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System Implementation from 'Enhancing Underwater Images: Automatic Colorization using Deep Learning and Image Enhancement Techniques', 2023 IEEE International Conference on Marine Artificial Intelligence and Law (IEEE ICMAIL 2023).

License: MIT License

Python 99.63% Dockerfile 0.37%
deep-learning pytorch underwater-image-enhancement underwater-images yolov8

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give-ocean-a-piece-of-your-mind's Issues

分支意思

main分支為專題競賽使用之分支
old-waternet為目前main分支之無文件版本

視窗大小的隱患

由於本系統的視窗大小固定,可能會讓與測試電腦不同解析度的使用者有不好的使用體驗。

在 windows 下 waterNet 與 colorization 運行錯誤的問題

 def use_waterNet(self):

        def call_inference(): # inference.py (WaterNet)
            # 設定參數
            inference_path = os.path.expanduser("waternet/inference.py")
            source_path = os.path.expanduser(self.img_path)
            weights_path = os.path.expanduser("waternet/weights/last.pt")
            output_path = os.path.expanduser('res/')

            #使用subprocess.call()來呼叫inference.py程式
            subprocess.call([
               "python3", inference_path,
               "--source", source_path,
               "--weights", weights_path,
                "--output", output_path,
            ])

其中 "python3", inference_path, 是基於 ubuntu 環境下運行,如果是在 windows 環境下使用,會出現"Error: 請先上傳圖片或是您的waterNet運行有錯誤"。
請更換您的環境或是將 "python3", inference_path, 改為 "python", inference_path,

操作順序可能造成的潛在問題

#2 中有說明:處理操作順序會造成的各種意外錯誤,目前用簡單的措施應對。由於無法保證不會有錯誤發生,後續一定需要處理。

如何提高縮放圖片的精度

使用 Qt 能提高縮放圖片的精度

def load_image(self):
        pixmap = QPixmap('res/delta_e_1.png')
        scaled_pixmap = pixmap.scaled(500, 500, Qt.KeepAspectRatio, Qt.SmoothTransformation)
        self.image_label.setPixmap(scaled_pixmap)

如果遇到 waterNet 與 colorization 運行錯誤

  1. 請確認 #5
  2. 請確認 waterNet 與 colorization 是否能獨立運行
    在介面中按下 waterNet 或 colorization ,正常情況會在 terminal 中顯示運行過程,如果沒有出現如 Using device: cuda Total images/videos: 1 Saved output to res! 代表運行有問題,請檢查您的環境是否有按照文件設置。
  3. 請確認 waterNet 與 colorization 的資料夾內是否有放入權重檔案

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