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| import os | |
| import cv2 | |
| import sys | |
| import numpy as np | |
| import gradio as gr | |
| from PIL import Image | |
| import matplotlib.pyplot as plt | |
| from segment_anything import sam_model_registry, SamAutomaticMaskGenerator | |
| models = { | |
| 'vit_b': './checkpoints/sam_vit_b_01ec64.pth', | |
| 'vit_l': './checkpoints/sam_vit_l_0b3195.pth', | |
| 'vit_h': './checkpoints/sam_vit_h_4b8939.pth' | |
| } | |
| def segment_one(img, mask_generator, seed=None): | |
| if seed is not None: | |
| np.random.seed(seed) | |
| masks = mask_generator.generate(img) | |
| sorted_anns = sorted(masks, key=(lambda x: x['area']), reverse=True) | |
| mask_all = np.ones((img.shape[0], img.shape[1], 3)) | |
| for ann in sorted_anns: | |
| m = ann['segmentation'] | |
| color_mask = np.random.random((1, 3)).tolist()[0] | |
| for i in range(3): | |
| mask_all[m == True, i] = color_mask[i] | |
| result = img / 255 * 0.3 + mask_all * 0.7 | |
| return result, mask_all | |
| def inference(device, model_type, points_per_side, pred_iou_thresh, stability_score_thresh, min_mask_region_area, | |
| stability_score_offset, box_nms_thresh, crop_n_layers, crop_nms_thresh, input_x, progress=gr.Progress()): | |
| # sam model | |
| sam = sam_model_registry[model_type](checkpoint=models[model_type]).to(device) | |
| mask_generator = SamAutomaticMaskGenerator( | |
| sam, | |
| points_per_side=points_per_side, | |
| pred_iou_thresh=pred_iou_thresh, | |
| stability_score_thresh=stability_score_thresh, | |
| stability_score_offset=stability_score_offset, | |
| box_nms_thresh=box_nms_thresh, | |
| crop_n_layers=crop_n_layers, | |
| crop_nms_thresh=crop_nms_thresh, | |
| crop_overlap_ratio=512 / 1500, | |
| crop_n_points_downscale_factor=1, | |
| point_grids=None, | |
| min_mask_region_area=min_mask_region_area, | |
| output_mode='binary_mask' | |
| ) | |
| # input is image, type: numpy | |
| if type(input_x) == np.ndarray: | |
| result, mask_all = segment_one(input_x, mask_generator) | |
| return result, mask_all | |
| elif isinstance(input_x, str): # input is video, type: path (str) | |
| cap = cv2.VideoCapture(input_x) # read video | |
| frames_num = cap.get(cv2.CAP_PROP_FRAME_COUNT) | |
| W, H = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) | |
| fps = int(cap.get(cv2.CAP_PROP_FPS)) | |
| print(fps) | |
| out = cv2.VideoWriter("output.mp4", cv2.VideoWriter_fourcc('x', '2', '6', '4'), fps, (W, H), isColor=True) | |
| for _ in progress.tqdm(range(int(frames_num)), desc='Processing video ({} frames, size {}x{})'.format(int(frames_num), W, H)): | |
| ret, frame = cap.read() # read a frame | |
| result, mask_all = segment_one(frame, mask_generator, seed=2023) | |
| result = (result * 255).astype(np.uint8) | |
| out.write(result) | |
| out.release() | |
| cap.release() | |
| return 'output.mp4' | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| gr.Markdown( | |
| '''# Segment Anything!π | |
| The Segment Anything Model (SAM) produces high quality object masks from input prompts such as points or boxes, and it can be used to generate masks for all objects in an image. It has been trained on a dataset of 11 million images and 1.1 billion masks, and has strong zero-shot performance on a variety of segmentation tasks. | |
| [**Official Project**](https://segment-anything.com/) | |
| ''' | |
| ) | |
| with gr.Row(): | |
| # select model | |
| model_type = gr.Dropdown(["vit_b", "vit_l", "vit_h"], value='vit_b', label="Select Model") | |
| # select device | |
| device = gr.Dropdown(["cpu", "cuda"], value='cuda', label="Select Device") | |
| # εζ° | |
| with gr.Accordion(label='Parameters', open=False): | |
| with gr.Row(): | |
| points_per_side = gr.Number(value=32, label="points_per_side", precision=0, | |
| info='''The number of points to be sampled along one side of the image. The total | |
| number of points is points_per_side**2.''') | |
| pred_iou_thresh = gr.Slider(value=0.88, minimum=0, maximum=1.0, step=0.01, label="pred_iou_thresh", | |
| info='''A filtering threshold in [0,1], using the model's predicted mask quality.''') | |
| stability_score_thresh = gr.Slider(value=0.95, minimum=0, maximum=1.0, step=0.01, label="stability_score_thresh", | |
| info='''A filtering threshold in [0,1], using the stability of the mask under | |
| changes to the cutoff used to binarize the model's mask predictions.''') | |
| min_mask_region_area = gr.Number(value=0, label="min_mask_region_area", precision=0, | |
| info='''If >0, postprocessing will be applied to remove disconnected regions | |
| and holes in masks with area smaller than min_mask_region_area.''') | |
| with gr.Row(): | |
| stability_score_offset = gr.Number(value=1, label="stability_score_offset", | |
| info='''The amount to shift the cutoff when calculated the stability score.''') | |
| box_nms_thresh = gr.Slider(value=0.7, minimum=0, maximum=1.0, step=0.01, label="box_nms_thresh", | |
| info='''The box IoU cutoff used by non-maximal ression to filter duplicate masks.''') | |
| crop_n_layers = gr.Number(value=0, label="crop_n_layers", precision=0, | |
| info='''If >0, mask prediction will be run again on crops of the image. | |
| Sets the number of layers to run, where each layer has 2**i_layer number of image crops.''') | |
| crop_nms_thresh = gr.Slider(value=0.7, minimum=0, maximum=1.0, step=0.01, label="crop_nms_thresh", | |
| info='''The box IoU cutoff used by non-maximal suppression to filter duplicate | |
| masks between different crops.''') | |
| # Show image | |
| with gr.Tab(label='Image'): | |
| with gr.Row().style(equal_height=True): | |
| with gr.Column(): | |
| input_image = gr.Image(type="numpy") | |
| with gr.Row(): | |
| button = gr.Button("Auto!") | |
| with gr.Tab(label='Image+Mask'): | |
| output_image = gr.Image(type='numpy') | |
| with gr.Tab(label='Mask'): | |
| output_mask = gr.Image(type='numpy') | |
| gr.Examples( | |
| examples=[os.path.join(os.path.dirname(__file__), "./images/53960-scaled.jpg"), | |
| os.path.join(os.path.dirname(__file__), "./images/2388455-scaled.jpg"), | |
| os.path.join(os.path.dirname(__file__), "./images/1.jpg"), | |
| os.path.join(os.path.dirname(__file__), "./images/2.jpg"), | |
| os.path.join(os.path.dirname(__file__), "./images/3.jpg"), | |
| os.path.join(os.path.dirname(__file__), "./images/4.jpg"), | |
| os.path.join(os.path.dirname(__file__), "./images/5.jpg"), | |
| os.path.join(os.path.dirname(__file__), "./images/6.jpg"), | |
| os.path.join(os.path.dirname(__file__), "./images/7.jpg"), | |
| os.path.join(os.path.dirname(__file__), "./images/8.jpg"), | |
| ], | |
| inputs=input_image, | |
| outputs=output_image, | |
| ) | |
| # Show video | |
| with gr.Tab(label='Video'): | |
| with gr.Row().style(equal_height=True): | |
| with gr.Column(): | |
| input_video = gr.Video() | |
| with gr.Row(): | |
| button_video = gr.Button("Auto!") | |
| output_video = gr.Video(format='mp4') | |
| gr.Markdown(''' | |
| **Note:** processing video will take a long time, please upload a short video. | |
| ''') | |
| gr.Examples( | |
| examples=[os.path.join(os.path.dirname(__file__), "./images/video1.mp4"), | |
| os.path.join(os.path.dirname(__file__), "./images/video2.mp4") | |
| ], | |
| inputs=input_video, | |
| outputs=output_video | |
| ) | |
| # button image | |
| button.click(inference, inputs=[device, model_type, points_per_side, pred_iou_thresh, stability_score_thresh, | |
| min_mask_region_area, stability_score_offset, box_nms_thresh, crop_n_layers, | |
| crop_nms_thresh, input_image], | |
| outputs=[output_image, output_mask]) | |
| # button video | |
| button_video.click(inference, inputs=[device, model_type, points_per_side, pred_iou_thresh, stability_score_thresh, | |
| min_mask_region_area, stability_score_offset, box_nms_thresh, crop_n_layers, | |
| crop_nms_thresh, input_video], | |
| outputs=[output_video]) | |
| demo.queue().launch(debug=True, enable_queue=True) | |