From 8fa2cde7870b303cc59d4213e224bc3a0b06d90c Mon Sep 17 00:00:00 2001
From: Laurent Fainsin <laurentfainsin@protonmail.com>
Date: Tue, 12 Jul 2022 16:17:37 +0200
Subject: [PATCH] feat: yay more drafts for filtering output of unet

Former-commit-id: 8c46294df7a834e6b67043afbb905b5fcc8194f2 [formerly 79b7ecefd45e16c01afc3e062780147d14da127a]
Former-commit-id: e334164ea7c9b6c9093935d7e468efa66b4ccbe5
---
 src/notebooks/predict.ipynb                | 448 ---------------------
 src/notebooks/predict.ipynb.REMOVED.git-id |   1 +
 2 files changed, 1 insertion(+), 448 deletions(-)
 delete mode 100644 src/notebooks/predict.ipynb
 create mode 100644 src/notebooks/predict.ipynb.REMOVED.git-id

diff --git a/src/notebooks/predict.ipynb b/src/notebooks/predict.ipynb
deleted file mode 100644
index e83106f..0000000
--- a/src/notebooks/predict.ipynb
+++ /dev/null
@@ -1,448 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import albumentations as A\n",
-    "import numpy as np\n",
-    "import onnx\n",
-    "import onnxruntime\n",
-    "from albumentations.pytorch import ToTensorV2\n",
-    "from PIL import Image\n",
-    "\n",
-    "%config InlineBackend.figure_formats = ['svg']\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "\n",
-    "import torch\n",
-    "from utils.dice import dice_score\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sigmoid(x):\n",
-    "    return 1 / (1 + np.exp(-x))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def to_numpy(tensor):\n",
-    "    return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model_path = \"../../checkpoints/best.onnx\"\n",
-    "# image_path = \"../../images/SM.png\"\n",
-    "image_path = \"/home/lilian/data_disk/lfainsin/test/2022_SM/DOS_DETAIL/DSC_0055.jpg\"\n",
-    "gt_path = \"/home/lilian/data_disk/lfainsin/test/2022_SM/DOS_DETAIL/MASK.PNG\"\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "onnx_model = onnx.load(model_path)\n",
-    "onnx.checker.check_model(onnx_model)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "session = onnxruntime.InferenceSession(model_path)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "image = Image.open(image_path).convert(\"RGB\")\n",
-    "\n",
-    "plt.figure(figsize=(25, 10))\n",
-    "plt.imshow(image)\n",
-    "\n",
-    "print(image.size)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "transform = A.Compose(\n",
-    "    [\n",
-    "        A.LongestMaxSize(1024),\n",
-    "        A.ToFloat(max_value=255), # [0, 255] -> [0.0, 1.0]\n",
-    "        ToTensorV2(), # HWC -> CHW\n",
-    "    ],\n",
-    ")\n",
-    "aug = transform(image=np.asarray(image))\n",
-    "img = aug[\"image\"]\n",
-    "\n",
-    "img = img.unsqueeze(0) # -> 1CHW"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "inputs = {\n",
-    "    session.get_inputs()[0].name: to_numpy(img),\n",
-    "}\n",
-    "\n",
-    "outs = session.run(None, inputs)\n",
-    "\n",
-    "img_out = outs[0][0][0] # extract HW\n",
-    "img_out = img_out * 255 # [0.0, 255.0]\n",
-    "img_out = img_out.clip(0, 255) \n",
-    "img_out = np.uint8(img_out) # [0, 255]\n",
-    "img_out = Image.fromarray(img_out, \"L\") # PIL img\n",
-    "\n",
-    "# plt.figure(figsize=(25, 10))\n",
-    "# plt.imshow(img_out, cmap=plt.cm.gray)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "inputs = {\n",
-    "    session.get_inputs()[0].name: to_numpy(img),\n",
-    "}\n",
-    "\n",
-    "outs = session.run(None, inputs)\n",
-    "\n",
-    "img_out = outs[0][0][0] # extract HW\n",
-    "img_out = sigmoid(img_out) # -> [0.0, 1.0]\n",
-    "img_out = img_out * 255 # [0.0, 255.0]\n",
-    "img_out = np.uint8(img_out) # [0, 255]\n",
-    "img_out = Image.fromarray(img_out, \"L\") # PIL img\n",
-    "\n",
-    "plt.figure(figsize=(25, 10))\n",
-    "plt.imshow(img_out, cmap=plt.cm.gray)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from skimage import measure, draw\n",
-    "\n",
-    "# Find contours at a constant value of 0.8\n",
-    "contours = measure.find_contours(np.asarray(img_out))\n",
-    "\n",
-    "# Display the image and plot all contours found\n",
-    "plt.figure(figsize=(19, 18))\n",
-    "plt.imshow(img_out, cmap=plt.cm.gray)\n",
-    "\n",
-    "lenc = [len(c) for c in contours]\n",
-    "indexs = np.argsort(lenc)\n",
-    "l = indexs[-1]\n",
-    "\n",
-    "# plt.plot(contours[l][:, 1], contours[l][:, 0], linewidth=2, c=\"red\")\n",
-    "\n",
-    "# on estime l'ellipse\n",
-    "ellipse = measure.EllipseModel()\n",
-    "ellipse.estimate(contours[l])\n",
-    "\n",
-    "# on récupère les coords des points de l'ellipse\n",
-    "cx, cy, a, b, theta = ellipse.params\n",
-    "ex, ey = draw.ellipse_perimeter(int(cx), int(cy), int(a), int(b), orientation=theta, shape=img_out.size[::-1])\n",
-    "\n",
-    "plt.scatter(ey, ex, c=\"green\", s=0.5)\n",
-    "plt.scatter(cy, cx, c=\"green\", s=0.5)\n",
-    "\n",
-    "# # on estime le cercle\n",
-    "circle = measure.CircleModel()\n",
-    "circle.estimate(contours[l])\n",
-    "\n",
-    "# on récupère les coords des points du cercle\n",
-    "cx, cy, r = circle.params\n",
-    "ex, ey = draw.circle_perimeter(int(cx), int(cy), int(r), shape=img_out.size[::-1])\n",
-    "\n",
-    "plt.scatter(ey, ex, c=\"blue\", s=0.5)\n",
-    "plt.scatter(cy, cx, c=\"blue\", s=0.5)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# mutilresolution zoom"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "factor = image.size[0] / img_out.size[0]\n",
-    "taille = min(a, b) * factor\n",
-    "percentage = 0.1\n",
-    "size = taille / percentage\n",
-    "\n",
-    "img = image.crop((cy*factor - size, cx*factor - size/1.5, cy*factor + size, cx*factor + size/1.5))\n",
-    "\n",
-    "transform = A.Compose(\n",
-    "    [\n",
-    "        A.LongestMaxSize(1024),\n",
-    "        A.ToFloat(max_value=255), # [0, 255] -> [0.0, 1.0]\n",
-    "        ToTensorV2(), # HWC -> CHW\n",
-    "    ],\n",
-    ")\n",
-    "aug = transform(image=np.asarray(img))\n",
-    "img = aug[\"image\"]\n",
-    "\n",
-    "img = img.unsqueeze(0) # -> 1CHW"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "inputs = {\n",
-    "    session.get_inputs()[0].name: to_numpy(img),\n",
-    "}\n",
-    "\n",
-    "outs = session.run(None, inputs)\n",
-    "\n",
-    "img_out = outs[0][0][0] # extract HW\n",
-    "img_out = sigmoid(img_out) # -> [0.0, 1.0]\n",
-    "img_out = img_out * 255 # [0.0, 255.0]\n",
-    "img_out = np.uint8(img_out) # [0, 255]\n",
-    "img_out = Image.fromarray(img_out, \"L\") # PIL img\n",
-    "\n",
-    "plt.figure(figsize=(25, 10))\n",
-    "plt.imshow(img_out, cmap=plt.cm.gray)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.356565656565656564\r"
-     ]
-    }
-   ],
-   "source": [
-    "image = Image.open(image_path).convert(\"RGB\")\n",
-    "ground_truth = Image.open(gt_path).convert(\"L\")\n",
-    "\n",
-    "Image.MAX_IMAGE_PIXELS = 5000000000                                                                                              \n",
-    "\n",
-    "dices = []\n",
-    "percentages = np.linspace(0.01, 0.35, 100)\n",
-    "\n",
-    "for p in percentages:\n",
-    "    size = taille / p \n",
-    "    print(p, end=\"\\r\")\n",
-    "\n",
-    "    img = image.crop((cy * factor - size, cx * factor - size, cy * factor + size, cx * factor + size))\n",
-    "    gt = ground_truth.crop((cy * factor - size, cx * factor - size, cy * factor + size, cx * factor + size))\n",
-    "\n",
-    "    transform = A.Compose(\n",
-    "        [\n",
-    "            A.LongestMaxSize(1024),\n",
-    "            A.ToFloat(max_value=255),  # [0, 255] -> [0.0, 1.0]\n",
-    "            ToTensorV2(),  # HWC -> CHW\n",
-    "        ],\n",
-    "    )\n",
-    "    aug = transform(image=np.asarray(img))\n",
-    "    img = aug[\"image\"]\n",
-    "    img = img.unsqueeze(0)  # -> 1CHW\n",
-    "\n",
-    "    inputs = {\n",
-    "        session.get_inputs()[0].name: to_numpy(img),\n",
-    "    }\n",
-    "\n",
-    "    outs = session.run(None, inputs)\n",
-    "\n",
-    "    img_out2 = outs[0][0][0] # extract HW\n",
-    "    img_out2 = sigmoid(img_out2) # -> [0.0, 1.0]\n",
-    "    img_out2 = img_out2 > 0.5 # {False, True}\n",
-    "    img_out2 = np.uint8(img_out2) # [0, 1]\n",
-    "    img_out2 = torch.tensor(img_out2)\n",
-    "\n",
-    "    aug = transform(image=np.array(gt))\n",
-    "    gt = aug[\"image\"]\n",
-    "    gt = gt > 0.1 # {False, True}\n",
-    "    gt = np.uint8(gt) # [0, 1]\n",
-    "    gt = torch.tensor(gt)\n",
-    "\n",
-    "    dice = dice_score(gt, img_out2, logits=False)\n",
-    "    dices.append(dice)\n",
-    "\n",
-    "    # plt.figure(figsize=(25, 10))\n",
-    "    # plt.imshow(Image.fromarray(gt.squeeze(0).numpy()), cmap=plt.cm.gray)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fb1cc0a54f0>]"
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
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-      "text/plain": [
-       "<Figure size 1080x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(15, 10))\n",
-    "plt.plot(percentages, dices)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Dice(resolution)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "image_path = \"/home/lilian/data_disk/lfainsin/test/2022_SM/DOS_DETAIL/DSC_0050.jpg\"\n",
-    "gt_path = \"/home/lilian/data_disk/lfainsin/test/2022_SM/DOS_DETAIL/MASK.PNG\"\n",
-    "\n",
-    "image = Image.open(image_path).convert(\"RGB\")\n",
-    "image = torch.tensor(np.uint8(image))\n",
-    "\n",
-    "ground_truth = Image.open(gt_path).convert(\"L\")\n",
-    "ground_truth = torch.tensor(np.uint8(ground_truth))\n",
-    "\n",
-    "dices = []\n",
-    "rezs = range(128, 1920+4, 4)\n",
-    "\n",
-    "for rez in rezs:\n",
-    "\n",
-    "    print(rez, end=\"\\r\")\n",
-    "\n",
-    "    transform = A.Compose(\n",
-    "        [\n",
-    "            A.LongestMaxSize(rez),\n",
-    "            A.ToFloat(max_value=255), # [0, 255] -> [0.0, 1.0]\n",
-    "            ToTensorV2(), # HWC -> CHW\n",
-    "        ],\n",
-    "    )\n",
-    "    aug = transform(image=np.array(image))\n",
-    "    img = aug[\"image\"]\n",
-    "    img = img.unsqueeze(0) # -> 1CHW\n",
-    "\n",
-    "    inputs = {\n",
-    "        session.get_inputs()[0].name: to_numpy(img),\n",
-    "    }\n",
-    "    outs = session.run(None, inputs)\n",
-    "\n",
-    "    img_out = outs[0][0][0] # extract HW\n",
-    "    img_out = sigmoid(img_out) # -> [0.0, 1.0]\n",
-    "    img_out = img_out > 0.5 # {False, True}\n",
-    "    img_out = np.uint8(img_out) # [0, 1]\n",
-    "    img_out = torch.tensor(img_out)\n",
-    "\n",
-    "    aug = transform(image=np.array(ground_truth))\n",
-    "    gt = aug[\"image\"]\n",
-    "    gt = gt > 0.1 # {False, True}\n",
-    "    gt = np.uint8(gt) # [0, 1]\n",
-    "    gt = torch.tensor(gt)\n",
-    "\n",
-    "    dice = dice_score(gt, img_out, logits=False)\n",
-    "    dices.append(dice)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.figure(figsize=(15, 10))\n",
-    "plt.plot(rezs, dices)\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.8.0 ('.venv': poetry)",
-   "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": "3.8.0"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "dc80d2c03865715c8671359a6bf138f6c8ae4e26ae025f2543e0980b8db0ed7e"
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-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/src/notebooks/predict.ipynb.REMOVED.git-id b/src/notebooks/predict.ipynb.REMOVED.git-id
new file mode 100644
index 0000000..8f5a3d0
--- /dev/null
+++ b/src/notebooks/predict.ipynb.REMOVED.git-id
@@ -0,0 +1 @@
+b9209497dc0ffc22fedf1f80bc68d81cdc5acb6a
\ No newline at end of file