no message

.vscode/extensions.json

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+{
+    "recommendations": [
+        "ms-python.python",
+        "tabbyml.vscode-tabby"
+    ]
+}

README.md

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+# run
+bash run.sh 
+
+# 参数
+compare1 DICM 
+
+compare2 LIME
+
+
+...
+
+
+改动的只有image_file，our_method
+挑选出感觉还不错的图片之后调整crops
+代表的是两个框，[左上角，右下角，宽度，长度，缩放比例]，其中缩放比例可以视情况而定暂时都为5
+
+
+改完了运行即可，觉得合适了就下载下来
+
+any problem？
+
+
+
+

config/compare1_1.yaml

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+data_root: ./data # 这个不用动
+dataset_type: DICM # 这个是在不同的数据集上
+
+ours_method: bezierdce-9 # 这个是我们选取的比较模型
+
+image_file_1: 01.jpg # 这个是图片的文件名
+crops_1: [[500,100,100,100,5],[200,200,100,100,5]] # 这是选取的框框
+
+image_file_2: 02.jpg # 这个是图片的文件名
+crops_2: [[150,300,100,100,5],[300,500,200,200,5]] # 这是选取的框框
+
+image_file_3: 32.jpg # 这个是图片的文件名
+crops_3: [[220,300,100,100,5],[200,600,100,100,10]] # 这是选取的框框
+
+width_cm: 40 # 图片宽度设置
+
+output_image: ./result/compare1_1.pdf # 这个是输出的图片

config/compare2_1.yaml

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+data_root: ./data # 这个不用动
+dataset_type: LIME # 这个是在不同的数据集上
+
+ours_method: bezierdce-5 # 这个是我们选取的比较模型
+ours_method_1: bezierdce-10
+image_file_1: 7.bmp # 这个是图片的文件名
+crops_1: [[200,50,100,100,5],[250,250,200,200,5]] # 这是选取的框框
+
+ours_method_2: bezierdce-5
+image_file_2: 5.bmp # 这个是图片的文件名
+crops_2: [[500,200,200,200,5],[800,800,400,400,5]] # 这是选取的框框
+ours_method_3: bezierdce-5
+image_file_3: 3.bmp # 这个是图片的文件名
+crops_3: [[220,100,100,100,5],[170,300,100,100,5]] # 这是选取的框框
+
+width_cm: 40 # 图片宽度设置
+
+# 目前不能直接生成pdf，主要因为我电脑上面没有latex， 正在安装，安装完后就可以直接查看pdf 了，然后下载下来放进去，
+# 目前可以通过下载html 文件到本地然后查看
+
+
+output_image: ./result/compare2_1.pdf # 这个是输出的图片

config/compare3_1.yaml

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+data_root: ./data # 这个不用动
+dataset_type: MEF # 这个是在不同的数据集上
+
+ours_method: bezierdce-22 # 这个是我们选取的比较模型
+image_file_1: Landscape.png # 这个是图片的文件名
+crops_1: [[100,0,100,100,5],[200,200,100,100,5]] # 这是选取的框框
+
+image_file_2: Candle.png # 这个是图片的文件名
+crops_2: [[50,50,100,100,5],[120,200,100,100,5]] # 这是选取的框框
+
+image_file_3: Memorial.png # 这个是图片的文件名
+crops_3: [[50,50,100,100,5],[200,200,100,100,5]] # 这是选取的框框
+
+width_cm: 40 # 图片宽度设置
+
+# 目前不能直接生成pdf，主要因为我电脑上面没有latex， 正在安装，安装完后就可以直接查看pdf 了，然后下载下来放进去，
+# 目前可以通过下载html 文件到本地然后查看
+
+
+output_image: ./result/compare3_1.pdf # 这个是输出的图片

config/compare4_1.yaml

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+data_root: ./data # 这个不用动
+dataset_type: NPE # 这个是在不同的数据集上
+
+ours_method: bezierdce-22 # 这个是我们选取的比较模型
+
+image_file_1: rail.png # 这个是图片的文件名
+crops_1: [[50,0,100,100,5],[200,200,100,100,5]] # 这是选取的框框
+
+
+
+image_file_2: harbor.png # 这个是图片的文件名
+crops_2: [[0,0,100,100,5],[200,200,100,100,5]] # 这是选取的框框
+
+
+image_file_3: night fall.png # 这个是图片的文件名
+crops_3: [[350,0,100,100,5],[100,100,200,200,5]] # 这是选取的框框
+
+width_cm: 40 # 图片宽度设置
+
+# 目前不能直接生成pdf，主要因为我电脑上面没有latex， 正在安装，安装完后就可以直接查看pdf 了，然后下载下来放进去，
+# 目前可以通过下载html 文件到本地然后查看
+
+
+output_image: ./result/compare4_1.pdf # 这个是输出的图片

config/compare5_1.yaml

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+data_root: ./data # 这个不用动
+dataset_type: VV # 这个是在不同的数据集上
+
+ours_method: bezierdce-22 # 这个是我们选取的比较模型
+
+
+
+
+image_file_1: P1020044.jpg # 这个是图片的文件名
+crops_1: [[700,500,200,200,5],[800,800,400,400,5]] # 这是选取的框框
+
+ours_method_2: bezierdce-22
+image_file_2: P1000333.jpg # 这个是图片的文件名
+crops_2: [[0,400,600,600,5],[600,1200,400,400,5]] # 这是选取的框框
+
+
+image_file_3: P1000511.jpg # 这个是图片的文件名
+crops_3: [[0,0,400,400,5],[800,800,400,400,5]] # 这是选取的框框
+
+width_cm: 40 # 图片宽度设置
+
+# 目前不能直接生成pdf，主要因为我电脑上面没有latex， 正在安装，安装完后就可以直接查看pdf 了，然后下载下来放进去，
+# 目前可以通过下载html 文件到本地然后查看
+
+
+output_image: ./result/compare5_1.pdf # 这个是输出的图片

data

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+/data/compare

main.py

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+import simpleimageio as sio
+import figuregen
+from figuregen.util.templates import FullSizeWithCrops
+from figuregen.util.image import Cropbox
+
+
+import os
+import argparse
+import yaml
+import numpy as np
+
+
+class MyCrops(FullSizeWithCrops):
+    def __init__(self, reference_image, method_images, crops,
+                 crops_below = True, method_names = None, use_latex = False,add=False):
+        """ Shows a reference image next to a grid of crops from different methods.
+
+        Args:
+            reference_image: a reference image (or any other image to put full-size in the lefthand grid)
+            method_images: list of images, each corresponds to a new column in the crop grid
+            crops: list of crops to take from each method, each creates a new row and a marker on the reference
+            crops_below: [optional] if False, the crops will be a column to the right of each image
+            method_names: [optional] list of string, names for the reference and each method, to put above the crops
+            use_latex: set to true to pretty-print captions with LaTeX commands (requires TikZ backend)
+
+        Returns:
+            A list of two grids:
+            The first is a single image (reference), the second a series of crops, one or more for each method.
+        """
+        self._reference_image = reference_image
+        self._method_images = method_images
+        self.use_latex = use_latex
+        self._crops_below = crops_below
+
+        self._errors = [
+            self.compute_error(reference_image, m)
+            for m in method_images
+        ]
+        self._crop_errors = [
+            [
+                self.compute_error(crop.crop(reference_image), crop.crop(m))
+                for m in method_images
+            ]
+            for crop in crops
+        ]
+
+        # Put in one list to make our life easier in the following
+        images = [reference_image]
+        images.extend(method_images)
+
+        # Create the grid for the reference image
+        self._ref_grid = [ figuregen.Grid(1, 1) for _ in range(len(images)) ]
+        for i in range(len(images)):
+            self._ref_grid[i][0, 0].image = self.tonemap(images[i])
+            for crop in crops:
+                self._ref_grid[i][0, 0].set_marker(crop.marker_pos, crop.marker_size, color=[255,255,255])
+
+        # Create the grid with the crops
+        if self._crops_below:
+            self._crop_grid = [
+                figuregen.Grid(num_cols=len(crops), num_rows=1)
+                for _ in range(len(images))
+            ]
+            for i in range(len(images)):
+                for col in range(len(crops)):
+                    self._crop_grid[i][0, col].image = self.tonemap(crops[col].crop(images[i]))
+        else:
+            self._crop_grid = [
+                figuregen.Grid(num_cols=1, num_rows=len(crops))
+                for _ in range(len(images))
+            ]
+            for i in range(len(images)):
+                for row in range(len(crops)):
+                    self._crop_grid[i][row, 0].image = self.tonemap(crops[row].crop(images[i]))
+
+        # Add padding to the right of all but the last image
+        for i in range(len(images) - 1):
+            self._ref_grid[i].layout.set_padding(right=1)
+            self._crop_grid[i].layout.set_padding(right=1)
+            if self._crops_below:
+                self._ref_grid[i].layout.set_padding(bottom=1)
+
+        if self._crops_below:
+            self._ref_grid[-1].layout.set_padding(bottom=1)
+        else:
+            self._ref_grid[-1].layout.set_padding(right=1)
+
+        # Put error values underneath the columns
+        if self._crops_below:
+            for i in range(len(images)):
+                # if i > 0:
+                #     err = self.error_string(i - 1, self.errors)
+                # else:
+                #     err = self.error_metric_name
+                if add:
+                    self._crop_grid[i].set_title("bottom", method_names[i])
+                    self._crop_grid[i].layout.set_title("bottom", 6, 1, 8)
+        else:
+            pass # TODO
+
+        # TODO this requires titles spanning multiple grids (the image and its crops)!
+        # error_strings = [ f"{self.error_metric_name}" ]
+        # error_strings.extend([ self.error_string(i, self.errors) for i in range(len(self.errors)) ])
+        # self._crop_grid.set_col_titles("bottom", error_strings)
+        # self._crop_grid.layout.set_padding(column=1, row=1)
+        # self._crop_grid.layout.set_col_titles("bottom", fontsize=8, field_size_mm=2.8, offset_mm=0.5)
+
+        # If given, show method names on top
+        # TODO combine with error values, and always show both or neither
+        # if method_names is not None:
+        #     self._crop_grid.set_col_titles("top", method_names)
+        #     self._crop_grid.layout.set_col_titles("top", fontsize=8, field_size_mm=2.8, offset_mm=0.25)
+
+        # self._ref_grid.copy_layout(self._crop_grid)
+        # self._ref_grid.layout.set_padding(right=1)
+        # TODO set appropriate paddings for alignment etc
+
+    def error_string(self, index, errors):
+        """ Generates the human-readable error string for the i-th element in a list of error values.
+
+        Args:
+            index: index in the list of errors
+            errors: list of error values, one per method, in order
+        """
+        if self.use_latex and index == np.argmin(errors):
+            return f""
+        elif self.use_latex:
+            return f""
+        else:
+            return f""
+    def error_metric_name(self) -> str:
+        return ""
+
+
+
+def main(data_root,image_file_1,image_file_2,image_file_3,dataset_type,ours_method,crops_1,crops_2,crops_3,width_cm,output_image,ours_method_1=None,ours_method_2=None,ours_method_3=None):
+
+    data_root = os.path.join(data_root)
+
+    original_image_1 = sio.read(os.path.join(data_root,"original",dataset_type,image_file_1))
+    # 写完了你在这个里面直接运行就可以
+    KinD_image_1= sio.read(os.path.join(data_root,"KinD",dataset_type,image_file_1))
+    LIME_image_1= sio.read(os.path.join(data_root,"LIME",dataset_type,image_file_1))
+    NPE_image_1 = sio.read(os.path.join(data_root,"NPE",dataset_type,image_file_1))
+    SRIE_image_1 = sio.read(os.path.join(data_root,"SRIE",dataset_type,image_file_1))
+    ZeroDCE_image_1 = sio.read(os.path.join(data_root,"ZeroDCE",dataset_type,image_file_1))
+    ours_image_1 = sio.read(os.path.join(data_root,ours_method_1 if ours_method_1 else ours_method,dataset_type,image_file_1))
+
+    original_image_2 = sio.read(os.path.join(data_root,"original",dataset_type,image_file_2))
+    # 写完了你在这个里面直接运行就可以
+    KinD_image_2= sio.read(os.path.join(data_root,"KinD",dataset_type,image_file_2))
+    LIME_image_2= sio.read(os.path.join(data_root,"LIME",dataset_type,image_file_2))
+    NPE_image_2 = sio.read(os.path.join(data_root,"NPE",dataset_type,image_file_2))
+    SRIE_image_2 = sio.read(os.path.join(data_root,"SRIE",dataset_type,image_file_2))
+    ZeroDCE_image_2 = sio.read(os.path.join(data_root,"ZeroDCE",dataset_type,image_file_2))
+    ours_image_2 = sio.read(os.path.join(data_root,ours_method_2 if ours_method_2 else ours_method,dataset_type,image_file_2))
+
+    original_image_3 = sio.read(os.path.join(data_root,"original",dataset_type,image_file_3))
+    # 写完了你在这个里面直接运行就可以
+    KinD_image_3= sio.read(os.path.join(data_root,"KinD",dataset_type,image_file_3))
+    LIME_image_3= sio.read(os.path.join(data_root,"LIME",dataset_type,image_file_3))
+    NPE_image_3 = sio.read(os.path.join(data_root,"NPE",dataset_type,image_file_3))
+    SRIE_image_3 = sio.read(os.path.join(data_root,"SRIE",dataset_type,image_file_3))
+    ZeroDCE_image_3 = sio.read(os.path.join(data_root,"ZeroDCE",dataset_type,image_file_3))
+    ours_image_3 = sio.read(os.path.join(data_root,ours_method_3 if ours_method_3 else ours_method,dataset_type,image_file_3))
+
+    output_image = os.path.abspath(output_image)
+
+
+
+
+    figure_1 = MyCrops(
+        reference_image=original_image_1,
+        method_images=[
+            KinD_image_1,
+            LIME_image_1,
+            NPE_image_1,
+            SRIE_image_1,
+            ZeroDCE_image_1,
+            ours_image_1
+        ],
+        crops=[
+            Cropbox(top=crop[0], left=crop[1], height=crop[2], width=crop[3], scale=crop[4]) for crop in crops_1
+        ],
+        # scene_name="Pool",
+        method_names=["Reference", "KinD", "LIME", "NPE", "SRIE", "ZeroDCE", "Ours"],
+        use_latex=True
+    ).figure
+
+
+    figure_2 = MyCrops(
+        reference_image=original_image_2,
+        method_images=[
+            KinD_image_2,
+            LIME_image_2,
+            NPE_image_2,
+            SRIE_image_2,
+            ZeroDCE_image_2,
+            ours_image_2
+        ],
+        crops=[
+            Cropbox(top=crop[0], left=crop[1], height=crop[2], width=crop[3], scale=crop[4]) for crop in crops_2
+        ],
+        # scene_name="Pool",
+        method_names=["Reference", "KinD", "LIME", "NPE", "SRIE", "ZeroDCE", "Ours"],
+        use_latex=True
+    ).figure
+
+    figure_3=MyCrops(
+        reference_image=original_image_3,
+        method_images=[
+            KinD_image_3,
+            LIME_image_3,
+            NPE_image_3,
+            SRIE_image_3,
+            ZeroDCE_image_3,
+            ours_image_3
+        ],
+        crops=[
+            Cropbox(top=crop[0], left=crop[1], height=crop[2], width=crop[3], scale=crop[4]) for crop in crops_3
+        ],
+        method_names=["Reference", "KinD", "LIME", "NPE", "SRIE", "ZeroDCE", "Ours"],
+        use_latex=True,
+        add=True
+    ).figure
+        # scene_name="Pool",
+
+
+    figuregen.figure([figure_1[0],figure_1[1],figure_2[0],figure_2[1],figure_3[0],figure_3[1]], width_cm=width_cm, filename=output_image)
+    # grid = figuregen.Grid(3,1)
+    # grid[0,0].set_image(figure)
+    # grid[1,0].set_image(figure)
+    # grid[2,0].set_image(figure)
+
+
+
+
+
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", type=str, default="./config/compare3_1.yaml")
+    args = parser.parse_args()
+    with open(args.config, "r") as f:
+        config = yaml.safe_load(f)
+    main(**config)

run.sh

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+python main.py --config ./config/compare1_1.yaml
+
+python main.py --config ./config/compare2_1.yaml
+
+python main.py --config ./config/compare3_1.yaml
+
+python main.py --config ./config/compare4_1.yaml
+
+python main.py --config ./config/compare5_1.yaml