init

mask_predictor.py

@@ -0,0 +1,197 @@
+from typing import Any, Dict, List, Optional
+
+import cv2
+import numpy as np
+import torch
+from tqdm import tqdm
+
+from metaseg import SamAutomaticMaskGenerator, SamPredictor, sam_model_registry
+from metaseg.utils import download_model, load_image, load_video
+
+
+class SegAutoMaskPredictor:
+    def __init__(self):
+        self.model = None
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    def load_model(self, model_type):
+        if self.model is None:
+            self.model_path = download_model(model_type)
+            self.model = sam_model_registry[model_type](checkpoint=self.model_path)
+            self.model.to(device=self.device)
+
+        return self.model
+
+    def predict(self, frame, points_per_side, points_per_batch, min_area):
+        frame = load_image(frame)
+        mask_generator = SamAutomaticMaskGenerator(
+            self.model, points_per_side=points_per_side, points_per_batch=points_per_batch, min_mask_region_area=min_area
+        )
+
+        masks = mask_generator.generate(frame)
+
+        return frame, masks
+
+    def save_image(self, source, model_type, points_per_side, points_per_batch, min_area):
+        read_image = load_image(source)
+        image, anns = self.predict(read_image, model_type, points_per_side, points_per_batch, min_area)
+        if len(anns) == 0:
+            return
+
+        sorted_anns = sorted(anns, key=(lambda x: x["area"]), reverse=True)
+        mask_image = np.zeros((anns[0]["segmentation"].shape[0], anns[0]["segmentation"].shape[1], 3), dtype=np.uint8)
+        colors = np.random.randint(0, 255, size=(256, 3), dtype=np.uint8)
+        for i, ann in enumerate(sorted_anns):
+            m = ann["segmentation"]
+            img = np.ones((m.shape[0], m.shape[1], 3), dtype=np.uint8)
+            color = colors[i % 256]
+            for i in range(3):
+                img[:, :, 0] = color[0]
+                img[:, :, 1] = color[1]
+                img[:, :, 2] = color[2]
+            img = cv2.bitwise_and(img, img, mask=m.astype(np.uint8))
+            img = cv2.addWeighted(img, 0.35, np.zeros_like(img), 0.65, 0)
+            mask_image = cv2.add(mask_image, img)
+
+        combined_mask = cv2.add(image, mask_image)
+        cv2.imwrite("output.jpg", combined_mask)
+
+        return "output.jpg"
+
+    def save_video(self, source, model_type, points_per_side, points_per_batch, min_area):
+        cap, out = load_video(source)
+        length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        colors = np.random.randint(0, 255, size=(256, 3), dtype=np.uint8)
+
+        for _ in tqdm(range(length)):
+            ret, frame = cap.read()
+            if not ret:
+                break
+
+            image, anns = self.predict(frame, model_type, points_per_side, points_per_batch, min_area)
+            if len(anns) == 0:
+                continue
+
+            sorted_anns = sorted(anns, key=(lambda x: x["area"]), reverse=True)
+            mask_image = np.zeros(
+                (anns[0]["segmentation"].shape[0], anns[0]["segmentation"].shape[1], 3), dtype=np.uint8
+            )
+
+            for i, ann in enumerate(sorted_anns):
+                m = ann["segmentation"]
+                color = colors[i % 256]  # Her nesne için farklı bir renk kullan
+                img = np.zeros((m.shape[0], m.shape[1], 3), dtype=np.uint8)
+                img[:, :, 0] = color[0]
+                img[:, :, 1] = color[1]
+                img[:, :, 2] = color[2]
+                img = cv2.bitwise_and(img, img, mask=m.astype(np.uint8))
+                img = cv2.addWeighted(img, 0.35, np.zeros_like(img), 0.65, 0)
+                mask_image = cv2.add(mask_image, img)
+
+            combined_mask = cv2.add(frame, mask_image)
+            out.write(combined_mask)
+
+        out.release()
+        cap.release()
+        cv2.destroyAllWindows()
+
+        return "output.mp4"
+
+
+class SegManualMaskPredictor:
+    def __init__(self):
+        self.model = None
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    def load_model(self, model_type):
+        if self.model is None:
+            self.model_path = download_model(model_type)
+            self.model = sam_model_registry[model_type](checkpoint=self.model_path)
+            self.model.to(device=self.device)
+
+        return self.model
+
+    def load_mask(self, mask, random_color):
+        if random_color:
+            color = np.random.rand(3) * 255
+        else:
+            color = np.array([100, 50, 0])
+
+        h, w = mask.shape[-2:]
+        mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
+        mask_image = mask_image.astype(np.uint8)
+        return mask_image
+
+    def load_box(self, box, image):
+        x, y, w, h = int(box[0]), int(box[1]), int(box[2]), int(box[3])
+        cv2.rectangle(image, (x, y), (w, h), (0, 255, 0), 2)
+        return image
+
+    def multi_boxes(self, boxes, predictor, image):
+        input_boxes = torch.tensor(boxes, device=predictor.device)
+        transformed_boxes = predictor.transform.apply_boxes_torch(input_boxes, image.shape[:2])
+        return input_boxes, transformed_boxes
+
+    def predict(
+        self,
+        frame,
+        input_box=None,
+        input_point=None,
+        input_label=None,
+        multimask_output=False,
+    ):
+        predictor = SamPredictor(self.model)
+        predictor.set_image(frame)
+
+        if type(input_box[0]) == list:
+            input_boxes, new_boxes = self.multi_boxes(input_box, predictor, frame)
+
+            masks, _, _ = predictor.predict_torch(
+                point_coords=None,
+                point_labels=None,
+                boxes=new_boxes,
+                multimask_output=False,
+            )
+
+        elif type(input_box[0]) == int:
+            input_boxes = np.array(input_box)[None, :]
+
+            masks, _, _ = predictor.predict(
+                point_coords=input_point,
+                point_labels=input_label,
+                box=input_boxes,
+                multimask_output=multimask_output,
+            )
+
+        return frame, masks, input_boxes
+
+    def save_image(
+        self,
+        source,
+        model_type,
+        input_box=None,
+        input_point=None,
+        input_label=None,
+        multimask_output=False,
+        output_path="v0.jpg",
+    ):
+        read_image = load_image(source)
+        image, anns, boxes = self.predict(read_image, model_type, input_box, input_point, input_label, multimask_output)
+        if len(anns) == 0:
+            return
+
+        if type(input_box[0]) == list:
+            for mask in anns:
+                mask_image = self.load_mask(mask.cpu().numpy(), False)
+
+            for box in boxes:
+                image = self.load_box(box.cpu().numpy(), image)
+
+        elif type(input_box[0]) == int:
+            mask_image = self.load_mask(anns, True)
+            image = self.load_box(input_box, image)
+
+        combined_mask = cv2.add(image, mask_image)
+        cv2.imwrite(output_path, combined_mask)
+
+        return output_path

requirements.txt

@@ -0,0 +1,178 @@
+absl-py==1.3.0
+addict==2.4.0
+albumentations==1.3.0
+alembic @ file:///home/conda/feedstock_root/build_artifacts/alembic_1675830701130/work
+antlr4-python3-runtime==4.9.3
+argon2-cffi @ file:///home/conda/feedstock_root/build_artifacts/argon2-cffi_1640817743617/work
+argon2-cffi-bindings @ file:///D:/bld/argon2-cffi-bindings_1649500465986/work
+backcall @ file:///home/conda/feedstock_root/build_artifacts/backcall_1592338393461/work
+backports.functools-lru-cache @ file:///home/conda/feedstock_root/build_artifacts/backports.functools_lru_cache_1618230623929/work
+beautifulsoup4 @ file:///home/conda/feedstock_root/build_artifacts/beautifulsoup4_1675252249248/work
+black==22.12.0
+bleach @ file:///home/conda/feedstock_root/build_artifacts/bleach_1674535352125/work
+brotlipy @ file:///D:/bld/brotlipy_1648854404735/work
+cachetools==5.2.0
+certifi==2022.12.7
+cffi @ file:///D:/bld/cffi_1656782956657/work
+charset-normalizer @ file:///home/conda/feedstock_root/build_artifacts/charset-normalizer_1661170624537/work
+click==7.1.2
+clip @ git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1
+cloudpickle==2.2.0
+colorama @ file:///home/conda/feedstock_root/build_artifacts/colorama_1666700638685/work
+cryptography @ file:///D:/bld/cryptography_1657174118602/work
+cycler==0.11.0
+Cython==0.29.32
+dataclasses==0.6
+debugpy @ file:///C:/ci/debugpy_1637091911212/work
+decorator @ file:///home/conda/feedstock_root/build_artifacts/decorator_1641555617451/work
+defusedxml @ file:///home/conda/feedstock_root/build_artifacts/defusedxml_1615232257335/work
+detectron2 @ git+https://github.com/facebookresearch/detectron2.git@857d5de21a7789d1bba46694cf608b1cb2ea128a
+easydict==1.10
+entrypoints @ file:///home/conda/feedstock_root/build_artifacts/entrypoints_1643888246732/work
+fasttext==0.9.2
+filelock==3.8.2
+flit_core @ file:///home/conda/feedstock_root/build_artifacts/flit-core_1667734568827/work/source/flit_core
+fonttools==4.38.0
+ftfy==6.1.1
+future==0.18.2
+fuzzywuzzy @ file:///home/conda/feedstock_root/build_artifacts/fuzzywuzzy_1605704184142/work
+fvcore==0.1.5.post20221221
+google-auth==2.15.0
+google-auth-oauthlib==0.4.6
+graphviz==0.20.1
+greenlet @ file:///D:/bld/greenlet_1661444189601/work
+grpcio==1.51.1
+huggingface-hub==0.11.1
+hydra-core==1.3.1
+idna @ file:///home/conda/feedstock_root/build_artifacts/idna_1663625384323/work
+imageio==2.23.0
+imgaug==0.4.0
+imgviz==1.6.2
+importlib-metadata==5.2.0
+importlib-resources @ file:///home/conda/feedstock_root/build_artifacts/importlib_resources_1672681417544/work
+iopath==0.1.9
+ipykernel @ file:///D:/bld/ipykernel_1655369313836/work
+ipython @ file:///D:/bld/ipython_1651240762678/work
+ipython-genutils==0.2.0
+ipywidgets @ file:///home/conda/feedstock_root/build_artifacts/ipywidgets_1671720089366/work
+jedi @ file:///home/conda/feedstock_root/build_artifacts/jedi_1669134318875/work
+Jinja2 @ file:///home/conda/feedstock_root/build_artifacts/jinja2_1654302431367/work
+joblib==1.2.0
+jsonschema==2.6.0
+jupyter @ file:///D:/bld/jupyter_1637233295291/work
+jupyter-console @ file:///home/conda/feedstock_root/build_artifacts/jupyter_console_1655961255101/work
+jupyter_client @ file:///home/conda/feedstock_root/build_artifacts/jupyter_client_1673615989977/work
+jupyter_core @ file:///C:/b/abs_a9330r1z_i/croots/recipe/jupyter_core_1664917313457/work
+jupyterlab-pygments @ file:///home/conda/feedstock_root/build_artifacts/jupyterlab_pygments_1649936611996/work
+jupyterlab-widgets @ file:///home/conda/feedstock_root/build_artifacts/jupyterlab_widgets_1671722028097/work
+kiwisolver==1.4.4
+labelme==5.1.1
+lvis @ git+https://github.com/lvis-dataset/lvis-api.git@35f09cd7c5f313a9bf27b329ca80effe2b0c8a93
+Mako @ file:///home/conda/feedstock_root/build_artifacts/mako_1668568582731/work
+Markdown==3.4.1
+MarkupSafe @ file:///C:/ci/markupsafe_1654508076077/work
+matplotlib==3.5.3
+matplotlib-inline @ file:///home/conda/feedstock_root/build_artifacts/matplotlib-inline_1660814786464/work
+mistune @ file:///home/conda/feedstock_root/build_artifacts/mistune_1675771498296/work
+-e d:\cvpr_2023_dataset\detpro
+mmpycocotools==12.0.3
+mss==7.0.1
+mypy-extensions==0.4.3
+natsort==8.2.0
+nbclient @ file:///home/conda/feedstock_root/build_artifacts/nbclient_1662750566673/work
+nbconvert @ file:///home/conda/feedstock_root/build_artifacts/nbconvert-meta_1674590374792/work
+nbformat @ file:///home/conda/feedstock_root/build_artifacts/nbformat_1617383142101/work
+nbgrader @ file:///D:/bld/nbgrader_1615406840332/work
+nest-asyncio @ file:///home/conda/feedstock_root/build_artifacts/nest-asyncio_1664684991461/work
+networkx==2.6.3
+ninja==1.11.1
+nltk==3.8
+notebook @ file:///D:/bld/notebook_1616419450136/work
+numpy==1.21.6
+oauthlib==3.2.2
+omegaconf==2.3.0
+opencv-python==4.6.0.66
+opencv-python-headless==4.6.0.66
+packaging @ file:///home/conda/feedstock_root/build_artifacts/packaging_1673482170163/work
+pandocfilters @ file:///home/conda/feedstock_root/build_artifacts/pandocfilters_1631603243851/work
+parso @ file:///home/conda/feedstock_root/build_artifacts/parso_1638334955874/work
+pathspec==0.10.3
+pickleshare @ file:///home/conda/feedstock_root/build_artifacts/pickleshare_1602536217715/work
+Pillow==9.3.0
+platformdirs==2.6.0
+portalocker==2.6.0
+prometheus-client @ file:///home/conda/feedstock_root/build_artifacts/prometheus_client_1674535637125/work
+prompt-toolkit @ file:///home/conda/feedstock_root/build_artifacts/prompt-toolkit_1670414775770/work
+protobuf==3.20.3
+psutil @ file:///C:/Windows/Temp/abs_b2c2fd7f-9fd5-4756-95ea-8aed74d0039flsd9qufz/croots/recipe/psutil_1656431277748/work
+pyasn1==0.4.8
+pyasn1-modules==0.2.8
+pybind11==2.10.2
+pycocotools==2.0.6
+pycparser @ file:///home/conda/feedstock_root/build_artifacts/pycparser_1636257122734/work
+Pygments @ file:///home/conda/feedstock_root/build_artifacts/pygments_1672682006896/work
+pyOpenSSL @ file:///home/conda/feedstock_root/build_artifacts/pyopenssl_1663846997386/work
+pyparsing==3.0.9
+PyQt5==5.15.4
+pyqt5-plugins==5.15.4.2.2
+PyQt5-Qt5==5.15.2
+PyQt5-sip==12.11.0
+pyqt5-tools==5.15.4.3.2
+PyQtChart==5.12
+PyQtWebEngine==5.12.1
+PySocks @ file:///D:/bld/pysocks_1648857448477/work
+python-dateutil @ file:///home/conda/feedstock_root/build_artifacts/python-dateutil_1626286286081/work
+python-dotenv==0.21.0
+python-Levenshtein @ file:///D:/bld/python-levenshtein_1649607057775/work
+PyWavelets==1.3.0
+pywin32==305
+pywinpty @ file:///C:/ci_310/pywinpty_1644230983541/work/target/wheels/pywinpty-2.0.2-cp37-none-win_amd64.whl
+PyYAML==6.0
+pyzmq @ file:///C:/ci/pyzmq_1657597757589/work
+qt5-applications==5.15.2.2.2
+qt5-tools==5.15.2.1.2
+qtconsole @ file:///home/conda/feedstock_root/build_artifacts/qtconsole-base_1667404144336/work
+QtPy @ file:///home/conda/feedstock_root/build_artifacts/qtpy_1667873092748/work
+qudida==0.0.4
+regex==2022.10.31
+requests @ file:///home/conda/feedstock_root/build_artifacts/requests_1673863902341/work
+requests-oauthlib==1.3.1
+rsa==4.9
+scikit-image==0.19.3
+scikit-learn==1.0.2
+scipy==1.7.3
+Send2Trash @ file:///home/conda/feedstock_root/build_artifacts/send2trash_1628511208346/work
+shapely==2.0.0
+six @ file:///home/conda/feedstock_root/build_artifacts/six_1620240208055/work
+soupsieve @ file:///home/conda/feedstock_root/build_artifacts/soupsieve_1658207591808/work
+SQLAlchemy @ file:///D:/bld/sqlalchemy_1659998874450/work
+tabulate==0.9.0
+tensorboard==2.11.0
+tensorboard-data-server==0.6.1
+tensorboard-plugin-wit==1.8.1
+termcolor==2.1.1
+terminado @ file:///D:/bld/terminado_1652790822513/work
+terminaltables==3.1.10
+threadpoolctl==3.1.0
+tifffile==2021.11.2
+timm==0.6.12
+tinycss2 @ file:///home/conda/feedstock_root/build_artifacts/tinycss2_1666100256010/work
+tomli==2.0.1
+torch==1.13.1+cu116
+torchaudio==0.13.1+cu116
+torchvision==0.14.1+cu116
+tornado @ file:///D:/bld/tornado_1656937938087/work
+tqdm==4.64.1
+traitlets @ file:///home/conda/feedstock_root/build_artifacts/traitlets_1675110562325/work
+typed-ast==1.5.4
+typing_extensions @ file:///home/conda/feedstock_root/build_artifacts/typing_extensions_1665144421445/work
+urllib3 @ file:///home/conda/feedstock_root/build_artifacts/urllib3_1673452138552/work
+wcwidth @ file:///home/conda/feedstock_root/build_artifacts/wcwidth_1673864653149/work
+webencodings==0.5.1
+Werkzeug==2.2.2
+widgetsnbextension @ file:///home/conda/feedstock_root/build_artifacts/widgetsnbextension_1672066693230/work
+win-inet-pton @ file:///D:/bld/win_inet_pton_1667051142467/work
+wincertstore==0.2
+yacs==0.1.8
+yapf==0.32.0
+zipp @ file:///home/conda/feedstock_root/build_artifacts/zipp_1675982654259/work