139 lines
5.4 KiB
Python
139 lines
5.4 KiB
Python
import datetime
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import json
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import os.path
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import time
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from io import BytesIO
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from PIL import Image
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from torchvision import transforms
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from CropLand_CD_module.change_detection_module import ChangeDetectionModule
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from CropLand_CD_module.visualize_pil_segmentation_mask import visualize_pil_segmentation_mask_opencv
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from middleware.conver_segementation_mask import apply_mask_to_image
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from middleware.minio_util import downFile, upload_file, upload_file_from_buffer
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from mqtt_pub import MQTTClient
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# from change_detection_module import ChangeDetectionModule
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mqtt_client=None
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# MQTT 代理地址和端口
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# broker = "112.44.103.230" # 公共 MQTT 代理（免费）
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broker = "8.137.54.85" # 公共 MQTT 代理（免费）
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port = 1883 # MQTT 默认端口
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# 主题
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topic = "thing/product/ai/events"
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def corpland_detection_func(task_id, s3_id, early_pic_url, later_pic_url, model_func_id):
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print("\n=== 变化检测系统 ===")
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gpu_id = "0"
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img_size = 256 # 模型预期的输入尺寸
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lr = 0.0001
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early_pic = downFile(early_pic_url)
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later_pic = downFile(later_pic_url)
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# from PIL import Image
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# image1 = Image.open(image1_path).convert('RGB')
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# image2 = Image.open(image2_path).convert('RGB')
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# image1 = Image.open(early_pic).convert('RGB')
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# image2 = Image.open(later_pic).convert('RGB')
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early_pic_url=os.path.abspath(early_pic)
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later_pic_url=os.path.abspath(later_pic)
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# windows路径
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# model_path = r'D:\project\2025-7-10_data+model\CropLand-CD-main_3\CropLand-CD-main\save_epoch\best_model.pth'
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# save_dir= r"D:\project\2025-7-10_data+model\CropLand-CD-main_3\CropLand-CD-main\save"
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# # linux路径
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model_path = r"CropLand_CD_module/save_epoch/best_model.pth"
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save_dir= r"CropLand_CD_module/save"
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cd = ChangeDetectionModule(gpu_id, img_size, lr)
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cd.load_model(model_path)
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out_save_path = cd.predict_from_imgurl(early_pic_url,later_pic_url,save_dir)
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print(out_save_path)
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pic_path = os.path.abspath(later_pic)
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# src_crs, instance_results_json_path, vis_png, vis_json=visualize_pil_segmentation_mask_opencv(out_save_path,pic_path)
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src_crs, instance_results_json_path, vis_png, _ = visualize_pil_segmentation_mask_opencv(out_save_path, pic_path)
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mask_dir=os.path.dirname(out_save_path)
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mask_name=os.path.basename(out_save_path)
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convert_mask_with_pic_name="mask_"+mask_name
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convert_mask_path=os.path.join(mask_dir,convert_mask_with_pic_name)
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apply_mask_to_image(
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image_path=later_pic_url,
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mask_path=out_save_path,
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output_path=convert_mask_path,
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alpha=0.4
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)
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date_str = datetime.datetime.now().strftime("%Y%m%d")
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time_s = datetime.datetime.now().timestamp()
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mqtt_client = MQTTClient(broker, port, topic)
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pic_name = os.path.basename(later_pic)
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pic_name_1 = f"{date_str}/{time_s}-1-{pic_name}"
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pic_name_2 = f"{date_str}/{time_s}-2-{pic_name}"
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pic_name = f"{date_str}/{time_s}-result-{pic_name}"
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minio_path_1, file_type_1 = upload_file(early_pic, None)
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minio_path_2, file_type_2 = upload_file(later_pic, None)
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minio_path_result, file_type_result = upload_file(convert_mask_path, None)
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minio_vis_png, file_type_result = upload_file(vis_png, None)
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# if instance_results_json_path and os.path.exists(instance_results_json_path):
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# # minio_vis_json, file_type_result = upload_file(json_result_path, "application/json")
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# minio_vis_json, file_type_result = upload_file(instance_results_json_path, None)
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# # minio_path_result, file_type_result = upload_file_from_buffer(buffer, pic_name)
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if instance_results_json_path is None or not os.path.exists(instance_results_json_path):
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print("警告: 无法生成有效的JSON结果文件，使用空文件替代")
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# 创建一个空的JSON文件作为替代
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empty_result = {"error": "无法生成有效的结果", "instances": []}
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instance_results_json_path = os.path.join(os.path.dirname(out_save_path), "empty_result.json")
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with open(instance_results_json_path, 'w') as f:
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json.dump(empty_result, f)
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minio_vis_json, file_type_result = upload_file(instance_results_json_path, None)
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message = {
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"task_id": task_id,
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"minio": {
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"minio_path_1": minio_path_1,
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"minio_path_2": minio_path_2,
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"minio_path_result": minio_path_result,
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"minio_path_boundary": minio_vis_png,
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"minio_path_json": minio_vis_json,
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"crs_info":str(src_crs),
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"file_type": file_type_result
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}
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}
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json_message = json.dumps(message, indent=4, ensure_ascii=False)
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mqtt_client.publish_message(json_message)
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time.sleep(0.5)
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# 删除一期
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if os.path.exists(early_pic):
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os.remove(early_pic)
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# 删除二期
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if os.path.exists(later_pic):
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os.remove(later_pic)
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# 删除掩码
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if os.path.exists(out_save_path):
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os.remove(out_save_path)
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# 删除掩码覆盖原图
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if os.path.exists(convert_mask_path):
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os.remove(convert_mask_path)
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# 删除本地掩码图片
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if os.path.exists(minio_vis_png):
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os.remove(minio_vis_png)
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# 删除本地json文件
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if os.path.exists(minio_vis_json):
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os.remove(minio_vis_json)
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# 删除修改经纬度之后的json文件
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if os.path.exists(instance_results_json_path):
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os.remove(instance_results_json_path)
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# if __name__ == '__main__':
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