debug=True)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "727a3589-364b-4bfd-9c32-bef5ebe34dbe",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

models/model.pth

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:eb46c5796093f5921996b04f3d85ded01f4070366e38898e748e06c00d262972
-size 129455527

requirements.txt

@@ -1,9 +0,0 @@
-#icevision[all]
-
-#opencv-python-headless
-
-git+https://github.com/dnth/icevision.git
-icedata
-
-#fastai
-#scikit-image