feat: click model

.env.example

@@ -2,3 +2,4 @@ DEBUG=false
 DOMAIN=
 HOST=0.0.0.0
 PORT=8888
+APPKEY=key

pyproject.toml

@@ -5,8 +5,8 @@ description = "Add your description here"
 readme = "README.md"
 requires-python = ">=3.12"
 dependencies = [
-    "bili-ticket-gt-python>=0.2.7",
     "cryptography>=43.0.3",
+    "ddddocr>=1.5.6",
     "fastapi>=0.115.4",
     "httpx>=0.27.2",
     "numpy>=2.1.3",

requirements.txt

@@ -1,14 +1,14 @@
 # This file was autogenerated by uv via the following command:
-#    uv export -o requirements.txt --no-hashes
+#    uv export -o .\requirements.txt --no-hashes
 annotated-types==0.7.0
 anyio==4.6.2.post1
-bili-ticket-gt-python==0.2.7
 certifi==2024.8.30
 cffi==1.17.1 ; platform_python_implementation != 'PyPy'
 click==8.1.7
 colorama==0.4.6 ; platform_system == 'Windows'
 coloredlogs==15.0.1
 cryptography==43.0.3
+ddddocr==1.5.6
 fastapi==0.115.4
 flatbuffers==24.3.25
 h11==0.14.0
@@ -20,6 +20,7 @@ mpmath==1.3.0
 networkx==3.4.2
 numpy==2.1.3
 onnxruntime==1.20.0
+opencv-python-headless==4.10.0.84
 packaging==24.1
 persica==0.1.0a4
 pillow==11.0.0

src/core/click_model.py

@@ -1,13 +1,77 @@
-import bili_ticket_gt_python
+from pathlib import Path
+
+import ddddocr
+from PIL import Image
+from io import BytesIO
+import numpy as np
+import onnxruntime as ort
+
 from persica.factory.component import AsyncInitializingComponent
 
+assets_path = Path("src") / "assets"
+model_path = assets_path / "siamese.onnx"
+
 
 class ClickModel(AsyncInitializingComponent):
     def __init__(self):
-        self.click = bili_ticket_gt_python.ClickPy()
+        self.det = ddddocr.DdddOcr(det=True, ocr=False)
+        self.siamese = ort.InferenceSession(model_path)
 
-    def predict(self, url: str):
+    def get_icons(self, image_bytes: bytes):
-        key = self.click.calculate_key(url)
+        def remove_subicon(data):
-        if key:
+            result = []
-            return key.split(",")
+            for i in data:
-        return []
+                remove = False
+                for j in data:
+                    if (
+                            i != j
+                            and i[0] >= j[0]
+                            and i[1] >= j[1]
+                            and i[2] <= j[2]
+                            and i[3] <= j[3]
+                    ):
+                        remove = True
+                if not remove:
+                    result.append(i)
+            return result
+
+        img = Image.open(BytesIO(image_bytes))
+        bboxes = self.det.detection(image_bytes)
+        small_bboxes = [i for i in bboxes if i[1] > 300]
+        big_bboxes = [i for i in bboxes if i[1] <= 300]
+        small_bboxes = sorted(small_bboxes, key=lambda x: x[0])
+        big_bboxes = remove_subicon(big_bboxes)
+        small_images = [img.crop(i) for i in small_bboxes]
+        big_images = [img.crop(i) for i in big_bboxes]
+        return big_bboxes, small_images, big_images
+
+    def calculate_similarity(self, img1: "Image", img2: "Image"):
+        def preprocess_image(img, size=(105, 105)):
+            img_resized = img.resize(size)
+            img_normalized = np.array(img_resized) / 255.0
+            img_transposed = np.transpose(img_normalized, (2, 0, 1))
+            img_expanded = np.expand_dims(img_transposed, axis=0).astype(np.float32)
+            return img_expanded
+
+        image_data_1 = preprocess_image(img1)
+        image_data_2 = preprocess_image(img2)
+
+        inputs = {"input": image_data_1, "input.53": image_data_2}
+
+        output = self.siamese.run(None, inputs)
+
+        output_sigmoid = 1 / (1 + np.exp(-output[0]))
+        similarity_score = output_sigmoid[0][0]
+
+        return similarity_score
+
+    def predict(self, image_bytes: bytes):
+        big_bboxes, small_images, big_images = self.get_icons(image_bytes)
+        ans = []
+        for i in small_images:
+            similarities = [self.calculate_similarity(i, j) for j in big_images]
+            target_bbox = big_bboxes[similarities.index(max(similarities))]
+            x = (target_bbox[0] + target_bbox[2]) / 2
+            y = (target_bbox[1] + target_bbox[3]) / 2
+            ans.append((x, y))
+        return ans

src/core/web_app.py

@@ -6,7 +6,7 @@ from fastapi import FastAPI
 from persica.factory.component import AsyncInitializingComponent
 from starlette.middleware.trustedhost import TrustedHostMiddleware
 
-from src.env import DOMAIN, DEBUG, PORT
+from src.env import DOMAIN, DEBUG, HOST, PORT
 
 
 class WebApp(AsyncInitializingComponent):
@@ -27,7 +27,7 @@ class WebApp(AsyncInitializingComponent):
     async def start(self):
         self.init_web()
         self.web_server = uvicorn.Server(
-            config=uvicorn.Config(self.app, host="0.0.0.0", port=PORT)
+            config=uvicorn.Config(self.app, host=HOST, port=PORT)
         )
         server_config = self.web_server.config
         server_config.setup_event_loop()

src/route/predict.py

@@ -36,17 +36,22 @@ class PredictPlugin(AsyncInitializingComponent):
         self.click_model = click_model
         self.resnet_model = resnet_model
 
-    def get_point_list(self, pic_content: bytes, pic_url: str, pic_type: str):
+    def get_point_list(self, pic_content: bytes, _: str, pic_type: str):
         point_list = []
         if pic_type == "nine":
             # 九宫格
             points = self.resnet_model.predict(pic_content)
             for x, y in points:
                 point_list.append(f"{x}_{y}")
+        elif pic_type == "icon":
+            # 点按
+            points = self.click_model.predict(pic_content)
+            for x, y in points:
+                left = round(x / 333 * 10000)
+                top = round(y / 333 * 10000)
+                point_list.append(f"{left}_{top}")
         else:
-            # 滑动验证码
+            return []
-            key = self.click_model.predict(pic_url)
-            point_list = key
         return point_list
 
     def vvv(self, crack: Crack, retry: int = 3):

uv.lock

@@ -1,8 +1,12 @@
 version = 1
 requires-python = ">=3.12"
 resolution-markers = [
-    "python_full_version < '3.13'",
+    "python_full_version < '3.13' and platform_system == 'Darwin'",
-    "python_full_version >= '3.13'",
+    "python_full_version < '3.13' and platform_machine == 'aarch64' and platform_system == 'Linux'",
+    "(python_full_version < '3.13' and platform_machine != 'aarch64' and platform_system != 'Darwin') or (python_full_version < '3.13' and platform_system != 'Darwin' and platform_system != 'Linux')",
+    "python_full_version >= '3.13' and platform_system == 'Darwin'",
+    "python_full_version >= '3.13' and platform_machine == 'aarch64' and platform_system == 'Linux'",
+    "(python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_system != 'Darwin') or (python_full_version >= '3.13' and platform_system != 'Darwin' and platform_system != 'Linux')",
 ]
 
 [[package]]
@@ -27,18 +31,6 @@ wheels = [
     { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/e4/f5/f2b75d2fc6f1a260f340f0e7c6a060f4dd2961cc16884ed851b0d18da06a/anyio-4.6.2.post1-py3-none-any.whl", hash = "sha256:6d170c36fba3bdd840c73d3868c1e777e33676a69c3a72cf0a0d5d6d8009b61d", size = 90377 },
 ]
 
-[[package]]
-name = "bili-ticket-gt-python"
-version = "0.2.7"
-source = { registry = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple" }
-sdist = { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/41/21/2cba4545e796dd3f2544e36d2b84f1ba69e8b39d3a317e23677e51f4477e/bili_ticket_gt_python-0.2.7.tar.gz", hash = "sha256:f087bdfa18e971b1f7c8da26dacab2e69578293207a30da58f50f1ef3d5c9c7e", size = 261373 }
-wheels = [
-    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/e9/12/6a2df55e98f8ebf7f983a892f0083e90ab8f2b65640c9002e461067aa481/bili_ticket_gt_python-0.2.7-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:000198cc643b0a8358711006662d6207bfbe2a8b53a26bc61bfa51c7a8429576", size = 87622391 },
-    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/ce/a0/8f531e5fcab92936f71cf1133e08aaaf3bd3fa8bb51409a6e4611bd0935c/bili_ticket_gt_python-0.2.7-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:3c17222e1b454e196c3a58795640b1d48519f65036780e5776221f0762902f49", size = 86557330 },
-    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/7f/b5/03b7850c36b21c4161a4dd4cae2f22bb48f29596f9903ea0337b8e943edb/bili_ticket_gt_python-0.2.7-cp312-cp312-manylinux_2_35_x86_64.whl", hash = "sha256:638aff60325d5294d7ee375abf4f490b9c4492c5af27dab6954f00804ba7b614", size = 93546997 },
-    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/62/9d/5bfd2a1af11983f7c3ae99b2ff6cb2f2d8cb7df68c87ba9cdb55e5934841/bili_ticket_gt_python-0.2.7-cp312-none-win_amd64.whl", hash = "sha256:0a24bf2b567f5814eb8a699faf29a142a39c98f9078bfef11efd8a598b565b45", size = 85581487 },
-]
-
 [[package]]
 name = "certifi"
 version = "2024.8.30"
@@ -143,6 +135,21 @@ wheels = [
     { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/87/5c/3dab83cc4aba1f4b0e733e3f0c3e7d4386440d660ba5b1e3ff995feb734d/cryptography-43.0.3-cp39-abi3-win_amd64.whl", hash = "sha256:0c580952eef9bf68c4747774cde7ec1d85a6e61de97281f2dba83c7d2c806362", size = 3068026 },
 ]
 
+[[package]]
+name = "ddddocr"
+version = "1.5.6"
+source = { registry = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple" }
+dependencies = [
+    { name = "numpy" },
+    { name = "onnxruntime" },
+    { name = "opencv-python-headless" },
+    { name = "pillow" },
+]
+sdist = { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/0e/cf/1243d5f0d03763a287375366f68eadb5c14418f5b3df00c09eb971e526a7/ddddocr-1.5.6.tar.gz", hash = "sha256:2839a940bfabe02e3284ef3f9d2a037292aa9f641f355b43a9b70bece9e1b73d", size = 75825027 }
+wheels = [
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/54/74/418c1c0be49463799f9eeb307a8aa4013ff5fca5e0387f0ef2762fcdb4e2/ddddocr-1.5.6-py3-none-any.whl", hash = "sha256:f13865b00e42de5c2507c1889ba73c2bacd218a49d15b928c2a5c82667062ac5", size = 75868010 },
+]
+
 [[package]]
 name = "fastapi"
 version = "0.115.4"
@@ -171,8 +178,8 @@ name = "geetestbypass"
 version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
-    { name = "bili-ticket-gt-python" },
     { name = "cryptography" },
+    { name = "ddddocr" },
     { name = "fastapi" },
     { name = "httpx" },
     { name = "numpy" },
@@ -188,8 +195,8 @@ dependencies = [
 
 [package.metadata]
 requires-dist = [
-    { name = "bili-ticket-gt-python", specifier = ">=0.2.7" },
     { name = "cryptography", specifier = ">=43.0.3" },
+    { name = "ddddocr", specifier = ">=1.5.6" },
     { name = "fastapi", specifier = ">=0.115.4" },
     { name = "httpx", specifier = ">=0.27.2" },
     { name = "numpy", specifier = ">=2.1.3" },
@@ -344,6 +351,23 @@ wheels = [
     { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/ca/4f/d1d7642706ddce8c253255b52cf8a6fdb6d4aca171a7a476188039816b79/onnxruntime-1.20.0-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:dc4e7c10c98c1f407835448c26a7e14ebff3234f131e1fbc53bd9500c828df89", size = 13319499 },
 ]
 
+[[package]]
+name = "opencv-python-headless"
+version = "4.10.0.84"
+source = { registry = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple" }
+dependencies = [
+    { name = "numpy" },
+]
+sdist = { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/2f/7e/d20f68a5f1487adf19d74378d349932a386b1ece3be9be9915e5986db468/opencv-python-headless-4.10.0.84.tar.gz", hash = "sha256:f2017c6101d7c2ef8d7bc3b414c37ff7f54d64413a1847d89970b6b7069b4e1a", size = 95117755 }
+wheels = [
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/1c/9b/583c8d9259f6fc19413f83fd18dd8e6cbc8eefb0b4dc6da52dd151fe3272/opencv_python_headless-4.10.0.84-cp37-abi3-macosx_11_0_arm64.whl", hash = "sha256:a4f4bcb07d8f8a7704d9c8564c224c8b064c63f430e95b61ac0bffaa374d330e", size = 54835657 },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/c0/7b/b4c67f5dad7a9a61c47f7a39e4050e8a4628bd64b3c3daaeb755d759f928/opencv_python_headless-4.10.0.84-cp37-abi3-macosx_12_0_x86_64.whl", hash = "sha256:5ae454ebac0eb0a0b932e3406370aaf4212e6a3fdb5038cc86c7aea15a6851da", size = 56475470 },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/91/61/f838ce2046f3ec3591ea59ea3549085e399525d3b4558c4ed60b55ed88c0/opencv_python_headless-4.10.0.84-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:46071015ff9ab40fccd8a163da0ee14ce9846349f06c6c8c0f2870856ffa45db", size = 29329705 },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/d1/09/248f86a404567303cdf120e4a301f389b68e3b18e5c0cc428de327da609c/opencv_python_headless-4.10.0.84-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:377d08a7e48a1405b5e84afcbe4798464ce7ee17081c1c23619c8b398ff18295", size = 49858781 },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/30/c0/66f88d58500e990a9a0a5c06f98862edf1d0a3a430781218a8c193948438/opencv_python_headless-4.10.0.84-cp37-abi3-win32.whl", hash = "sha256:9092404b65458ed87ce932f613ffbb1106ed2c843577501e5768912360fc50ec", size = 28675298 },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/26/d0/22f68eb23eea053a31655960f133c0be9726c6a881547e6e9e7e2a946c4f/opencv_python_headless-4.10.0.84-cp37-abi3-win_amd64.whl", hash = "sha256:afcf28bd1209dd58810d33defb622b325d3cbe49dcd7a43a902982c33e5fad05", size = 38754031 },
+]
+
 [[package]]
 name = "packaging"
 version = "24.1"