From 05463814b0063b81108cd3daffe777dc52075e28 Mon Sep 17 00:00:00 2001 From: taskmgr818 Date: Sun, 3 Nov 2024 17:22:24 +0800 Subject: [PATCH] first commit --- LICENSE | 661 +++++++++++++++++++++++++++++++++++++++ README.md | 66 ++++ crack.py | 533 +++++++++++++++++++++++++++++++ main.py | 82 +++++ mousepath.json | 336 ++++++++++++++++++++ predict/__init__.py | 8 + predict/icon/__init__.py | 0 predict/icon/icon.py | 73 +++++ predict/nine/__init__.py | 0 predict/nine/icon/0.jpg | Bin 0 -> 1116 bytes predict/nine/icon/1.jpg | Bin 0 -> 979 bytes predict/nine/icon/10.jpg | Bin 0 -> 994 bytes predict/nine/icon/11.jpg | Bin 0 -> 987 bytes predict/nine/icon/12.jpg | Bin 0 -> 1037 bytes predict/nine/icon/13.jpg | Bin 0 -> 861 bytes predict/nine/icon/14.jpg | Bin 0 -> 982 bytes predict/nine/icon/15.jpg | Bin 0 -> 1188 bytes predict/nine/icon/16.jpg | Bin 0 -> 1184 bytes predict/nine/icon/17.jpg | Bin 0 -> 1064 bytes predict/nine/icon/18.jpg | Bin 0 -> 1190 bytes predict/nine/icon/19.jpg | Bin 0 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There are many ways you could offer source, and different +solutions will be better for different programs; see section 13 for the +specific requirements. + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU AGPL, see +. diff --git a/README.md b/README.md new file mode 100644 index 0000000..9502a83 --- /dev/null +++ b/README.md @@ -0,0 +1,66 @@ +# geetest-v3-click-server +极验三代九宫格、图标点选的打码服务端 + +**本项目仅供学习交流使用，请勿用于商业用途，否则后果自负。** + +**本项目仅供学习交流使用，请勿用于商业用途，否则后果自负。** + +**本项目仅供学习交流使用，请勿用于商业用途，否则后果自负。** + +# 使用方法 + +## 服务端 +* [训练模型](https://github.com/taskmgr818/MihoyoBBS-gt-server/blob/main/train.md) + +* 将resnet18.onnx、siamese.onnx分别放入predict/nine、predict/icon + +* 安装依赖 + +```commandline +pip install -r requirements.txt +``` +* 运行 + +```commandline +python3 main.py +``` + +## 调用接口 + +``` python +import httpx + +def geetest(gt, challenge): +    data = httpx.post( +        "http://127.0.0.1:10721", +        json={"gt": gt, "challenge": challenge}, +        timeout=30, +    ).json() +    if data["status"] == "success": +        return data["validate"] +    return None +``` + +成功率接近100%（1000次测试全部成功） + +# 实现思路 + +## 九宫格 +* 采用均方误差（MSE）法对小图标进行分类 +* 使用resnet18对图片进行分类 + +## 图标点选 +* 使用ddddocr进行目标检测 +* 使用孪生神经网络计算相似度 + +# 协议 +本项目遵循 AGPL-3.0 协议开源，请遵守相关协议。 + +# 鸣谢 +[ravizhan/geetest-v3-click-crack](https://github.com/ravizhan/geetest-v3-click-crack) 提供极验接口逆向 + +[taisuii/ClassificationCaptchaOcr](https://github.com/taisuii/ClassificationCaptchaOcr) 提供九宫格识别思路及模型 + +[bubbliiiing/Siamese-pytorch](https://github.com/bubbliiiing/Siamese-pytorch) 提供孪生网络模型 + +[sml2h3/ddddocr](https://github.com/sml2h3/ddddocr) 提供目标检测SDK \ No newline at end of file diff --git a/crack.py b/crack.py new file mode 100644 index 0000000..3f33aa4 --- /dev/null +++ b/crack.py @@ -0,0 +1,533 @@ +import hashlib +import json +import math +import random +import time + +import httpx +from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes +from cryptography.hazmat.primitives import padding, serialization +from cryptography.hazmat.primitives.asymmetric.padding import PKCS1v15 + + +class Crack: + def __init__(self, gt=None, challenge=None): + self.pic_path = None + self.s = None + self.c = None + self.session = httpx.Client(http2=True) + self.session.headers = { + "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36" + } + # self.session.verify = False + self.gt = gt + self.challenge = challenge + self.aeskey = "".join( + f"{int((1 + random.random()) * 65536):04x}"[1:] for _ in range(4) + ) + public_key = """-----BEGIN PUBLIC KEY----- +MIGfMA0GCSqGSIb3DQEBAQUAA4GNADCBiQKBgQDB45NNFhRGWzMFPn9I7k7IexS5 +XviJR3E9Je7L/350x5d9AtwdlFH3ndXRwQwprLaptNb7fQoCebZxnhdyVl8Jr2J3 +FZGSIa75GJnK4IwNaG10iyCjYDviMYymvCtZcGWSqSGdC/Bcn2UCOiHSMwgHJSrg +Bm1Zzu+l8nSOqAurgQIDAQAB +-----END PUBLIC KEY-----""" + self.public_key = serialization.load_pem_public_key(public_key.encode()) + self.enc_key = self.public_key.encrypt(self.aeskey.encode(), PKCS1v15()).hex() + with open("mousepath.json", "r") as f: + self.mouse_path = json.loads(f.read()) + + def get_type(self) -> dict: + url = f"https://api.geetest.com/gettype.php?gt={self.gt}" + res = self.session.get(url) + return json.loads(res.text[1:-1])["data"] + + @staticmethod + def encode(input_bytes: list): + def get_char_from_index(index): + char_table = ( + "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789()" + ) + return char_table[index] if 0 <= index < len(char_table) else "." + + def transform_value(value, bit_mask): + result = 0 + for r in range(23, -1, -1): + if (bit_mask >> r) & 1: + result = (result << 1) + ((value >> r) & 1) + return result + + encoded_string = "" + padding = "" + input_length = len(input_bytes) + for i in range(0, input_length, 3): + chunk_length = min(3, input_length - i) + chunk = input_bytes[i : i + chunk_length] + if chunk_length == 3: + value = (chunk[0] << 16) + (chunk[1] << 8) + chunk[2] + encoded_string += ( + get_char_from_index(transform_value(value, 7274496)) + + get_char_from_index(transform_value(value, 9483264)) + + get_char_from_index(transform_value(value, 19220)) + + get_char_from_index(transform_value(value, 235)) + ) + elif chunk_length == 2: + value = (chunk[0] << 16) + (chunk[1] << 8) + encoded_string += ( + get_char_from_index(transform_value(value, 7274496)) + + get_char_from_index(transform_value(value, 9483264)) + + get_char_from_index(transform_value(value, 19220)) + ) + padding = "." + elif chunk_length == 1: + value = chunk[0] << 16 + encoded_string += get_char_from_index( + transform_value(value, 7274496) + ) + get_char_from_index(transform_value(value, 9483264)) + padding = ".." + return encoded_string + padding + + @staticmethod + def MD5(text: str): + return hashlib.md5(text.encode()).hexdigest() + + @staticmethod + def encode_mouse_path(path: list, c: list, s: str): + def preprocess(path: list): + def BFIQ(e): + t = 32767 + if not isinstance(e, int): + return e + else: + if t < e: + e = t + elif e < -t: + e = -t + return round(e) + + def BGAB(e): + t = "" + n = 0 + len(e or []) + while n < len(e) and not t: + if e[n]: + t = e[n][4] + n += 1 + if not t: + return e + r = "" + i = ["mouse", "touch", "pointer", "MSPointer"] + for s in range(len(i)): + if t.startswith(i[s]): + r = i[s] + _ = list(e) + for a in range(len(_) - 1, -1, -1): + c = _[a] + l = c[0] + if l in ["move", "down", "up"]: + value = c[4] or "" + if not value.startswith(r): + _.pop(a) + return _ + + t = 0 + n = 0 + r = [] + s = 0 + if len(path) <= 0: + return [] + o = None + _ = None + a = BGAB(path) + c = len(a) + for l in range(0 if c < 300 else c - 300, c): + u = a[l] + h = u[0] + if h in ["down", "move", "up", "scroll"]: + if not o: + o = u + _ = u + r.append([h, [u[1] - t, u[2] - n], BFIQ(u[3] - s if s else s)]) + t = u[1] + n = u[2] + s = u[3] + elif h in ["blur", "focus", "unload"]: + r.append([h, BFIQ(u[1] - s if s else s)]) + s = u[1] + return r + + def process(prepared_path: list): + h = { + "move": 0, + "down": 1, + "up": 2, + "scroll": 3, + "focus": 4, + "blur": 5, + "unload": 6, + "unknown": 7, + } + + def p(e, t): + n = bin(e)[2:] + r = "" + i = len(n) + 1 + while i <= t: + i += 1 + r += "0" + return r + n + + def d(e): + t = [] + n = len(e) + r = 0 + while r < n: + i = e[r] + s = 0 + while True: + if s >= 16: + break + o = r + s + 1 + if o >= n: + break + if e[o] != i: + break + s += 1 + r += 1 + s + _ = h[i] + if s != 0: + t.append(_ | 8) + t.append(s - 1) + else: + t.append(_) + a = p(n | 32768, 16) + c = "" + for l in range(len(t)): + c += p(t[l], 4) + return a + c + + def g(e, tt): + def temp1(e1): + n = len(e) + r = 0 + i = [] + while r < n: + s = 1 + o = e[r] + _ = abs(o) + while True: + if n <= r + s: + break + if e[r + s] != o: + break + if (_ >= 127) or (s >= 127): + break + s += 1 + if s > 1: + i.append((49152 if o < 0 else 32768) | s << 7 | _) + else: + i.append(o) + r += s + return i + + e = temp1(e) + + r = [] + i = [] + + def n(e, t): + return 0 if e == 0 else math.log(e) / math.log(t) + + for temp in e: + t = math.ceil(n(abs(temp) + 1, 16)) + if t == 0: + t = 1 + r.append(p(t - 1, 2)) + i.append(p(abs(temp), t * 4)) + + s = "".join(r) + o = "".join(i) + + def temp2(t): + return t != 0 and t >> 15 != 1 + + def temp3(e1): + n = [] + + def temp(e2): + if temp2(e2): + n.append(e2) + + for r in range(len(e1)): + temp(e1[r]) + return n + + def temp4(t): + if t < 0: + return "1" + else: + return "0" + + if tt: + n = [] + e1 = temp3(e) + for r in range(len(e1)): + n.append(temp4(e1[r])) + n = "".join(n) + else: + n = "" + return p(len(e) | 32768, 16) + s + o + n + + def u(e): + t = "" + n = len(e) // 6 + for r in range(n): + t += "()*,-./0123456789:?@ABCDEFGHIJKLMNOPQRSTUVWXYZ_abcdefghijklmnopqrstuvwxyz~"[ + int(e[6 * r : 6 * (r + 1)], 2) + ] + return t + + t = [] + n = [] + r = [] + i = [] + for a in range(len(prepared_path)): + _ = prepared_path[a] + a = len(_) + t.append(_[0]) + n.append(_[1] if a == 2 else _[2]) + if a == 3: + r.append(_[1][0]) + i.append(_[1][1]) + c = d(t) + g(n, False) + g(r, True) + g(i, True) + l = len(c) + if l % 6 != 0: + c += p(0, 6 - l % 6) + return u(c) + + def postprocess(e, t, n): + i = 0 + s = e + o = t[0] + _ = t[2] + a = t[4] + while True: + r = n[i : i + 2] + if not r: + break + i += 2 + c = int(r, 16) + l = chr(c) + u = (o * c * c + _ * c + a) % len(e) + s = s[:u] + l + s[u:] + return s + + return postprocess(process(preprocess(path)), c, s) + + def aes_encrypt(self, content: str): + cipher = Cipher( + algorithms.AES(self.aeskey.encode()), modes.CBC(b"0000000000000000") + ) + encryptor = cipher.encryptor() + padder = padding.PKCS7(128).padder() + padded_data = padder.update(content.encode()) + padded_data += padder.finalize() + ct = encryptor.update(padded_data) + encryptor.finalize() + return ct + + def get_c_s(self): + o = { + "gt": self.gt, + "challenge": self.challenge, + "offline": False, + "new_captcha": True, + "product": "embed", + "width": "300px", + "https": True, + "protocol": "https://", + } + o.update(self.get_type()) + o.update( + { + "cc": 16, + "ww": True, + "i": "-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1!!-1", + } + ) + o = json.dumps(o, separators=(",", ":")) + # print(o) + ct = self.aes_encrypt(o) + s = [] + for byte in ct: + s.append(byte) + i = self.encode(s) + r = self.enc_key + w = i + r + params = { + "gt": self.gt, + "challenge": self.challenge, + "lang": "zh-cn", + "pt": 0, + "client_type": "web", + "callback": "geetest_" + str(int(round(time.time() * 1000))), + "w": w, + } + resp = self.session.get("https://api.geetest.com/get.php", params=params).text + # print(resp) + data = json.loads(resp[22:-1])["data"] + self.c = data["c"] + self.s = data["s"] + return data["c"], data["s"] + + def gettype(self): + url = f"https://api.geetest.com/gettype.php?gt={self.gt}&callback=geetest_{str(int(round(time.time() * 1000)))}" + return self.session.get(url).text + + def ajax(self): + def transform(e, t, n): + if not t or not n: + return e + o = 0 + i = list(e) + s = t[0] + a = t[2] + b = t[4] + while o < len(n): + r = n[o : o + 2] + o += 2 + c = int(r, 16) + l = chr(c) + u = (s * c * c + a * c + b) % len(i) + i.insert(u, l) + return "".join(i) + + mouse_path = [ + ["move", 385, 313, 1724572150164, "pointermove"], + ["move", 385, 315, 1724572150166, "pointermove"], + ["move", 386, 315, 1724572150174, "pointermove"], + ["move", 387, 315, 1724572150182, "pointermove"], + ["move", 387, 316, 1724572150188, "pointermove"], + ["move", 388, 316, 1724572150204, "pointermove"], + ["move", 388, 317, 1724572150218, "pointermove"], + ["down", 388, 317, 1724572150586, "pointerdown"], + ["focus", 1724572150587], + ["up", 388, 317, 1724572150632, "pointerup"], + ] + tt = transform( + self.encode_mouse_path(mouse_path, self.c, self.s), self.c, self.s + ) + rp = self.MD5(self.gt + self.challenge + self.s) + temp1 = ( + """"lang":"zh-cn","type":"fullpage","tt":"%s","light":"DIV_0","s":"c7c3e21112fe4f741921cb3e4ff9f7cb","h":"321f9af1e098233dbd03f250fd2b5e21","hh":"39bd9cad9e425c3a8f51610fd506e3b3","hi":"09eb21b3ae9542a9bc1e8b63b3d9a467","vip_order":-1,"ct":-1,"ep":{"v":"9.1.9-dbjg5z","te":false,"me":true,"ven":"Google Inc. (Intel)","ren":"ANGLE (Intel, Intel(R) Iris(R) Xe Graphics (0x0000A7A0) Direct3D11 vs_5_0 ps_5_0, D3D11)","fp":["scroll",0,1602,1724571628498,null],"lp":["up",386,217,1724571629854,"pointerup"],"em":{"ph":0,"cp":0,"ek":"11","wd":1,"nt":0,"si":0,"sc":0},"tm":{"a":1724571567311,"b":1724571567549,"c":1724571567562,"d":0,"e":0,"f":1724571567312,"g":1724571567312,"h":1724571567312,"i":1724571567317,"j":1724571567423,"k":1724571567330,"l":1724571567423,"m":1724571567545,"n":1724571567547,"o":1724571567569,"p":1724571568259,"q":1724571568259,"r":1724571568261,"s":1724571570378,"t":1724571570378,"u":1724571570380},"dnf":"dnf","by":0},"passtime":1600,"rp":"%s",""" + % (tt, rp) + ) + r = "{" + temp1 + '"captcha_token":"1198034057","du6o":"eyjf7nne"}' + # print(r) + ct = self.aes_encrypt(r) + s = [byte for byte in ct] + w = self.encode(s) + params = { + "gt": self.gt, + "challenge": self.challenge, + "lang": "zh-cn", + "pt": 0, + "client_type": "web", + "callback": "geetest_" + str(int(round(time.time() * 1000))), + "w": w, + } + resp = self.session.get("https://api.geetest.com/ajax.php", params=params).text + return json.loads(resp[22:-1])["data"] + + def get_pic(self, retry): + params = { + "type": "click", + "gt": self.gt, + "challenge": self.challenge, + "lang": "zh-cn", + "callback": "geetest_" + str(int(round(time.time() * 1000))), + } + if retry == 0: + url = "https://api.geevisit.com/get.php" + params.update( + { + "is_next": "true", + "https": "true", + "protocol": "https://", + "offline": "false", + "product": "float", + "api_server": "api.geevisit.com", + "isPC": True, + "autoReset": True, + "width": "100%", + } + ) + else: + url = "https://api.geetest.com/refresh.php" + resp = self.session.get(url, params=params).text + data = json.loads(resp[22:-1])["data"] + self.pic_path = data["pic"] + pic_url = "https://" + data["image_servers"][0][:-1] + data["pic"] + return data["pic_type"], self.session.get(pic_url).content + + def verify(self, points: list): + u = self.enc_key + o = { + "lang": "zh-cn", + "passtime": 1600, + "a": ",".join(points), + "pic": self.pic_path, + "tt": self.encode_mouse_path(self.mouse_path, self.c, self.s), + "ep": { + "ca": [ + {"x": 524, "y": 209, "t": 0, "dt": 1819}, + {"x": 558, "y": 299, "t": 0, "dt": 428}, + {"x": 563, "y": 95, "t": 0, "dt": 952}, + {"x": 670, "y": 407, "t": 3, "dt": 892}, + ], + "v": "3.1.0", + "$_FB": False, + "me": True, + "tm": { + "a": 1724585496403, + "b": 1724585496605, + "c": 1724585496613, + "d": 0, + "e": 0, + "f": 1724585496404, + "g": 1724585496404, + "h": 1724585496404, + "i": 1724585496404, + "j": 1724585496404, + "k": 0, + "l": 1724585496413, + "m": 1724585496601, + "n": 1724585496603, + "o": 1724585496618, + "p": 1724585496749, + "q": 1724585496749, + "r": 1724585496751, + "s": 1724585498068, + "t": 1724585498068, + "u": 1724585498069, + }, + }, + "h9s9": "1816378497", + } + o["rp"] = self.MD5(self.gt + self.challenge + str(o["passtime"])) + o = json.dumps(o, separators=(",", ":")) + # print(o) + ct = self.aes_encrypt(o) + s = [] + for byte in ct: + s.append(byte) + p = self.encode(s) + w = p + u + params = { + "gt": self.gt, + "challenge": self.challenge, + "lang": "zh-cn", + "pt": 0, + "client_type": "web", + "w": w, + } + resp = self.session.get("https://api.geevisit.com/ajax.php", params=params).text + return resp[1:-1] diff --git a/main.py b/main.py new file mode 100644 index 0000000..cb778d3 --- /dev/null +++ b/main.py @@ -0,0 +1,82 @@ +import json +import os +import time +from crack import Crack +import predict + +from flask import Flask, request, jsonify + +app = Flask(__name__) + + +stats_file = "stats.json" + +if not os.path.exists(stats_file): + with open(stats_file, "w") as f: + json.dump({"total": 0, "success": 0, "failure": 0, "error": 0}, f) + + +def read_stats(): + with open(stats_file, "r") as f: + return json.load(f) + + +def update_stats(msg): + stats = read_stats() + stats[msg] += 1 + stats["total"] += 1 + with open(stats_file, "w") as f: + json.dump(stats, f) + + +@app.route("/statistic", methods=["get"]) +def statistic(): + stats = read_stats() + return f"调用{stats['total']}次，成功{stats['success']}次，失败{stats['failure']}次，错误{stats['error']}次" + + +@app.route("/", methods=["POST"]) +def handle_post(): + data = request.get_json() + if "gt" in data and "challenge" in data: + crack = Crack(data["gt"], data["challenge"]) + crack.get_type() + crack.get_c_s() + crack.ajax() + for retry in range(6): + a = [] + type, image = crack.get_pic(retry) + t = time.time() + if type == "nine": + points = predict.nine(image) + for x, y in points: + a.append(f"{x}_{y}") + elif type == "icon": + points = predict.icon(image) + for x, y in points: + left = round(x / 333 * 10000) + top = round(y / 333 * 10000) + a.append(f"{left}_{top}") + else: + update_stats("error") + return jsonify({"status": "error", "msg": "unsupported type"}) + wait_time = t + 2 - time.time() + if wait_time > 0: + time.sleep(wait_time) + res = eval(crack.verify(a)) + if res["data"]["result"] == "success": + update_stats("success") + return jsonify( + {"status": "success", "validate": res["data"]["validate"]} + ) + update_stats("failure") + return jsonify({"status": "failed"}) + else: + return ( + jsonify({"status": "error", "message": "Missing 'gt' or 'challenge'"}), + 400, + ) + + +if __name__ == "__main__": + app.run(host="0.0.0.0", port=10721) diff --git a/mousepath.json b/mousepath.json new file mode 100644 index 0000000..3e6aadb --- /dev/null +++ b/mousepath.json @@ -0,0 +1,336 @@ +[ + ["move", 170, 514, 1721639139496, "pointermove"], + ["move", 171, 514, 1721639139518, "pointermove"], + ["move", 172, 514, 1721639139564, "pointermove"], + ["move", 174, 514, 1721639139578, "pointermove"], + ["move", 174, 514, 1721639139586, "pointermove"], + ["move", 175, 514, 1721639139646, "pointermove"], + ["move", 178, 514, 1721639139651, "pointermove"], + ["move", 177, 513, 1721639139652, "mousemove"], + ["move", 179, 514, 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file diff --git a/predict/__init__.py b/predict/__init__.py new file mode 100644 index 0000000..fe6e45b --- /dev/null +++ b/predict/__init__.py @@ -0,0 +1,8 @@ +import sys +import os + +current_dir = os.path.dirname(__file__) +sys.path.append(current_dir) + +from nine.nine import nine +from icon.icon import icon diff --git a/predict/icon/__init__.py b/predict/icon/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/predict/icon/icon.py b/predict/icon/icon.py new file mode 100644 index 0000000..30ce41d --- /dev/null +++ b/predict/icon/icon.py @@ -0,0 +1,73 @@ +import os +import ddddocr +from PIL import Image +from io import BytesIO +import numpy as np +import onnxruntime as ort + +det = ddddocr.DdddOcr(det=True, ocr=False) +current_dir = os.path.dirname(os.path.abspath(__file__)) +model_path = os.path.join(current_dir, "siamese.onnx") +Siamese = ort.InferenceSession(model_path) + + +def get_icons(image_bytes): + def remove_subicon(data): + result = [] + for i in data: + 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 = 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(img1, img2): + 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 = Siamese.run(None, inputs) + + output_sigmoid = 1 / (1 + np.exp(-output[0])) + similarity_score = output_sigmoid[0][0] + + return similarity_score + + +def icon(image_bytes): + big_bboxes, small_images, big_images = get_icons(image_bytes) + ans = [] + for i in small_images: + similarities = [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 diff --git a/predict/nine/__init__.py b/predict/nine/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/predict/nine/icon/0.jpg b/predict/nine/icon/0.jpg new file mode 100644 index 0000000000000000000000000000000000000000..e755b0e52092f61f3c13c95f9b55f6adccac6937 GIT binary patch literal 1116 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+session = ort.InferenceSession(model_path) +input_name = session.get_inputs()[0].name + + +def read_icon(): + folder_path = os.path.join(current_dir, "icon") + icon_list = [] + for i in range(91): + file_path = os.path.join(folder_path, f"{i}.jpg") + icon = Image.open(file_path) + icon_list.append(np.array(icon)) + return icon_list + + +icon_list = read_icon() + + +def get_target_id(target_image): + target_array = np.array(target_image) + for i, icon_array in enumerate(icon_list): + err = np.sum((target_array.astype("float") - icon_array.astype("float")) ** 2) + err /= float(target_array.shape[0] * target_array.shape[1]) + if err < 1000: + return i + + +def crop_image(image_bytes, coordinates): + cropped_images = [] + img = Image.open(BytesIO(image_bytes)) + width, height = img.size + + left = 0 + upper = width + right = height - width + lower = height + box = (left, upper, right, lower) + icon_img = img.crop(box) + + grid_edge_length = width // 3 + for coord in coordinates: + x, y = coord + left = (x - 1) * grid_edge_length + upper = (y - 1) * grid_edge_length + right = left + grid_edge_length + lower = upper + grid_edge_length + box = (left, upper, right, lower) + cropped_img = img.crop(box) + cropped_images.append(cropped_img) + return icon_img, cropped_images + + +def nine(image_bytes): + coordinates = [ + (1, 1), + (1, 2), + (1, 3), + (2, 1), + (2, 2), + (2, 3), + (3, 1), + (3, 2), + (3, 3), + ] + + def data_transforms(image): + image = image.resize((224, 224)) + image_array = np.array(image) + image_array = image_array.astype(np.float32) / 255.0 + mean = np.array([0.485, 0.456, 0.406], dtype=np.float32) + std = np.array([0.229, 0.224, 0.225], dtype=np.float32) + image_array = (image_array - mean) / std + image_array = np.transpose(image_array, (2, 0, 1)) + # image_array = np.expand_dims(image_array, axis=0) + return image_array + + icon_image, cropped_image = crop_image(image_bytes, coordinates) + target_id = get_target_id(icon_image) + target_images = [data_transforms(i) for i in cropped_image] + outputs = session.run(None, {input_name: target_images})[0] + class_ids = np.argmax(outputs, axis=1).tolist() + + ans = [] + for i, id in enumerate(class_ids): + if id == target_id: + ans.append(coordinates[i]) + return ans diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..3bbe206 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,7 @@ +httpx[http2] +flask +ddddocr +numpy +Pillow +onnxruntime +cryptography diff --git a/train.md b/train.md new file mode 100644 index 0000000..c8e8234 --- /dev/null +++ b/train.md @@ -0,0 +1,28 @@ +# 训练模型 +## resnet18 +参见 [taisuii/ClassificationCaptchaOcr](https://github.com/taisuii/ClassificationCaptchaOcr) +## siamese +参见 [bubbliiiing/Siamese-pytorch](https://github.com/bubbliiiing/Siamese-pytorch) +* 每个分类图片数不得低于三张 +* 使用以下代码导出 +``` python +import torch +from nets.siamese import Siamese + +weights = "logs/last_epoch_weights.pth" # 选择合适的权重 +onnx_outpath = "siamese.onnx" + +model = Siamese([105, 105]) +model.load_state_dict(torch.load(weights)) +input_image = [torch.randn(1, 3, 105, 105), torch.randn(1, 3, 105, 105)] +torch.onnx.export( + model, + input_image, + onnx_outpath, + opset_version=13, + verbose=True, + do_constant_folding=True, + input_names=["input", "input.53"], + output_names=["output"], +) +``` \ No newline at end of file