Create pictureDeal2.py

pictureDeal2.py

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+import cv2  
+from PIL import Image, ImageEnhance,ImageColor
+import gradio as gr
+import numpy as np
+
+with gr.Blocks() as interface:
+
+    with gr.Accordion("请选择一张图片"):
+        # gr.Markdown("Look at me...")
+        img_input = gr.Image(label='请选择一张待加工图片')
+
+    with gr.Accordion("每次调整参数后，点击【加工图片】按钮，得到图片的勾边"):
+        with gr.Row():
+            enhance = gr.Slider(0, 1, 0.8, step=0.1, label="图片彩色度")
+            blend = gr.Slider(0, 1, 0.4, step=0.1, label="颜色填充度")
+            color = gr.ColorPicker(label="勾边颜色")
+        
+        section_btn = gr.Button("加工图片")
+    
+    with gr.Accordion("提供4种勾边效果，均可下载本地"):
+        with gr.Row():
+            closed_output0 = gr.Image(label='自选颜色勾边')
+            img_param_output0 = gr.Image(label='极简勾边')
+        
+        with gr.Row():
+            closed_output1 = gr.Image(label='自选颜色勾边')
+            img_param_output1 = gr.Image(label='简单勾边')
+        
+        with gr.Row():
+            closed_output2 = gr.Image(label='自选颜色勾边')
+            img_param_output2 = gr.Image(label='细致勾边')
+            
+        with gr.Row():
+            closed_output3 = gr.Image(label='彩色勾边')
+            img_param_output3 = gr.Image(label='图片+勾边合成图')
+        
+
+    # 调整模型结果参数
+    def turn_arguments(img,enhance,blend,color):
+        imageX = Image.fromarray(img)
+        contrast = ImageEnhance.Contrast(imageX)
+        imageX = contrast.enhance(1.5)
+        sharpness = ImageEnhance.Sharpness(imageX)
+        imageX = sharpness.enhance(1.5)
+        img = np.asarray(imageX)
+
+        #####################################
+        #   极简勾边-自选颜色                #
+        #####################################
+        gaussian_blur_0 = 13
+        structuring_element_0 = 3
+        canny_start_0 = 65
+        canny_end_0 = 100
+        thresh_val_0 = 205
+        maxval_0 = 330
+        gray0 = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        # 对灰度图像进行高斯滤波，以去除噪声  
+        gray0 = cv2.GaussianBlur(gray0, (gaussian_blur_0,gaussian_blur_0), 0)
+        # 使用Canny算子进行边缘检测  
+        edges0 = cv2.Canny(gray0, canny_start_0, canny_end_0)
+        # 将边缘图像转换为二值图像  
+        _, thresh0 = cv2.threshold(edges0, thresh_val_0, maxval_0, cv2.THRESH_BINARY) 
+        # 对二值图像进行形态学操作，以去除小的噪点  
+        kernel0 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (structuring_element_0, structuring_element_0))  
+        closed0 = cv2.morphologyEx(thresh0, cv2.MORPH_CLOSE, kernel0)
+        closed0 = closed0.astype(img.dtype)
+        result0 = cv2.bitwise_and(img, img, mask=closed0)
+        result0[closed0==0] = (255,255,255)
+        line_color0 = ImageColor.getcolor(color, "RGB")
+        result0[closed0!=0] = (line_color0)
+        close00 = Image.fromarray(result0).convert('RGB')
+
+        # 颜色空间转换  
+        image0 = Image.fromarray(img)
+        enhancer0 = ImageEnhance.Color(image=image0)
+        # 增强颜色  
+        img0 = enhancer0.enhance(enhance).convert('RGB')
+        union_img0 = np.asarray(Image.blend(close00, img0, blend))
+
+
+        #####################################
+        #   简单勾边-自选颜色                #
+        #####################################
+        gaussian_blur_1 = 13
+        structuring_element_1 = 3
+        canny_start_1 = 25
+        canny_end_1 = 45
+        thresh_val_1 = 205
+        maxval_1 = 330
+        gray1 = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        # 对灰度图像进行高斯滤波，以去除噪声  
+        gray1 = cv2.GaussianBlur(gray1, (gaussian_blur_1,gaussian_blur_1), 0)
+        # 使用Canny算子进行边缘检测  
+        edges1 = cv2.Canny(gray1, canny_start_1, canny_end_1)
+        # 将边缘图像转换为二值图像  
+        _, thresh1 = cv2.threshold(edges1, thresh_val_1, maxval_1, cv2.THRESH_BINARY) 
+        # 对二值图像进行形态学操作，以去除小的噪点  
+        kernel1 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (structuring_element_1, structuring_element_1))  
+        closed1 = cv2.morphologyEx(thresh1, cv2.MORPH_CLOSE, kernel1)
+        closed1 = closed1.astype(img.dtype)
+        result1 = cv2.bitwise_and(img, img, mask=closed1)
+        result1[closed1==0] = (255,255,255)
+        line_color1 = ImageColor.getcolor(color, "RGB")
+        result1[closed1!=0] = (line_color1)
+        close01 = Image.fromarray(result1).convert('RGB')
+
+        # 颜色空间转换  
+        image1 = Image.fromarray(img)
+        enhancer1 = ImageEnhance.Color(image=image1)
+        # 增强颜色  
+        img1 = enhancer1.enhance(enhance).convert('RGB')
+        union_img1 = np.asarray(Image.blend(close01, img1, blend))
+
+        #####################################
+        #   复杂勾边-自选颜色                #
+        #####################################
+        gaussian_blur_2 = 13
+        structuring_element_2 = 3
+        canny_start_2 = 10
+        canny_end_2 = 40
+        thresh_val_2 = 205
+        maxval_2 = 330
+        gray2 = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 
+        # 对灰度图像进行高斯滤波，以去除噪声  
+        gray2 = cv2.GaussianBlur(gray2, (gaussian_blur_2,gaussian_blur_2), 0)  
+        # 使用Canny算子进行边缘检测  
+        edges2 = cv2.Canny(gray2, canny_start_2, canny_end_2)
+        # 将边缘图像转换为二值图像  
+        _, thresh2 = cv2.threshold(edges2, thresh_val_2, maxval_2, cv2.THRESH_BINARY) 
+        # 对二值图像进行形态学操作，以去除小的噪点  
+        kernel2 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (structuring_element_2, structuring_element_2))  
+        closed2 = cv2.morphologyEx(thresh2, cv2.MORPH_CLOSE, kernel2)
+        closed2 = closed2.astype(img.dtype)
+        result2 = cv2.bitwise_and(img, img, mask=closed2)
+        result2[closed2==0] = (255,255,255)
+        line_color2 = ImageColor.getcolor(color, "RGB")
+        result2[closed2!=0] = (line_color2)
+        close02 = Image.fromarray(result2).convert('RGB')
+
+        # 颜色空间转换  
+        image2 = Image.fromarray(img)
+        enhancer2 = ImageEnhance.Color(image=image2)
+        # 增强颜色  
+        img2 = enhancer2.enhance(enhance).convert('RGB')
+        union_img2 = np.asarray(Image.blend(close02, img2, blend))
+
+        #####################################
+        #   简单勾边-彩色勾边                #
+        #####################################
+        closed3 = closed1.astype(img.dtype)
+        result3 = cv2.bitwise_and(img, img, mask=closed3)
+        result3[closed3==0] = (255,255,255)
+        close03 = Image.fromarray(result3).convert('RGB')
+
+        # 颜色空间转换  
+        image3 = Image.fromarray(img)
+        enhancer3 = ImageEnhance.Color(image=image3)
+        # 增强颜色  
+        img3 = enhancer3.enhance(enhance).convert('RGB')
+        union_img3 = np.asarray(Image.blend(close03, img3, blend))
+
+        return result0,union_img0,result1,union_img1,result2,union_img2,result3,union_img3
+    
+    section_btn.click(turn_arguments,inputs=[img_input,enhance,blend,color],
+                                    outputs = [closed_output0,img_param_output0,closed_output1,img_param_output1,closed_output2,img_param_output2,closed_output3,img_param_output3])
+
+interface.launch(show_api=False)