add files

.github/workflows/replicate-push.yml

@@ -0,0 +1,23 @@
+name: Push to Replicate
+
+on:
+  workflow_dispatch:
+    inputs:
+      model_name:
+        description: "owner/model-name (Replicate)"
+        required: true
+        type: string
+
+jobs:
+  push:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Setup Cog
+        uses: replicate/setup-cog@v2
+        with:
+          token: ${{ secrets.REPLICATE_API_TOKEN }}
+
+      - name: Push
+        run: cog push r8.im/${{ inputs.model_name }}

.gitignore

@@ -3,4 +3,4 @@
 *.pkl
 *.pth
 result*
-weights*
+# weights*

README.md

@@ -1,74 +1,83 @@
 ## CRAFT: Character-Region Awareness For Text detection
+
 Official Pytorch implementation of CRAFT text detector | [Paper](https://arxiv.org/abs/1904.01941) | [Pretrained Model](https://drive.google.com/open?id=1Jk4eGD7crsqCCg9C9VjCLkMN3ze8kutZ) | [Supplementary](https://youtu.be/HI8MzpY8KMI)
 
 **[Youngmin Baek](mailto:[email protected]), Bado Lee, Dongyoon Han, Sangdoo Yun, Hwalsuk Lee.**
- 
+
 Clova AI Research, NAVER Corp.
 
 ### Sample Results
 
 ### Overview
-PyTorch implementation for CRAFT text detector that effectively detect text area by exploring each character region and affinity between characters. The bounding box of texts are obtained by simply finding minimum bounding rectangles on binary map after thresholding character region and affinity scores. 
+
+PyTorch implementation for CRAFT text detector that effectively detect text area by exploring each character region and affinity between characters. The bounding box of texts are obtained by simply finding minimum bounding rectangles on binary map after thresholding character region and affinity scores.
 
 <img width="1000" alt="teaser" src="./figures/craft_example.gif">
 
 ## Updates
+
 **13 Jun, 2019**: Initial update
 **20 Jul, 2019**: Added post-processing for polygon result
 **28 Sep, 2019**: Added the trained model on IC15 and the link refiner
 
-
 ## Getting started
+
 ### Install dependencies
+
 #### Requirements
+
 - PyTorch>=0.4.1
 - torchvision>=0.2.1
 - opencv-python>=3.4.2
 - check requiremtns.txt
+
 ```
 pip install -r requirements.txt
 ```
 
 ### Training
-The code for training is not included in this repository, and we cannot release the full training code for IP reason.
 
+The code for training is not included in this repository, and we cannot release the full training code for IP reason.
 
 ### Test instruction using pretrained model
+
 - Download the trained models
-
- *Model name* | *Used datasets* | *Languages* | *Purpose* | *Model Link* |
- | :--- | :--- | :--- | :--- | :--- |
-General | SynthText, IC13, IC17 | Eng + MLT | For general purpose | [Click](https://drive.google.com/open?id=1Jk4eGD7crsqCCg9C9VjCLkMN3ze8kutZ)
-IC15 | SynthText, IC15 | Eng | For IC15 only | [Click](https://drive.google.com/open?id=1i2R7UIUqmkUtF0jv_3MXTqmQ_9wuAnLf)
-LinkRefiner | CTW1500 | - | Used with the General Model | [Click](https://drive.google.com/open?id=1XSaFwBkOaFOdtk4Ane3DFyJGPRw6v5bO)
+  | _Model name_ | _Used datasets_ | _Languages_ | _Purpose_ | _Model Link_ |
+  | :----------- | :-------------------- | :---------- | :-------------------------- | :-------------------------------------------------------------------------- |
+  | General | SynthText, IC13, IC17 | Eng + MLT | For general purpose | [Click](https://drive.google.com/open?id=1Jk4eGD7crsqCCg9C9VjCLkMN3ze8kutZ) |
+  | IC15 | SynthText, IC15 | Eng | For IC15 only | [Click](https://drive.google.com/open?id=1i2R7UIUqmkUtF0jv_3MXTqmQ_9wuAnLf) |
+  | LinkRefiner | CTW1500 | - | Used with the General Model | [Click](https://drive.google.com/open?id=1XSaFwBkOaFOdtk4Ane3DFyJGPRw6v5bO) |
 
 * Run with pretrained model
-``` (with python 3.7)
+
+```(with python 3.7)
 python test.py --trained_model=[weightfile] --test_folder=[folder path to test images]
 ```
 
 The result image and socre maps will be saved to `./result` by default.
 
 ### Arguments
-* `--trained_model`: pretrained model
-* `--text_threshold`: text confidence threshold
-* `--low_text`: text low-bound score
-* `--link_threshold`: link confidence threshold
-* `--cuda`: use cuda for inference (default:True)
-* `--canvas_size`: max image size for inference
-* `--mag_ratio`: image magnification ratio
-* `--poly`: enable polygon type result
-* `--show_time`: show processing time
-* `--test_folder`: folder path to input images
-* `--refine`: use link refiner for sentense-level dataset
-* `--refiner_model`: pretrained refiner model
 
+- `--trained_model`: pretrained model
+- `--text_threshold`: text confidence threshold
+- `--low_text`: text low-bound score
+- `--link_threshold`: link confidence threshold
+- `--cuda`: use cuda for inference (default:True)
+- `--canvas_size`: max image size for inference
+- `--mag_ratio`: image magnification ratio
+- `--poly`: enable polygon type result
+- `--show_time`: show processing time
+- `--test_folder`: folder path to input images
+- `--refine`: use link refiner for sentense-level dataset
+- `--refiner_model`: pretrained refiner model
 
 ## Links
+
 - WebDemo : https://demo.ocr.clova.ai/
 - Repo of recognition : https://github.com/clovaai/deep-text-recognition-benchmark
 
 ## Citation
+
 ```
 @inproceedings{baek2019character,
   title={Character Region Awareness for Text Detection},
@@ -80,6 +89,7 @@ The result image and socre maps will be saved to `./result` by default.
 ```
 
 ## License
+
 ```
 Copyright (c) 2019-present NAVER Corp.
 
@@ -101,3 +111,9 @@ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
 THE SOFTWARE.
 ```
+
+---
+
+cd D:\python\CRAFT-pytorch
+python -m venv venv
+.\venv\Scripts\Activate.ps1

cog.yaml

@@ -0,0 +1,16 @@
+build:
+  python_version: "3.10"
+  system_packages:
+    - "libgl1"
+    - "libglib2.0-0"
+  python_packages:
+    - torch==2.0.1
+    - torchvision==0.15.2
+    - opencv-python
+    - matplotlib
+    - scikit-image
+    - scipy
+    - Pillow
+    - numpy
+
+predict: "predict.py:Predictor"

predict.py

@@ -0,0 +1,67 @@
+import os
+import cv2
+import torch
+import numpy as np
+from cog import BasePredictor, Input, Path
+
+import craft_utils
+import imgproc
+from craft import CRAFT
+
+
+class Predictor(BasePredictor):
+    def setup(self):
+        # Load model once
+        self.net = CRAFT()
+        weight_path = "craft_mlt_25k.pth"
+
+        # Download weights if not present
+        if not os.path.exists(weight_path):
+            import requests
+            url = "https://github.com/clovaai/CRAFT-pytorch/releases/download/1.0/craft_mlt_25k.pth"
+            r = requests.get(url)
+            with open(weight_path, "wb") as f:
+                f.write(r.content)
+
+        self.net.load_state_dict(
+            torch.load(weight_path, map_location="cpu")
+        )
+        self.net.eval()
+
+    def predict(
+        self,
+        image: Path = Input(description="Input image"),
+        text_threshold: float = Input(default=0.7, description="Text confidence threshold"),
+        link_threshold: float = Input(default=0.4, description="Link confidence threshold"),
+        low_text: float = Input(default=0.4, description="Low text threshold"),
+    ) -> dict:
+        # Load image
+        img = imgproc.loadImage(str(image))
+
+        # Run detection
+        bboxes, polys, score_text = craft_utils.test_net(
+            self.net,
+            img,
+            text_threshold=text_threshold,
+            link_threshold=link_threshold,
+            low_text=low_text,
+            cuda=False,
+        )
+
+        # Draw boxes on image
+        vis = img.copy()
+        for box in bboxes:
+            pts = np.array(box).astype(np.int32).reshape((-1, 1, 2))
+            cv2.polylines(vis, [pts], True, (0, 255, 0), 2)
+
+        # Save output image
+        out_path = "/tmp/output.jpg"
+        cv2.imwrite(out_path, vis)
+
+        # Convert boxes to JSON serializable format
+        boxes_json = [np.array(box).astype(float).tolist() for box in bboxes]
+
+        return {
+            "output_image": Path(out_path),
+            "boxes": boxes_json,
+        }