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| # Reference: https://huggingface.co/spaces/haotiz/glip-zeroshot-demo/blob/main/app.py | |
| import requests | |
| import os | |
| from io import BytesIO | |
| from PIL import Image | |
| import numpy as np | |
| from pathlib import Path | |
| import gradio as gr | |
| import warnings | |
| warnings.filterwarnings("ignore") | |
| os.system("python setup.py build develop --user") | |
| from maskrcnn_benchmark.config import cfg | |
| from maskrcnn_benchmark.engine.predictor_glip import GLIPDemo | |
| config_file = "configs/pretrain_new/desco_glip.yaml" | |
| weight_file = "MODEL/desco_glip_tiny.pth" | |
| # update the config options with the config file | |
| # manual override some options | |
| cfg.local_rank = 0 | |
| cfg.num_gpus = 1 | |
| cfg.merge_from_file(config_file) | |
| cfg.merge_from_list(["MODEL.WEIGHT", weight_file]) | |
| cfg.merge_from_list(["MODEL.DEVICE", "cuda"]) | |
| glip_demo = GLIPDemo( | |
| cfg, | |
| min_image_size=800, | |
| confidence_threshold=0.7, | |
| show_mask_heatmaps=False | |
| ) | |
| config_file = "configs/pretrain_new/desco_fiber.yaml" | |
| weight_file = "MODEL/desco_fiber_base.pth" | |
| from copy import deepcopy | |
| cfg = deepcopy(cfg) | |
| cfg.merge_from_file(config_file) | |
| cfg.merge_from_list(["MODEL.WEIGHT", weight_file]) | |
| cfg.merge_from_list(["MODEL.DEVICE", "cuda"]) | |
| fiber_demo = GLIPDemo( | |
| cfg, | |
| min_image_size=800, | |
| confidence_threshold=0.7, | |
| show_mask_heatmaps=False | |
| ) | |
| athetics_params = { | |
| "skip_name": False, # whether we overlay the phrase over the box | |
| "override_color": (0, 90, 190), | |
| "text_size": 1.0, | |
| "text_pixel": 3, | |
| "box_alpha": 1.0, | |
| "box_pixel": 5, | |
| "text_offset_original": 8, # distance between text and box | |
| } | |
| def resize_image_by_height(image, new_height=500): | |
| import cv2 | |
| height, width, _ = image.shape | |
| aspect_ratio = width / height | |
| new_width = int(new_height * aspect_ratio) | |
| resized_image = cv2.resize(image, (new_width, new_height)) | |
| return resized_image | |
| def resize_image_by_width(image, new_width=500): | |
| import cv2 | |
| height, width, _ = image.shape | |
| aspect_ratio = height / width | |
| new_height = int(new_width * aspect_ratio) | |
| resized_image = cv2.resize(image, (new_width, new_height)) | |
| return resized_image | |
| def predict(image, text, ground_tokens=""): | |
| #image = resize_image_by_width(image) | |
| print(image.shape) | |
| max_len = max(image.shape) | |
| import math | |
| athetics_params["text_size"] = math.ceil(max_len/1000) | |
| athetics_params["text_pixel"] = math.ceil(max_len/1000*3) | |
| ground_tokens = None if ground_tokens.strip() == "" else ground_tokens.strip().split(";") | |
| result, _ = glip_demo.run_on_web_image(deepcopy(image[:, :, [2, 1, 0]]), text, 0.5, ground_tokens, **athetics_params) | |
| fiber_result, _ = fiber_demo.run_on_web_image(deepcopy(image[:, :, [2, 1, 0]]), text, 0.5, ground_tokens, **athetics_params) | |
| return result[:, :, [2, 1, 0]], fiber_result[:, :, [2, 1, 0]] | |
| image = gr.inputs.Image() | |
| gr.Interface( | |
| description="Object Recognition with DesCo (https://github.com/liunian-harold-li/DesCo)", | |
| fn=predict, | |
| inputs=["image", "text", "text"], | |
| outputs=[ | |
| gr.outputs.Image( | |
| type="pil", | |
| label="DesCo-GLIP" | |
| ), | |
| gr.outputs.Image( | |
| type="pil", | |
| label="DesCo-FIBER" | |
| ), | |
| ], | |
| examples=[ | |
| ["./1.jpg", "A clown making a balloon animal for a pretty lady.", "clown"], | |
| ["./1.jpg", "A clown kicking a soccer ball for a pretty lady.", "clown"], | |
| ["./2.jpg", "A kind of tool, wooden handle with a round head.", "tool"], | |
| ["./3.jpg", "Bumblebee, yellow with black accents.", "Bumblebee"], | |
| ], | |
| article=Path("docs/intro.md").read_text() | |
| ).launch() | |