mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-06-24 01:17:28 +08:00
8353bbbf93
* add segment anything model * add readme * reorg file structure * update * lint * minor updates * ack * fix weight loading * simplify * fix to run notebooks * amg in mlx * remove torch dependency * nit in README * return indices in nms * simplify * bugfix / simplify * fix bug' * simplify * fix notebook and remove output * couple more nits --------- Co-authored-by: Awni Hannun <awni@apple.com>
630 lines
17 KiB
Plaintext
630 lines
17 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Segmenting from Prompts\n",
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"\n",
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"This notebook walks through predicting object segmentations from a provided prompt. It uses the `Predictor` class. It is modified from [original SAM GitHub repo](https://github.com/facebookresearch/segment-anything/).\n",
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"\n",
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"### Setup\n",
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"Necessary imports and helper functions for displaying points, boxes, and masks."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import cv2\n",
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"import matplotlib.pyplot as plt\n",
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"import mlx.core as mx\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def show_mask(mask, ax, random_color=False):\n",
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" if random_color:\n",
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" color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)\n",
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" else:\n",
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" color = np.array([30/255, 144/255, 255/255, 0.6])\n",
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" h, w = mask.shape[:2]\n",
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" mask_image = np.array(mask).reshape(h, w, 1) * color.reshape(1, 1, -1)\n",
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" ax.imshow(mask_image)\n",
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" \n",
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"def show_points(coords, labels, ax, marker_size=375):\n",
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" pos_points = np.array(coords)[labels==1]\n",
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" neg_points = np.array(coords)[labels==0]\n",
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" ax.scatter(pos_points[:, 0], pos_points[:, 1], color='green', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)\n",
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" ax.scatter(neg_points[:, 0], neg_points[:, 1], color='red', marker='*', s=marker_size, edgecolor='white', linewidth=1.25) \n",
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" \n",
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"def show_box(box, ax):\n",
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" box = box.tolist()\n",
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" x0, y0 = box[0], box[1]\n",
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" w, h = box[2] - box[0], box[3] - box[1]\n",
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" ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2)) "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Example image"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"image = cv2.imread('images/truck.jpg')\n",
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"image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.figure(figsize=(10,10))\n",
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"plt.imshow(image)\n",
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"plt.axis('on')\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Selecting objects with SAM"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"First, load the SAM model and predictor. Change the path below to point to the SAM checkpoint."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"sys.path.append(\"..\")\n",
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"from segment_anything.sam import load\n",
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"from segment_anything.predictor import SamPredictor\n",
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"\n",
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"sam_checkpoint = \"../sam-vit-base\"\n",
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"sam = load(sam_checkpoint)\n",
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"predictor = SamPredictor(sam)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Process the image to produce an image embedding by calling `SamPredictor.set_image`. `SamPredictor` remembers this embedding and will use it for subsequent mask prediction."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"predictor.set_image(image)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"To select the truck, choose a point on it. Points are input to the model in (x,y) format and come with labels 1 (foreground point) or 0 (background point). Multiple points can be input; here we use only one. The chosen point will be shown as a star on the image."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"input_point = mx.array([[500, 375]])\n",
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"input_label = mx.array([1])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.figure(figsize=(10,10))\n",
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"plt.imshow(image)\n",
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"show_points(input_point, input_label, plt.gca())\n",
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"plt.axis('on')\n",
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"plt.show() "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Predict with `SamPredictor.predict`. The model returns masks, quality predictions for those masks, and low resolution mask logits that can be passed to the next iteration of prediction."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"masks, scores, logits = predictor.predict(\n",
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" point_coords=input_point[None],\n",
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" point_labels=input_label[None],\n",
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" multimask_output=True,\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"With `multimask_output=True` (the default setting), SAM outputs 3 masks, where `scores` gives the model's own estimation of the quality of these masks. This setting is intended for ambiguous input prompts, and helps the model disambiguate different objects consistent with the prompt. When `False`, it will return a single mask. For ambiguous prompts such as a single point, it is recommended to use `multimask_output=True` even if only a single mask is desired; the best single mask can be chosen by picking the one with the highest score returned in `scores`. This will often result in a better mask."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"for i in range(masks.shape[-1]):\n",
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" mask = masks[..., i]\n",
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" score = scores[..., i].item()\n",
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" plt.figure(figsize=(10,10))\n",
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" plt.imshow(image)\n",
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" show_mask(mask[0], plt.gca())\n",
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" show_points(input_point, input_label, plt.gca())\n",
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" plt.title(f\"Mask {i+1}, Score: {score:.3f}\", fontsize=18)\n",
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" plt.axis('off')\n",
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" plt.show() "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Specifying a specific object with additional points"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The single input point is ambiguous, and the model has returned multiple objects consistent with it. To obtain a single object, multiple points can be provided. If available, a mask from a previous iteration can also be supplied to the model to aid in prediction. When specifying a single object with multiple prompts, a single mask can be requested by setting `multimask_output=False`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"input_point = mx.array([[500, 375], [1125, 625]])\n",
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"input_label = mx.array([1, 1])\n",
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"mask_input = logits[..., mx.argmax(scores)] # Choose the model's best mask"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"masks, _, _ = predictor.predict(\n",
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" point_coords=input_point[None],\n",
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" point_labels=input_label[None],\n",
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" mask_input=mask_input[..., None],\n",
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" multimask_output=False,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.figure(figsize=(10,10))\n",
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"plt.imshow(image)\n",
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"show_mask(masks[0], plt.gca())\n",
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"show_points(input_point, input_label, plt.gca())\n",
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"plt.axis('off')\n",
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"plt.show() "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"To exclude the car and specify just the window, a background point (with label 0, here shown in red) can be supplied."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"input_point = mx.array([[500, 375], [1125, 625]])\n",
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"input_label = mx.array([1, 0])\n",
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"mask_input = logits[..., mx.argmax(scores)] # Choose the model's best mask"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"masks, _, _ = predictor.predict(\n",
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" point_coords=input_point[None],\n",
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" point_labels=input_label[None],\n",
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" mask_input=mask_input[..., None],\n",
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" multimask_output=False,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.figure(figsize=(10, 10))\n",
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"plt.imshow(image)\n",
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"show_mask(masks[0], plt.gca())\n",
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"show_points(input_point, input_label, plt.gca())\n",
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"plt.axis('off')\n",
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"plt.show() "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Specifying a specific object with a box"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The model can also take a box as input, provided in xyxy format."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"input_box = mx.array([425, 600, 700, 875])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"masks, _, _ = predictor.predict(\n",
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" point_coords=None,\n",
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" point_labels=None,\n",
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" box=input_box[None, :],\n",
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" multimask_output=False,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.figure(figsize=(10, 10))\n",
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"plt.imshow(image)\n",
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"show_mask(masks[0, ..., 0], plt.gca())\n",
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"show_box(input_box, plt.gca())\n",
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"plt.axis('off')\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Combining points and boxes"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Points and boxes may be combined, just by including both types of prompts to the predictor. Here this can be used to select just the trucks's tire, instead of the entire wheel."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"input_box = mx.array([425, 600, 700, 875])\n",
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"input_point = mx.array([[575, 750]])\n",
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"input_label = mx.array([0])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"masks, _, _ = predictor.predict(\n",
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" point_coords=input_point[None],\n",
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" point_labels=input_label[None],\n",
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" box=input_box,\n",
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" multimask_output=False,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.figure(figsize=(10, 10))\n",
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"plt.imshow(image)\n",
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"show_mask(masks[0, ..., 0], plt.gca())\n",
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"show_box(input_box, plt.gca())\n",
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"show_points(input_point, input_label, plt.gca())\n",
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"plt.axis('off')\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Batched prompt inputs"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"`SamPredictor` can take multiple input prompts for the same image."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"input_boxes = mx.array([\n",
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" [75, 275, 1725, 850],\n",
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" [425, 600, 700, 875],\n",
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" [1375, 550, 1650, 800],\n",
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" [1240, 675, 1400, 750],\n",
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"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"masks, _, _ = predictor.predict(\n",
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" point_coords=None,\n",
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" point_labels=None,\n",
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" box=input_boxes,\n",
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" multimask_output=False,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.figure(figsize=(10, 10))\n",
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"plt.imshow(image)\n",
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"for mask in masks:\n",
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" show_mask(mask, plt.gca(), random_color=True)\n",
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"for box in input_boxes:\n",
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" show_box(box, plt.gca())\n",
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"plt.axis('off')\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## End-to-end batched inference"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"If all prompts are available in advance, it is possible to run SAM directly in an end-to-end fashion. This also allows batching over images."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"image1 = image # truck.jpg from above\n",
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"image1_boxes = mx.array([\n",
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" [75, 275, 1725, 850],\n",
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" [425, 600, 700, 875],\n",
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" [1375, 550, 1650, 800],\n",
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" [1240, 675, 1400, 750],\n",
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"])\n",
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"\n",
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"image2 = cv2.imread('images/groceries.jpg')\n",
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"image2 = cv2.cvtColor(image2, cv2.COLOR_BGR2RGB)\n",
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"image2_boxes = mx.array([\n",
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" [450, 170, 520, 350],\n",
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" [350, 190, 450, 350],\n",
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" [500, 170, 580, 350],\n",
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" [580, 170, 640, 350],\n",
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"])"
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]
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},
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{
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"cell_type": "markdown",
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|
"metadata": {},
|
|
"source": [
|
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"Both images and prompts are input as mlx array that are already transformed to the correct frame. Inputs are packaged as a list over images, which each element is a dict that takes the following keys:\n",
|
|
"* `image`: The input image as a mlx array in HWC format.\n",
|
|
"* `original_size`: The size of the image before transforming for input to SAM, in (H, W) format.\n",
|
|
"* `point_coords`: Batched coordinates of point prompts.\n",
|
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"* `point_labels`: Batched labels of point prompts.\n",
|
|
"* `boxes`: Batched input boxes.\n",
|
|
"* `mask_inputs`: Batched input masks.\n",
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|
"\n",
|
|
"If a prompt is not present, the key can be excluded."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from segment_anything.utils.transforms import ResizeLongestSide\n",
|
|
"resize_transform = ResizeLongestSide(sam.vision_encoder.img_size)\n",
|
|
"\n",
|
|
"def prepare_image(image, transform, device):\n",
|
|
" image = transform.apply_image(image)\n",
|
|
" image = mx.array(image)\n",
|
|
" return image"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"batched_input = [\n",
|
|
" {\n",
|
|
" 'image': prepare_image(image1, resize_transform, sam),\n",
|
|
" 'boxes': resize_transform.apply_boxes(image1_boxes, image1.shape[:2]),\n",
|
|
" 'original_size': image1.shape[:2]\n",
|
|
" },\n",
|
|
" {\n",
|
|
" 'image': prepare_image(image2, resize_transform, sam),\n",
|
|
" 'boxes': resize_transform.apply_boxes(image2_boxes, image2.shape[:2]),\n",
|
|
" 'original_size': image2.shape[:2]\n",
|
|
" }\n",
|
|
"]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Run the model."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"batched_output = sam(batched_input, multimask_output=False)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"The output is a list over results for each input image, where list elements are dictionaries with the following keys:\n",
|
|
"* `masks`: A batched mlx array of predicted binary masks, the size of the original image.\n",
|
|
"* `iou_predictions`: The model's prediction of the quality for each mask.\n",
|
|
"* `low_res_logits`: Low res logits for each mask, which can be passed back to the model as mask input on a later iteration."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"fig, ax = plt.subplots(1, 2, figsize=(20, 20))\n",
|
|
"\n",
|
|
"ax[0].imshow(image1)\n",
|
|
"for mask in batched_output[0]['masks']:\n",
|
|
" show_mask(np.array(mask), ax[0], random_color=True)\n",
|
|
"for box in image1_boxes:\n",
|
|
" show_box(np.array(box), ax[0])\n",
|
|
"ax[0].axis('off')\n",
|
|
"\n",
|
|
"ax[1].imshow(image2)\n",
|
|
"for mask in batched_output[1]['masks']:\n",
|
|
" show_mask(np.array(mask), ax[1], random_color=True)\n",
|
|
"for box in image2_boxes:\n",
|
|
" show_box(np.array(box), ax[1])\n",
|
|
"ax[1].axis('off')\n",
|
|
"\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.show()"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"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": "3.9.17"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|