diff --git a/calculate_model.py b/calculate_model.py
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index 0000000..e4cad47
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+++ b/calculate_model.py
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+import model_elem
+
+import cv2
+import torch
+import torchvision
+import numpy as np
+import torchvision.transforms as transforms
+from torchvision.models.detection import KeypointRCNN_ResNet50_FPN_Weights
+# 加载pytorch提供的keypointrcnn_resnet50_fpn()网络模型，可以对17个人体关键点进行检测。
+#如果有可用的GPU，则使用GPU，否则使用CPU。
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+#pytorch模型的python接口
+model = torchvision.models.detection.keypointrcnn_resnet50_fpn(weights=KeypointRCNN_ResNet50_FPN_Weights.DEFAULT)
+model.to(device)
+model.eval()
+
+def Capture_Point(image, confidence=0.9):
+    image = cv2.cvtColor(image, cv2.COLOR_RGBA2RGB)
+    # 准备需要检测的图像
+    transform_d = transforms.Compose([transforms.ToTensor()])
+    image_t = transform_d(image)    ## 对图像进行变换
+    pred = model([image_t.to(device)])         ## 将模型作用到图像上
+    # 检测出目标的类别和得分
+    pred_class = [model_elem.OBJECT_LIST[ii] for ii in list(pred[0]['labels'].cpu().numpy())]
+    pred_score = list(pred[0]['scores'].detach().cpu().numpy())
+    # 检测出目标的边界框
+    pred_boxes = [[ii[0], ii[1], ii[2], ii[3]] for ii in list(pred[0]['boxes'].detach().cpu().numpy())]
+    ## 只保留识别的概率大约 confidence 的结果。
+    pred_index = [pred_score.index(x) for x in pred_score if x > confidence]
+    for index in pred_index:
+        box = pred_boxes[index]
+        box = [int(i) for i in box]
+        cv2.rectangle(image,(int(box[0]),int(box[1])),(int(box[2]),int(box[3])),(0,255,255))
+        texts = pred_class[index] + ":" + str(np.round(pred_score[index], 2))
+        cv2.putText(image, texts,(box[0], box[1]), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 255), 2)
+
+    pred_keypoint = pred[0]["keypoints"]
+    # 检测到实例的关键点
+    pred_keypoint = pred_keypoint[pred_index].detach().cpu().numpy()
+    # 对实例数量索引
+    my_result = {}
+    for index in range(pred_keypoint.shape[0]):
+        # 对每个实例的关键点索引
+        keypoints = pred_keypoint[index]
+        for ii in range(keypoints.shape[0]): ##ii为第几个坐标点
+            x = int(keypoints[ii, 0]) #x坐标
+            y = int(keypoints[ii, 1]) #y坐标
+            visi = keypoints[ii, 2] #置信度
+            if visi > 0.:
+                cv2.circle(image, (int(x),int(y)), 1, (0,0,255),4)
+                texts = str(ii+1)
+                cv2.putText(image,texts, (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 255), 2)
+                my_result[texts] = (int(x), int(y))
+    return image,my_result
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