complete all TODOs

app.py

@@ -15,7 +15,7 @@ import cv2
 from typing import Tuple
 from PIL import Image
 from io import BytesIO
-
+from skimage import color
 import os
 
 from Model.Model6.model6_inference import main as model6_inferencer
@@ -200,16 +200,25 @@ def model3_ext(x: np.ndarray, num_clusters=12):
     """主色调提取
     :param x: 前端传入的图片
     :param num_clusters: 聚类的数量
-    :return: 返回主色调条形卡片"""
+    :return: 返回处理后的图片,是否进行了resize的标志,颜色列表"""
 
-    # TODO: 编写颜色名称匹配算法[most important]
-    # TODO: 修改颜色条形卡片呈现形式，要求呈现颜色名称和比例[important]
-    def _find_name(color):
-        """根据颜色值查找颜色名称
-        :param color:颜色值
+    def _find_name(r, g, b):
+        """根据颜色值查找颜色名称。
+        :param r: 红色值
+        :param g: 绿色值
+        :param b: 蓝色值
         :return:返回颜色名称
         """
-        pass
+        # turn RGB to Lab
+        lab = color.rgb2lab([[r / 255, g / 255, b / 255]])[0]
+        for i in range(len(df)):
+            # culcuate the minimum chromatic distance
+            df['distance'] = np.sqrt((df['L'] - lab[0]) ** 2 + (df['a'] - lab[1]) ** 2 + (df['b'] - lab[2]) ** 2)
+        # find the color name, whose chromatic distance is the minimum, and the corresponding distance
+        name = df[df['distance'] == df['distance'].min()]['name'].values[0]
+        distance = df[df['distance'] == df['distance'].min()]['distance'].values[0]
+
+        return name, distance
 
     def _cluster(img, NUM_CLUSTERS):
         """K-means 聚类提取主色调
@@ -238,25 +247,38 @@ def model3_ext(x: np.ndarray, num_clusters=12):
         center = np.int32(center)
         x_offset = 0
         card = np.zeros((50, w, 3), dtype=np.uint8)
+        color_list = []
         for c in np.argsort(clusters)[::-1]:
             dx = int(clusters[c] * w)
-            b = center[c][0]
+            r = center[c][0]
             g = center[c][1]
-            r = center[c][2]
+            b = center[c][2]
             cv2.rectangle(card, (x_offset, 0), (x_offset + dx, 50),
-                          (int(b), int(g), int(r)), -1)
+                          (int(r), int(g), int(b)), -1)
+            color_list.append([r, g, b, str(round(clusters[c]*100, 2)) + '%'])
             x_offset += dx
 
-        return card, resized_f
+        return card, resized_f, color_list
+
+    file = '中国传统色_集合.xlsx'
+    df = pd.read_excel(file, sheet_name='Sheet1')[['name', 'L', 'a', 'b']]
 
     resized_x, resized_f = resize_image(x)
-    card, resized_f = _cluster(resized_x, num_clusters)
+    card, resized_f, c_list = _cluster(resized_x, num_clusters)
+
+    for c in c_list:
+        c_name, c_distance = _find_name(c[0], c[1], c[2])
+        c.append(c_name)
+        c.append(c_distance)
+
     if resized_f:
         resized_f = "图片尺寸已被修改至合适大小"
     else:
         resized_f = "图片尺寸无需修改"
 
-    return card, resized_f
+    c_df = pd.DataFrame(c_list, columns=['R', 'G', 'B', '比例', '颜色名称', '色差ΔE'])
+
+    return card, resized_f, c_df
 
 
 def model4_clo(x_path: str):
@@ -306,12 +328,14 @@ with gr.Blocks() as demo:
     with gr.Tab('主色调提取'):
         with gr.Row():
             with gr.Column():
-                # TODO: 参照“蒙娜丽莎”尝试修改前端界面[not important]
-                # TODO: 修改布局，使其更美观[moderately important]
                 model3_input = gr.Image(height=400, image_mode='RGBA')
                 model3_slider = gr.Slider(minimum=1, maximum=20, step=1, value=12,
                                           min_width=400, label="聚类数量")
-            model3_output_img = gr.Image(height=400)
+            with gr.Column():
+                model3_output_img = gr.Image(height=100)
+                model3_output_df = gr.DataFrame(headers=['R', 'G', 'B', '比例', '颜色名称', '色差ΔE'],
+                                                datatype=['number', 'number', 'number', 'str', 'str', 'number'])
+
         model3_button = gr.Button("开始提取")
     with gr.Tab("廓形识别"):
         with gr.Row():
@@ -326,6 +350,8 @@ with gr.Blocks() as demo:
 
     model1_button.click(model1_det, inputs=model1_input, outputs=[model1_output_img, running_info])
     model2_button.click(model2_rem, inputs=model2_input, outputs=[model2_output_img, running_info])
-    model3_button.click(model3_ext, inputs=[model3_input, model3_slider], outputs=[model3_output_img, running_info])
+    model3_button.click(model3_ext,
+                        inputs=[model3_input, model3_slider],
+                        outputs=[model3_output_img, running_info, model3_output_df])
     model4_button.click(model4_clo, inputs=model4_input, outputs=[model4_output_img, model4_output_df, running_info])
 demo.launch()