# copyright (c) 2024 PaddlePaddle Authors. All Rights Reserve. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import cv2 import numpy as np import copy, random import PIL from PIL import Image, ImageDraw, ImageFont from ....common.result import BaseCVResult, StrMixin, JsonMixin from ....utils.color_map import get_colormap from ....common.result import BaseCVResult def draw_segm(im, masks, mask_info, alpha=0.7): """ Draw segmentation on image """ mask_color_id = 0 w_ratio = 0.4 color_list = get_colormap(rgb=True) im = np.array(im).astype("float32") clsid2color = {} masks = np.array(masks) masks = masks.astype(np.uint8) for i in range(masks.shape[0]): mask = masks[i] clsid = random.randint(0, len(get_colormap(rgb=True)) - 1) if clsid not in clsid2color: color_index = i % len(color_list) clsid2color[clsid] = color_list[color_index] color_mask = clsid2color[clsid] for c in range(3): color_mask[c] = color_mask[c] * (1 - w_ratio) + w_ratio * 255 idx = np.nonzero(mask) color_mask = np.array(color_mask) idx0 = np.minimum(idx[0], im.shape[0] - 1) idx1 = np.minimum(idx[1], im.shape[1] - 1) im[idx0, idx1, :] *= 1.0 - alpha im[idx0, idx1, :] += alpha * color_mask # draw box prompt if mask_info[i]["label"] == "box_prompt": x0, y0, x1, y1 = mask_info[i]["prompt"] x0, y0, x1, y1 = int(x0), int(y0), int(x1), int(y1) cv2.rectangle( im, (x0, y0), (x1, y1), tuple(color_mask.astype("int32").tolist()), 1 ) bbox_text = "%s" % mask_info[i]["label"] t_size = cv2.getTextSize(bbox_text, 0, 0.3, thickness=1)[0] cv2.rectangle( im, (x0, y0), (x0 + t_size[0], y0 - t_size[1] - 3), tuple(color_mask.astype("int32").tolist()), -1, ) cv2.putText( im, bbox_text, (x0, y0 - 2), cv2.FONT_HERSHEY_SIMPLEX, 0.3, (0, 0, 0), 1, lineType=cv2.LINE_AA, ) elif mask_info[i]["label"] == "point_prompt": x, y = mask_info[i]["prompt"] bbox_text = "%s" % mask_info[i]["label"] t_size = cv2.getTextSize(bbox_text, 0, 0.3, thickness=1)[0] cv2.circle( im, (x, y), 1, (255, 255, 255), 4, ) cv2.putText( im, bbox_text, (x - t_size[0] // 2, y - t_size[1] - 1), cv2.FONT_HERSHEY_SIMPLEX, 0.3, (255, 255, 255), 1, lineType=cv2.LINE_AA, ) else: raise NotImplementedError( f"Prompt type {mask_info[i]['label']} not implemented." ) return Image.fromarray(im.astype("uint8")) class SAMSegResult(BaseCVResult): """Save Result Transform for SAM""" def __init__(self, data: dict) -> None: data["masks"] = [mask.squeeze(0) for mask in list(data["masks"])] prompts = data["prompts"] assert isinstance(prompts, dict) and len(prompts) == 1 prompt_type, prompts = list(prompts.items())[0] mask_infos = [ { "label": prompt_type, "prompt": p, } for p in prompts ] data["mask_infos"] = mask_infos assert len(data["masks"]) == len(mask_infos) super().__init__(data) def _to_img(self): """apply""" image = Image.fromarray(self["input_img"]) mask_infos = self["mask_infos"] masks = self["masks"] image = draw_segm(image, masks, mask_infos) return {"res": image} def _to_str(self, *args, **kwargs): data = copy.deepcopy(self) data.pop("input_img") data["masks"] = "..." return JsonMixin._to_str(data, *args, **kwargs) def _to_json(self, *args, **kwargs): data = copy.deepcopy(self) data.pop("input_img") return JsonMixin._to_json(data, *args, **kwargs)