Update pictureDeal.py

pictureDeal.py

@@ -1,7 +1,74 @@
+import cv2  
+from PIL import Image, ImageEnhance
 import gradio as gr
+from sklearn.cluster import KMeans
+import numpy as np
 
-def greet(name):
-    return "Hello " + name + "!!"
+with gr.Blocks() as interface:
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+    with gr.Row():
+        n_colors = gr.Slider(2, 32, 12, step=1, label="图片要加工的目标颜色数量")
+     
+    with gr.Row():
+        img_input = gr.Image()
+        img_output = gr.Image()
+    
+    section_btn1 = gr.Button("合并色彩")
+    
+    # 图片模型训练
+    def img_fit_predict(img,n_colors):
+        data = img.reshape(-1,3)
+        # 把原始图片压缩成n_colors个颜色
+        kmeans = KMeans(n_clusters=n_colors)
+        y_ = kmeans.fit_predict(data)
+        # 模型合并颜色
+        colors = kmeans.cluster_centers_/255
+        output_temp = colors[y_].reshape(img.shape)
+        return output_temp
+    
+    section_btn1.click(img_fit_predict, inputs=[img_input,n_colors], outputs=img_output)
+    
+    with gr.Row():
+        gaussian_blur = gr.Slider(1, 13, 13, step=2, label="整体降噪参数调整")
+        structuring_element = gr.Slider(1, 13, 3, step=2, label="去除小噪声")
+        canny_start = gr.Slider(1, 200, 4, step=1, label="边缘检测-开始参数")
+        canny_end = gr.Slider(1, 200, 10, step=1, label="边缘检测-结束参数")
+    
+    with gr.Row():
+        thresh_val = gr.Slider(50, 500, 205, step=1, label="二值图像-thresh")
+        maxval = gr.Slider(50, 500, 330, step=1, label="二值图像-maxval")
+        enhance = gr.Slider(0, 1, 0.8, step=0.1, label="增强颜色-enhance")
+        blend = gr.Slider(0, 1, 0.4, step=0.1, label="增强颜色-blend")
+     
+    section_btn2 = gr.Button("调整图片")   
+    with gr.Row():
+        closed_output = gr.Image()
+        img_param_output = gr.Image()
+
+    # 调整模型结果参数
+    def turn_arguments(img,img_output,gaussian_blur,structuring_element,canny_start,canny_end,thresh_val,maxval,enhance,blend):
+        gray = cv2.cvtColor(img_output, cv2.COLOR_BGR2GRAY) 
+        # 对灰度图像进行高斯滤波，以去除噪声  
+        gray = cv2.GaussianBlur(gray, (gaussian_blur,gaussian_blur), 0)  
+        # 使用Canny算子进行边缘检测  
+        edges = cv2.Canny(gray, canny_start, canny_end)
+        # 将边缘图像转换为二值图像  
+        _, thresh = cv2.threshold(edges, thresh_val, maxval, cv2.THRESH_BINARY) 
+        # 对二值图像进行形态学操作，以去除小的噪点  
+        kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (structuring_element, structuring_element))  
+        closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
+        image = Image.fromarray(img_output)
+        closed = closed.astype(img.dtype)
+        # 颜色空间转换  
+        enhancer = ImageEnhance.Color(image=image)
+        # 增强颜色  
+        img1 = enhancer.enhance(enhance).convert('RGB')
+        img2 = Image.fromarray(closed).convert('RGB')
+        union_img = np.asarray(Image.blend(img2, img1, blend))
+        return closed,union_img
+    
+    section_btn2.click(turn_arguments,inputs=[img_input, img_output,gaussian_blur,
+                                              structuring_element,canny_start,canny_end,thresh_val,maxval,enhance,blend ],
+                                    outputs = [closed_output,img_param_output])
+
+interface.launch()