-update: version beta1.0

individual detection strategy code work
not integrated yet

count_env.py

@@ -0,0 +1,135 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Time    : 2024/6/13 0013 下午 12:03
+# @Author  : liudan
+# @File    : count_env.py
+# @Software: pycharm
+import cv2
+import os
+from PIL import Image, ImageDraw
+import numpy as np
+
+from skimage.metrics import structural_similarity as compare_ssim
+import json
+import random
+import yaml
+import demo_env
+from demo_env import registration_demo
+
+
+def compare_boxes_similarity(image1_path, image2_path, json_file_path, similarity_threshold=0.4):
+    try:
+        if not os.path.exists(image1_path):
+            raise FileNotFoundError(f"Image file {image1_path} not found")
+
+
+        image1 = Image.open(image1_path)# 原图尺寸，未resize
+        image2 = wrap_image
+
+        draw1 = ImageDraw.Draw(image1)
+
+        # 存储相似度结果和是否相同的判断
+        similarity_results = []
+        same_content_boxes = []
+
+
+        with open(json_file_path, 'r') as f:
+            data = json.load(f)
+
+
+        for shape in data['shapes']:
+            if 'points' in shape:
+                shape['points'] = [[int(round(x)), int(round(y))] for x, y in shape['points']]
+                x1, y1 = shape['points'][0]
+                x2, y2 = shape['points'][1]
+
+            # 从两幅图像中截取对应区域
+            region1 = image1.crop((x1, y1, x2, y2))
+            # region1 = image1.crop((x1, y1, x2, y2)).convert('L')
+            draw1.rectangle([x1, y1, x2, y2], outline='red', width=2)
+            image1.save(os.path.join(params['save_dir'], f'save_annotated1_{i}.jpg'))
+            region1.save(os.path.join(params['save_dir'], f'111111{i}.jpg'))
+
+
+            # region2 = image2.crop((left-80, top, right-80, bottom))
+            region2 = image2[y1:y2, x1:x2]
+            # region2 = cv2.cvtColor(region2, cv2.COLOR_BGR2GRAY)
+            # region2= region2.transpose(Image.FLIP_TOP_BOTTOM) #旋转180°针对pillowImage对象
+            # region2 = cv2.rotate(region2, cv2.ROTATE_180)
+
+            filename = f'json_image_{shape["label"]}_{i}.jpg'
+            cv2.imwrite(os.path.join(params['save_dir'], filename), region2)
+            cv2.rectangle(image2, (x1, y1), (x2, y2),(0,255,0), 2)
+            cv2.imwrite(os.path.join(params['save_dir'], f'save_annotated2_{i}.jpg'), image2)
+
+            # 将PIL图像转换为numpy数组，以便进行计算
+            arr1 = np.array(region1)
+            arr2 = region2 # region2一直是numpy数组，所以上述image1和image2处理方式不同
+
+            # 确保两个数组的形状是相同的
+            assert arr1.shape == arr2.shape, "Images do not have the same size for the given box"
+
+            # 使用SSIM计算相似度（范围在-1到1之间，1表示完全相似）
+            # ssim = compare_ssim(arr1, arr2, multichannel=False) # 这是旧版，可以计算灰度图相似度，对于计算彩色图像即使设置multichannel=True也错
+            ssim = compare_ssim(arr1, arr2, channel_axis=2)
+
+
+            similarity_results.append(ssim)
+
+            if ssim > similarity_threshold:
+                same_content_boxes.append(shape)
+                cv2.rectangle(image2, (x1, y1), (x2, y2),(0,255,0), 2)
+
+                text = "Similarity: " + str(round(ssim, 3))
+                text_pos = (x1, y1 - 5)
+                # 参数：图像, 文本, 文本位置, 字体类型, 字体大小, 字体颜色, 字体粗细
+                cv2.putText(image2, text, text_pos, cv2.FONT_HERSHEY_SIMPLEX, 1.5, (0, 255, 0), 2)
+                cv2.imwrite(os.path.join(params['visualization_dir'],f'{wrap_images_name[:-8]}_{i}.jpg'), image2)
+
+            else:
+                cv2.rectangle(image2, (x1, y1), (x2, y2), (0, 0, 255), 2)
+                text = "score: " + str(round(ssim, 3))
+                text_pos = (x1, y1 - 5)
+                # 参数：图像, 文本, 文本位置, 字体类型, 字体大小, 字体颜色, 字体粗细
+                cv2.putText(image2, text, text_pos, cv2.FONT_HERSHEY_SIMPLEX, 1.5, (0, 0, 255), 2)
+                cv2.imwrite(os.path.join(params['visualization_dir'], f'{wrap_images_name[:-8]}_{i}.jpg'), image2)
+
+        return similarity_results, same_content_boxes
+
+    except FileNotFoundError as e:
+        print(f"An error occurred: {e}")
+    except Exception as e:
+        print(f"An unexpected error occurred: {e}")
+    return None, None
+
+
+def read_params_from_yml(yml_file_path):
+    with open(yml_file_path, 'r') as file:
+        params = yaml.safe_load(file)
+    return params
+
+
+
+
+
+if __name__ == "__main__":
+    yml_file_path = 'params.yml'
+    params = read_params_from_yml(yml_file_path)
+
+
+
+    wrap_images_all = registration_demo(params['image_dir'],params['demo_image_path'], params['json_ref_path'], params['ref_image_path'])
+    for i, item in enumerate(wrap_images_all):
+
+        wrap_image,wrap_images_name = item
+        similarity_results, same_content_boxes = compare_boxes_similarity(params['path_to_image1'], wrap_image, params['json_file_path'],
+                                                                          params['similarity_threshold'])
+        # 打印所有坐标框的相似度结果
+        print(f"{wrap_images_name}\n")
+        for idx, score in enumerate(similarity_results, 1):
+            print(f"Similarity Score for Box {idx}: {score}")
+
+        # 打印被认为是相同内容的坐标框
+        print("Boxes with the same content:")
+        for shape in same_content_boxes:
+            print(shape['label'] + ' object is same as template')