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新模型及fullpage.9.2.0-guwyxh.js适配
Fixes #5 添加dinov3、任务头及yolo11n用于通过二者的验证码 同时更新fullpage.9.2.0-guwyxh.js中新的常量 ``` json { captcha_token":"2064329542", "tsfq":"xovrayel" } ```
This commit is contained in:
luguoyixiazi
GitHub
469
predict.py
469
predict.py
@@ -1,12 +1,40 @@
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import os
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import numpy as np
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from crop_image import crop_image, convert_png_to_jpg,draw_points_on_image,bytes_to_pil,validate_path
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import time
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from PIL import Image, ImageDraw
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import base64
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import random
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import logging
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import numpy as np
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from io import BytesIO
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import onnxruntime as ort
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from PIL import Image, ImageDraw
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from crop_image import crop_image, convert_png_to_jpg,draw_points_on_image,bytes_to_pil,validate_path,save_path
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logger = logging.getLogger(__name__)
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def safe_load_img(image):
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im_pil = None
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try:
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if isinstance(image, Image.Image):
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im_pil = image
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elif isinstance(image, str):
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try:
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im_pil = Image.open(image)
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except (IOError, FileNotFoundError):
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if ',' in image:
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image = image.split(',')[-1]
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padding = len(image) % 4
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if padding > 0:
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image += '=' * (4 - padding)
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img_bytes = base64.b64decode(image)
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im_pil = Image.open(io.BytesIO(img_bytes))
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elif isinstance(image, bytes):
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im_pil = bytes_to_pil(image)
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elif isinstance(image, np.ndarray):
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im_pil = Image.fromarray(image)
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else:
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raise ValueError(f"不支持的输入类型: {type(image)}")
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return im_pil.convert("RGB")
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except Exception as e:
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raise ValueError(f"无法加载或解析图像，错误: {e}")
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def predict(icon_image, bg_image):
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import torch
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from train import MyResNet18, data_transform
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@@ -34,7 +62,7 @@ def predict(icon_image, bg_image):
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model = MyResNet18(num_classes=91) # 这里的类别数要与训练时一致
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model.load_state_dict(torch.load(model_path))
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model.eval()
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print("加载模型，耗时:", time.time() - start)
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logger.info("加载模型，耗时:", time.time() - start)
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start = time.time()
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target_images = torch.stack(target_images, dim=0)
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@@ -52,14 +80,14 @@ def predict(icon_image, bg_image):
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)
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scores.append(similarity.cpu().item())
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# 从左到右，从上到下，依次为每张图片的置信度
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print(scores)
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logger.info(scores)
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# 对数组进行排序，保持下标
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indexed_arr = list(enumerate(scores))
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sorted_arr = sorted(indexed_arr, key=lambda x: x[1], reverse=True)
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# 提取最大三个数及其下标
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largest_three = sorted_arr[:3]
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print(largest_three)
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print("识别完成，耗时:", time.time() - start)
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logger.info(largest_three)
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logger.info("识别完成，耗时:", time.time() - start)
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def load_model(name='PP-HGNetV2-B4.onnx'):
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# 加载onnx模型
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@@ -69,7 +97,7 @@ def load_model(name='PP-HGNetV2-B4.onnx'):
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model_path = os.path.join(current_dir, 'model', name)
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session = ort.InferenceSession(model_path)
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input_name = session.get_inputs()[0].name
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print(f"加载{name}模型，耗时:{time.time() - start}")
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logger.info(f"加载{name}模型，耗时:{time.time() - start}")
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def load_dfine_model(name='d-fine-n.onnx'):
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# 加载onnx模型
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@@ -78,8 +106,31 @@ def load_dfine_model(name='d-fine-n.onnx'):
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current_dir = os.path.dirname(os.path.abspath(__file__))
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model_path = os.path.join(current_dir, 'model', name)
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session_dfine = ort.InferenceSession(model_path)
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print(f"加载{name}模型，耗时:{time.time() - start}")
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logger.info(f"加载{name}模型，耗时:{time.time() - start}")
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def load_yolo11n(name='yolo11n.onnx'):
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global session_yolo11n
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start = time.time()
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current_dir = os.path.dirname(os.path.abspath(__file__))
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model_path = os.path.join(current_dir, 'model', name)
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session_yolo11n = ort.InferenceSession(model_path)
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logger.info(f"加载{name}模型，耗时:{time.time() - start}")
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def load_dinov3(name='dinov3-small.onnx'):
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global session_dino3
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start = time.time()
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current_dir = os.path.dirname(os.path.abspath(__file__))
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model_path = os.path.join(current_dir, 'model', name)
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session_dino3 = ort.InferenceSession(model_path)
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logger.info(f"加载{name}模型，耗时:{time.time() - start}")
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def load_dino_classify(name='atten.onnx'):
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global session_dino_cf
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start = time.time()
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current_dir = os.path.dirname(os.path.abspath(__file__))
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model_path = os.path.join(current_dir, 'model', name)
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session_dino_cf = ort.InferenceSession(model_path)
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logger.info(f"加载{name}模型，耗时:{time.time() - start}")
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def predict_onnx(icon_image, bg_image, point = None):
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import cv2
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@@ -137,8 +188,7 @@ def predict_onnx(icon_image, bg_image, point = None):
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else:
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similarity = cosine_similarity(target_output, out_put)
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scores.append(similarity)
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# 从左到右，从上到下，依次为每张图片的置信度
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# print(scores)
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logger.debug(f"从左到右，从上到下，依次为每张图片的置信度:\n{scores}")
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# 对数组进行排序，保持下标
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indexed_arr = list(enumerate(scores))
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sorted_arr = sorted(indexed_arr, key=lambda x: x[1], reverse=True)
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@@ -149,7 +199,7 @@ def predict_onnx(icon_image, bg_image, point = None):
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# 基于分数判断
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else:
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answer = [one[0] for one in sorted_arr if one[1] > point]
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print(f"识别完成{answer}，耗时: {time.time() - start}")
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logger.info(f"识别完成{answer}，耗时: {time.time() - start}")
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#draw_points_on_image(bg_image, answer)
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return answer
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@@ -202,40 +252,79 @@ def predict_onnx_pdl(images_path):
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if len(answer) == 0:
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all_sort =[np.argsort(one) for one in outputs]
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answer = [coordinates[index] for index in range(9) if all_sort[index][1] == target]
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print(f"识别完成{answer}，耗时: {time.time() - start}")
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logger.info(f"识别完成{answer}，耗时: {time.time() - start}")
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with open(os.path.join(images_path,"nine.jpg"),'rb') as f:
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bg_image = f.read()
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# draw_points_on_image(bg_image, answer)
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#draw_points_on_image(bg_image, answer)
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return answer
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# d-fine的推理代码及函数
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def calculate_iou(boxA, boxB):
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"""
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使用 NumPy 计算两个边界框的交并比 (IoU)。
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"""
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# 确定相交矩形的坐标
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xA = np.maximum(boxA[0], boxB[0])
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yA = np.maximum(boxA[1], boxB[1])
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xB = np.minimum(boxA[2], boxB[2])
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yB = np.minimum(boxA[3], boxB[3])
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# 计算相交区域的面积
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intersection_area = np.maximum(0, xB - xA) * np.maximum(0, yB - yA)
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# 计算两个边界框的面积
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boxA_area = (boxA[2] - boxA[0]) * (boxA[3] - boxA[1])
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boxB_area = (boxB[2] - boxB[0]) * (boxB[3] - boxB[1])
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# 计算并集面积
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union_area = float(boxA_area + boxB_area - intersection_area)
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# 计算 IoU
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if union_area == 0:
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return 0.0
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return 0.0
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iou = intersection_area / union_area
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return iou
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def non_maximum_suppression(detections, iou_threshold=0.35):
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"""
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对检测结果执行非极大值抑制 (NMS)。
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参数:
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detections -- 一个列表，其中每个元素是包含 'box', 'score' 的字典。
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例如: [{'box': [x1, y1, x2, y2], 'score': 0.9, ...}, ...]
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iou_threshold -- 一个浮点数，用于判断框是否重叠的 IoU 阈值。
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返回:
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final_detections -- 经过 NMS 处理后保留下来的检测结果列表。
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"""
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# 1. 检查检测结果是否为空
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if not detections:
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return []
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# 2. 按置信度（score）从高到低对边界框进行排序
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# 我们使用 lambda 函数来指定排序的键
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detections.sort(key=lambda x: x['score'], reverse=True)
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final_detections = []
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# 3. 循环处理，直到没有检测结果为止
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while detections:
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# 4. 将当前得分最高的检测结果（第一个）添加到最终列表中
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# 并将其从原始列表中移除
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best_detection = detections.pop(0)
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final_detections.append(best_detection)
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# 5. 计算刚刚取出的最佳框与剩余所有框的 IoU
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# 并只保留那些 IoU 小于阈值的框
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detections_to_keep = []
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for det in detections:
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# 假设相同类别的才进行NMS
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iou = calculate_iou(best_detection['box'], det['box'])
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if iou < iou_threshold:
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detections_to_keep.append(det)
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# 用筛选后的列表替换原始列表，进行下一轮迭代
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detections = detections_to_keep
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return final_detections
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@@ -246,10 +335,7 @@ def predict_onnx_dfine(image,draw_result=False):
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image_input_name = input_nodes[0].name
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size_input_name = input_nodes[1].name
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output_names = [node.name for node in output_nodes]
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if isinstance(image,bytes):
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im_pil = bytes_to_pil(image)
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else:
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im_pil = Image.open(image).convert("RGB")
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im_pil = safe_load_img(image)
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w, h = im_pil.size
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orig_size_np = np.array([[w, h]], dtype=np.int64)
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im_resized = im_pil.resize((320, 320), Image.Resampling.BILINEAR)
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@@ -340,18 +426,335 @@ def predict_onnx_dfine(image,draw_result=False):
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text_position = (box[0] + 2, box[1] - 12 if box[1] > 12 else box[1] + 2)
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draw.text(text_position, text, fill=color)
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if draw_result:
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save_path = os.path.join(validate_path,"icon_result.jpg")
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im_pil.save(save_path)
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print(f"图片可视化结果保存在{save_path}")
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print(f"图片顺序的中心点{points}")
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save_path_temp = os.path.join(validate_path,"icon_result.jpg")
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im_pil.save(save_path_temp)
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logger.info(f"图片可视化结果暂时保存在{save_path_temp},运行完成后移至{save_path}")
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logger.info(f"图片顺序的中心点{points}")
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return points
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print(f"使用推理设备: {ort.get_device()}")
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# yolo的推理代码及函数
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def predict_onnx_yolo(image):
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def filter_Detections(results, thresh = 0.5):
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results = results[0]
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results = results.transpose()
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# if model is trained on 1 class only
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if len(results[0]) == 5:
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# filter out the detections with confidence > thresh
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considerable_detections = [detection for detection in results if detection[4] > thresh]
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considerable_detections = np.array(considerable_detections)
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return considerable_detections
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# if model is trained on multiple classes
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else:
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A = []
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for detection in results:
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class_id = detection[4:].argmax()
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confidence_score = detection[4:].max()
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new_detection = np.append(detection[:4],[class_id,confidence_score])
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A.append(new_detection)
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A = np.array(A)
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# filter out the detections with confidence > thresh
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considerable_detections = [detection for detection in A if detection[-1] > thresh]
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considerable_detections = np.array(considerable_detections)
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return considerable_detections
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def NMS(boxes, conf_scores, iou_thresh = 0.55):
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# boxes [[x1,y1, x2,y2], [x1,y1, x2,y2], ...]
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x1 = boxes[:,0]
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y1 = boxes[:,1]
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x2 = boxes[:,2]
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y2 = boxes[:,3]
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areas = (x2-x1)*(y2-y1)
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order = conf_scores.argsort()
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keep = []
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keep_confidences = []
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while len(order) > 0:
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idx = order[-1]
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A = boxes[idx]
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conf = conf_scores[idx]
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order = order[:-1]
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xx1 = np.take(x1, indices= order)
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yy1 = np.take(y1, indices= order)
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xx2 = np.take(x2, indices= order)
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yy2 = np.take(y2, indices= order)
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keep.append(A)
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keep_confidences.append(conf)
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# iou = inter/union
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xx1 = np.maximum(x1[idx], xx1)
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yy1 = np.maximum(y1[idx], yy1)
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xx2 = np.minimum(x2[idx], xx2)
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yy2 = np.minimum(y2[idx], yy2)
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w = np.maximum(xx2-xx1, 0)
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h = np.maximum(yy2-yy1, 0)
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intersection = w*h
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# union = areaA + other_areas - intesection
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other_areas = np.take(areas, indices= order)
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union = areas[idx] + other_areas - intersection
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iou = intersection/union
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boleans = iou < iou_thresh
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order = order[boleans]
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# order = [2,0,1] boleans = [True, False, True]
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# order = [2,1]
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return keep, keep_confidences
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def rescale_back(results,img_w,img_h,imgsz=384):
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cx, cy, w, h, class_id, confidence = results[:,0], results[:,1], results[:,2], results[:,3], results[:, 4], results[:,-1]
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cx = cx/imgsz * img_w
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cy = cy/imgsz * img_h
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w = w/imgsz * img_w
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h = h/imgsz * img_h
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x1 = cx - w/2
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y1 = cy - h/2
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x2 = cx + w/2
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y2 = cy + h/2
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boxes = np.column_stack((x1, y1, x2, y2, class_id))
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keep, keep_confidences = NMS(boxes,confidence)
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return keep, keep_confidences
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im_pil = safe_load_img(image)
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im_resized = im_pil.resize((384, 384), Image.Resampling.BILINEAR)
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im_data = np.array(im_resized, dtype=np.float32) / 255.0
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im_data = im_data.transpose(2, 0, 1)
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im_data = np.expand_dims(im_data, axis=0)
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res = session_yolo11n.run(None,{"images":im_data})
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results = filter_Detections(res)
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rescaled_results, confidences = rescale_back(results,im_pil.size[0],im_pil.size[1])
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images = {"top":[],"bottom":[]}
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for r, conf in zip(rescaled_results, confidences):
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x1,y1,x2,y2, cls_id = r
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cls_id = int(cls_id)
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x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
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cropped_image = im_pil.crop((x1, y1, x2, y2))
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if cls_id == 0:
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images['top'].append({"image":cropped_image,"bbox":[x1, y1, x2, y2]})
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else:
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images['bottom'].append({"image":cropped_image,"bbox":[x1, y1, x2, y2]})
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return images
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# dinov3的推理代码及函数
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def make_lvd_transform(resize_size: int = 224):
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"""
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返回一个图像预处理函数，功能与PyTorch版本相同
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"""
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# 定义标准化参数
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mean = np.array([0.485, 0.456, 0.406], dtype=np.float32)
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std = np.array([0.229, 0.224, 0.225], dtype=np.float32)
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def transform(image) -> np.ndarray:
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"""
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图像预处理转换
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Args:
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image: PIL Image 或 numpy array (H,W,C) 范围[0,255]
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Returns:
|
||||
numpy array (C,H,W) 标准化后的float32数组
|
||||
"""
|
||||
# 确保输入是PIL图像
|
||||
if isinstance(image, np.ndarray):
|
||||
image = Image.fromarray(image.astype('uint8'))
|
||||
|
||||
# 1. 调整大小 (使用LANCZOS抗锯齿，对应antialias=True)
|
||||
image = image.resize((resize_size, resize_size), Image.LANCZOS)
|
||||
|
||||
# 2. 转换为numpy数组并调整数据类型和范围
|
||||
# PIL图像转换为numpy数组 (H,W,C) 范围[0,255]
|
||||
image_array = np.array(image, dtype=np.float32)
|
||||
|
||||
# 如果图像是RGBA，只取RGB通道
|
||||
if image_array.shape[-1] == 4:
|
||||
image_array = image_array[:, :, :3]
|
||||
|
||||
# 缩放到[0,1]范围 (对应scale=True)
|
||||
image_array /= 255.0
|
||||
|
||||
# 3. 标准化
|
||||
# 注意：PyTorch的Normalize是逐通道进行的
|
||||
image_array = (image_array - mean) / std
|
||||
|
||||
# 4. 转换维度从 (H,W,C) 到 (C,H,W) - 与PyTorch张量布局一致
|
||||
image_array = np.transpose(image_array, (2, 0, 1))
|
||||
|
||||
return image_array
|
||||
|
||||
return transform
|
||||
transform = make_lvd_transform(224)
|
||||
|
||||
def predict_onnx_dino(image):
|
||||
im_pil = safe_load_img(image)
|
||||
input_name_model = session_dino3.get_inputs()[0].name
|
||||
output_name_model = session_dino3.get_outputs()[0].name
|
||||
return session_dino3.run([output_name_model],
|
||||
{input_name_model:
|
||||
np.expand_dims(transform(im_pil), axis=0).astype(np.float32)
|
||||
}
|
||||
)[0]
|
||||
|
||||
# dinov3结果分类的推理代码及函数
|
||||
def predict_dino_classify(tokens1,tokens2):
|
||||
patch_tokens1 = tokens1[:, 5:, :]
|
||||
patch_tokens2 = tokens2[:, 5:, :]
|
||||
input_name_model =session_dino_cf.get_inputs()[0].name
|
||||
output_name_model =session_dino_cf.get_outputs()[0].name
|
||||
emb1 = session_dino_cf.run([output_name_model], {input_name_model: patch_tokens1})[0]
|
||||
emb2 = session_dino_cf.run([output_name_model], {input_name_model: patch_tokens2})[0]
|
||||
emb1_flat = emb1.flatten()
|
||||
emb2_flat = emb2.flatten()
|
||||
return float(np.dot(emb1_flat, emb2_flat) / (np.linalg.norm(emb1_flat) * np.linalg.norm(emb2_flat)))
|
||||
|
||||
def predict_dino_classify_pipeline(image,draw_result=False):
|
||||
im_pil = safe_load_img(image)
|
||||
if draw_result:
|
||||
draw = ImageDraw.Draw(im_pil)
|
||||
crops = predict_onnx_yolo(im_pil)
|
||||
features = {}
|
||||
for k in crops:
|
||||
features.update({k:[]})
|
||||
for v in crops[k]:
|
||||
features[k].append({"feature":predict_onnx_dino(v['image']),"bbox":v['bbox']})
|
||||
features["bottom"] = sorted(features["bottom"], key=lambda x: x["bbox"][0])
|
||||
used_indices = set()
|
||||
sequence = []
|
||||
|
||||
for target in features['bottom']:
|
||||
available = [(idx, opt) for idx, opt in enumerate(features['top']) if idx not in used_indices]
|
||||
|
||||
if not available:
|
||||
break
|
||||
|
||||
if len(available) == 1:
|
||||
best_idx, best_opt = available[0]
|
||||
else:
|
||||
best_idx, best_opt = max(
|
||||
available,
|
||||
key=lambda item: predict_dino_classify(target['feature'], item[1]['feature'])
|
||||
)
|
||||
|
||||
sequence.append(best_opt['bbox'])
|
||||
used_indices.add(best_idx)
|
||||
colors = ["red", "blue", "green", "yellow", "white", "purple", "orange"]
|
||||
points = []
|
||||
for id,one in enumerate(sequence):
|
||||
center_x = (one[0] + one[2]) / 2
|
||||
center_y = (one[1] + one[3]) / 2
|
||||
w = abs(one[0] - one[2])
|
||||
y = abs(one[1] - one[3])
|
||||
points.append((center_x+random.randint(int(-w/5),int(w/5)),
|
||||
center_y+random.randint(int(-y/5),int(y/5))
|
||||
))
|
||||
if draw_result:
|
||||
draw.rectangle(one, outline=colors[id], width=1)
|
||||
text = f"{id+1}"
|
||||
text_position = (center_x, center_y)
|
||||
draw.text(text_position, text, fill='white')
|
||||
if draw_result:
|
||||
save_path_temp = os.path.join(validate_path,"icon_result.jpg")
|
||||
im_pil.save(save_path_temp)
|
||||
logger.info(f"图片可视化结果暂时保存在{save_path_temp},运行完成后移至{save_path}")
|
||||
return points
|
||||
|
||||
|
||||
logger.info(f"使用推理设备: {ort.get_device()}")
|
||||
def use_pdl():
|
||||
load_model()
|
||||
|
||||
def use_dfine():
|
||||
load_dfine_model()
|
||||
|
||||
def use_multi():
|
||||
load_yolo11n()
|
||||
load_dinov3()
|
||||
load_dino_classify()
|
||||
|
||||
model_for = [
|
||||
{"loader":use_pdl,
|
||||
"include":["session"],
|
||||
"support":['paddle','pdl','nine','原神','genshin']
|
||||
},
|
||||
{"loader":use_dfine,
|
||||
"include":["session_dfine"],
|
||||
"support":['dfine','click','memo','note','便笺']
|
||||
},
|
||||
{"loader":use_multi,
|
||||
"include":['session_yolo11n', 'session_dino3', 'session_dino_cf'],
|
||||
"support":['multi','dino','click2','星穹铁道','崩铁','绝区零','zzz','hkrpg']
|
||||
}
|
||||
]
|
||||
|
||||
def get_models():
|
||||
res = ["以下是当前加载的模型及其对应关键字"]
|
||||
for key,value in globals().items():
|
||||
if key.startswith("session") and value is not None:
|
||||
for one in model_for:
|
||||
if key in one['include']:
|
||||
res.append(f" -{key},关键词:{one['support']}")
|
||||
return res
|
||||
def get_available_models():
|
||||
res = ["以下是所有可用模型及其对应关键字"]
|
||||
for one in model_for:
|
||||
res.append(f" -{one['include']}关键词:{one['support']}")
|
||||
return res
|
||||
|
||||
def load_by(name):
|
||||
for one in model_for:
|
||||
if name in one['support'] or name in one['include']:
|
||||
one['loader']()
|
||||
return get_models()
|
||||
logger.error(f"不支持的名称，可以使用便笺、原神、崩铁、绝区零表示")
|
||||
|
||||
def unload(*names, safe_mode=True):
|
||||
import gc
|
||||
protected_vars = {'__name__', '__file__', '__builtins__',
|
||||
'unload'}
|
||||
for name in names:
|
||||
if name in globals():
|
||||
if safe_mode and name in protected_vars:
|
||||
logger.error(f"警告: 跳过保护变量 '{name}'")
|
||||
continue
|
||||
if not name.startswith('session'):
|
||||
logger.info("删除的不是模型！")
|
||||
var = globals()[name]
|
||||
if hasattr(var, 'close'):
|
||||
try:
|
||||
var.close()
|
||||
except:
|
||||
pass
|
||||
globals()[name] = None
|
||||
logger.info(f"已释放变量: {name}")
|
||||
collected = gc.collect()
|
||||
logger.info(f"垃圾回收器清理了 {collected} 个对象")
|
||||
return get_models()
|
||||
|
||||
if int(os.environ.get("use_pdl",1)):
|
||||
load_model()
|
||||
use_pdl()
|
||||
if int(os.environ.get("use_dfine",1)):
|
||||
load_dfine_model()
|
||||
use_dfine()
|
||||
if int(os.environ.get("use_multi",1)):
|
||||
use_multi()
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 使用resnet18.onnx
|
||||
# load_model("resnet18.onnx")
|
||||
@@ -368,5 +771,7 @@ if __name__ == "__main__":
|
||||
|
||||
# 使用PP-HGNetV2-B4.onnx
|
||||
#predict_onnx_pdl(r'img_saved\img_fail\7fe559a85bac4c03bc6ea7b2e85325bf')
|
||||
predict_onnx_dfine(r"n:\爬点选\dataset\3f98ff0c91dd4882a8a24d451283ad96.jpg",True)
|
||||
|
||||
print(predict_onnx_dfine(r"f:\项目留档\JPEGImages\8bdee494b00d401aae3f496e76d886fc.jpg",True))
|
||||
# use_multi()
|
||||
# print(predict_dino_classify_pipeline("0a92e85f89b345279e74deaa9afa9e1c.jpg",True))
|
||||
|
||||
Reference in New Issue
Block a user