做游戏网站思想步骤,揭阳百度关键词优化,外贸建站哪家,sem推广是什么意思窗口设计 这里的代码放在py文件最前面或者最后面都无所谓 # 创建主窗口
window tk.Tk()
window.title(图像目标检测系统)
window.geometry(1000x650) # 设置窗口大小# 创建背景画布并使用grid布局管理器
canvas_background tk.Canvas(window, width1000, height…窗口设计 这里的代码放在py文件最前面或者最后面都无所谓 # 创建主窗口
window tk.Tk()
window.title(图像目标检测系统)
window.geometry(1000x650) # 设置窗口大小# 创建背景画布并使用grid布局管理器
canvas_background tk.Canvas(window, width1000, height650, bg#e6f2ff)
canvas_background.grid(row0, column0, columnspan2, rowspan4, stickynsew) # 使用grid管理器# 加载背景图像
background_image Image.open(r图标/5.jpg) # 替换为您的背景图像路径
background_image background_image.resize((1000, 650), Image.Resampling.LANCZOS) # 调整图像大小以适应窗口
background_photo ImageTk.PhotoImage(background_image)# 在背景画布上绘制背景图像
canvas_background.create_image(0, 0, anchornw, imagebackground_photo)
canvas_background.image background_photo # 保持对图像的引用# 加载按钮图片
upload_img Image.open(r图标/3.jpg) # 替换为您的上传按钮图片路径
upload_img upload_img.resize((100, 50), Image.Resampling.LANCZOS) # 调整图片大小
upload_img ImageTk.PhotoImage(upload_img)start_detection_img Image.open(r图标/2.jpg) # 替换为您的开始检测按钮图片路径
start_detection_img start_detection_img.resize((100, 50), Image.Resampling.LANCZOS) # 调整图片大小
start_detection_img ImageTk.PhotoImage(start_detection_img)img_intensification_img Image.open(r图标/1.jpg) # 替换为您的图像增强按钮图片路径
img_intensification_img img_intensification_img.resize((100, 50), Image.Resampling.LANCZOS) # 调整图片大小
img_intensification_img ImageTk.PhotoImage(img_intensification_img)# 创建两个画布区域
canvas_left tk.Canvas(window, width224, height224, bg#e6f2ff)
canvas_right tk.Canvas(window, width224, height224, bg#e6f2ff)
# 加载图片
waiting_image Image.open(r图标/8.jpg) # 替换为您的图片路径
detected_image Image.open(r图标/7.jpg) # 替换为您的图片路径
waiting_image waiting_image.resize((200, 50), Image.Resampling.LANCZOS)
detected_image detected_image.resize((200, 50), Image.Resampling.LANCZOS)
waiting_photo ImageTk.PhotoImage(waiting_image)
detected_photo ImageTk.PhotoImage(detected_image)
# 将两个画布区域放置在主窗口中
canvas_left.grid(row1, column0, padx10, pady10, stickynsew)
canvas_right.grid(row1, column1, padx10, pady10, stickynsew)# 创建标签
label_waiting_image tk.Label(window, imagewaiting_photo, font(Arial, 16), bg#e6f2ff)
label_detected_image tk.Label(window, imagedetected_photo, font(Arial, 16), bg#e6f2ff)# 定位标签
label_waiting_image.grid(row0, column0, padx10, pady10, stickynsew)
label_detected_image.grid(row0, column1, padx10, pady10, stickynsew)# 设置标签的图片
label_waiting_image.config(imagewaiting_photo)
label_detected_image.config(imagedetected_photo)# 创建按钮并放置在主窗口上
button_upload tk.Button(window, imageupload_img, commandupload_image, reliefflat, borderwidth0, background#e6f2ff, activebackground#e6f2ff)
button_start_detection tk.Button(window, imagestart_detection_img, commandstart_detection, reliefflat, borderwidth0, background#e6f2ff, activebackground#e6f2ff)
img_intensification tk.Button(window, imageimg_intensification_img, commandimg_inten, reliefflat, borderwidth0, background#e6f2ff, activebackground#e6f2ff)# 将按钮放置在主窗口上
button_upload.place(x155,y495)
button_start_detection.place(x755,y495)
img_intensification.place(x455,y495)# 运行主窗口
window.mainloop()
1.导入需要用到的包
import torch.nn as nn
import tkinter as tk
from tkinter import filedialog, messagebox
from PIL import Image, ImageTk
import torch
from torchvision import transforms, models
from efficientnet_pytorch import EfficientNet
import numpy as np
import cv2
2.加载模型 这里加载了模型需要的可以联系我 class EfficientNetModel(nn.Module):def __init__(self, num_classes10, pretrainedTrue):super(EfficientNetModel, self).__init__()# 加载预训练的EfficientNet模型self.efficientnet EfficientNet.from_name(efficientnet-b3)## if pretrained:# # 加载预训练权重# self.efficientnet.load_state_dict(# torch.load(rD:\python\pytorch\Vehicle identification\save pth\efficientnet-b3-5fb5a3c3.pth))# 获取EfficientNet模型的最后一层全连接层的输入特征数量num_ftrs self.efficientnet._fc.in_features# 将EfficientNet模型的最后一层全连接层替换为一个新的全连接层输出特征数量设置为num_classesself.efficientnet._fc nn.Linear(num_ftrs, num_classes)# forward方法定义了前向传播过程def forward(self, x):return self.efficientnet(x)# Example usage
model EfficientNetModel(num_classes12)# 加载训练好的模型参数
model_path best_EfficientNet_b3_updata1.pth
model.load_state_dict(torch.load(model_path, map_locationtorch.device(cpu)))
model.eval()
3.定义图像转换
# 定义图像转换
transform transforms.Compose([transforms.Resize((224, 224)), # ResNet-50 的输入图像大小transforms.ToTensor(),transforms.Normalize(mean[0.485, 0.456, 0.406], std[0.229, 0.224, 0.225]), # ResNet-50 的标准化
])
4.定义车类标签
classes [皮卡, 敞篷车, 跑车, 掀背两箱车, 小型面包车, SUV, 轿车, 厢式货车, 旅行车, 公共汽车, 消防车, 出租车]
5.定义全局变量
# 初始化全局变量
selected_image_path None
label_text None
right_canvas_image None
6.几个方法
def upload_image():global selected_image_path, label_textfile_path filedialog.askopenfilename()if file_path:selected_image_path file_pathimage Image.open(file_path)original_width, original_height image.size# 计算宽高比aspect_ratio original_width / original_height# 根据画布尺寸和宽高比计算新尺寸canvas_width 500canvas_height 300new_width canvas_widthnew_height int(new_width / aspect_ratio)if new_height canvas_height:new_height canvas_heightnew_width int(new_height * aspect_ratio)# 调整图片大小image image.resize((new_width, new_height), Image.Resampling.LANCZOS)# 居中图片photo ImageTk.PhotoImage(image)canvas_left.create_image((canvas_width - new_width) / 2, (canvas_height - new_height) / 2, anchornw, imagephoto)canvas_left.image photo # Keep a reference!# 创建图片的标签if label_text is None:label_text tk.Label(window, text, font(Arial, 16))label_text.grid(row3, column0, columnspan2, padx10, pady10)def start_detection():global right_canvas_imageif selected_image_path is not None:image Image.open(selected_image_path)input_image transform(image).unsqueeze(0)with torch.no_grad():outputs model(input_image)_, predicted torch.max(outputs, 1)label classes[predicted.item()]probabilities torch.nn.functional.softmax(outputs, dim1)max_probability probabilities[0][predicted].item()label_text.config(textf{label} - {max_probability:.2f})# 显示图片在右侧画布image Image.open(selected_image_path) # 重新打开图片以避免被转换影响original_width, original_height image.sizeaspect_ratio original_width / original_heightcanvas_width 500canvas_height 300new_width canvas_widthnew_height int(new_width / aspect_ratio)if new_height canvas_height:new_height canvas_heightnew_width int(new_height * aspect_ratio)image image.resize((new_width, new_height), Image.Resampling.LANCZOS)photo ImageTk.PhotoImage(image)# 检查是否已经创建了右侧画布的图片if right_canvas_image is None:right_canvas_image canvas_right.create_image((canvas_width - new_width) / 2, (canvas_height - new_height) / 2, anchornw, imagephoto)else:canvas_right.itemconfig(right_canvas_image, imagephoto)canvas_right.image photo # Keep a reference!else:messagebox.showwarning(警告, 请先选择一张图像)# 将标签放置在图片上label_text.grid(row1, column1, padx10, pady10, stickyn)def replaceZeroes(data):min_nonzero min(data[np.nonzero(data)])data[data 0] min_nonzeroreturn datadef MSR(img, scales):weight 1 / 3.0scales_size len(scales)h, w img.shape[:2]log_R np.zeros((h, w), dtypenp.float32)for i in range(scales_size):img replaceZeroes(img)L_blur cv2.GaussianBlur(img, (scales[i], scales[i]), 0)L_blur replaceZeroes(L_blur)dst_Img cv2.log(img / 255.0)dst_Lblur cv2.log(L_blur / 255.0)dst_Ixl cv2.multiply(dst_Img, dst_Lblur)log_R weight * cv2.subtract(dst_Img, dst_Ixl)dst_R cv2.normalize(log_R, None, 0, 255, cv2.NORM_MINMAX)log_uint8 cv2.convertScaleAbs(dst_R)return log_uint8def img_inten():global selected_image_path, right_canvas_imageif selected_image_path is not None:# 读取图像image Image.open(selected_image_path)# 转换为OpenCV格式image cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)# 应用MSR算法scales [15, 101, 301] # 可根据需要调整b_gray, g_gray, r_gray cv2.split(image)b_gray MSR(b_gray, scales)g_gray MSR(g_gray, scales)r_gray MSR(r_gray, scales)enhanced_image cv2.merge([b_gray, g_gray, r_gray])# 转换回PIL图像格式enhanced_image Image.fromarray(cv2.cvtColor(enhanced_image, cv2.COLOR_BGR2RGB))# 调整图像大小以适应模型输入enhanced_image enhanced_image.resize((224, 224), Image.Resampling.LANCZOS)# 转换图像为模型可以接受的格式input_image transform(enhanced_image).unsqueeze(0)# 使用模型进行检测with torch.no_grad():outputs model(input_image)_, predicted torch.max(outputs, 1)label classes[predicted.item()]probabilities torch.nn.functional.softmax(outputs, dim1)max_probability probabilities[0][predicted].item()# 显示增强后的图像和检测结果在右侧画布enhanced_image enhanced_image.resize((500, 300), Image.Resampling.LANCZOS)photo ImageTk.PhotoImage(enhanced_image)if right_canvas_image is None:right_canvas_image canvas_right.create_image((500 - 500) / 2, (300 - 300) / 2, anchornw, imagephoto)else:canvas_right.itemconfig(right_canvas_image, imagephoto)canvas_right.image photo # Keep a reference!# 更新标签文本label_text.config(textf{label} - {max_probability:.2f})else:messagebox.showwarning(警告, 请先选择一张图像)全部代码 这里直接用是用不了的只是给大家提供一个思路模型可以自己训练有需要的可以联系我我把整个代码给你。 import torch.nn as nn
import tkinter as tk
from tkinter import filedialog, messagebox
from PIL import Image, ImageTk
import torch
from torchvision import transforms, models
from efficientnet_pytorch import EfficientNet
import numpy as np
import cv2
class EfficientNetModel(nn.Module):def __init__(self, num_classes10, pretrainedTrue):super(EfficientNetModel, self).__init__()# 加载预训练的EfficientNet模型self.efficientnet EfficientNet.from_name(efficientnet-b3)## if pretrained:# # 加载预训练权重# self.efficientnet.load_state_dict(# torch.load(rD:\python\pytorch\Vehicle identification\save pth\efficientnet-b3-5fb5a3c3.pth))# 获取EfficientNet模型的最后一层全连接层的输入特征数量num_ftrs self.efficientnet._fc.in_features# 将EfficientNet模型的最后一层全连接层替换为一个新的全连接层输出特征数量设置为num_classesself.efficientnet._fc nn.Linear(num_ftrs, num_classes)# forward方法定义了前向传播过程def forward(self, x):return self.efficientnet(x)# Example usage
model EfficientNetModel(num_classes12)# 加载训练好的模型参数
model_path best_EfficientNet_b3_updata1.pth
model.load_state_dict(torch.load(model_path, map_locationtorch.device(cpu)))
model.eval()# 定义图像转换
transform transforms.Compose([transforms.Resize((224, 224)), # ResNet-50 的输入图像大小transforms.ToTensor(),transforms.Normalize(mean[0.485, 0.456, 0.406], std[0.229, 0.224, 0.225]), # ResNet-50 的标准化
])# 类别标签请根据您的实际类别名称设置classes [皮卡, 敞篷车, 跑车, 掀背两箱车, 小型面包车, SUV, 轿车, 厢式货车, 旅行车, 公共汽车, 消防车, 出租车]# 初始化全局变量
selected_image_path None
label_text None
right_canvas_image Nonedef upload_image():global selected_image_path, label_textfile_path filedialog.askopenfilename()if file_path:selected_image_path file_pathimage Image.open(file_path)original_width, original_height image.size# 计算宽高比aspect_ratio original_width / original_height# 根据画布尺寸和宽高比计算新尺寸canvas_width 500canvas_height 300new_width canvas_widthnew_height int(new_width / aspect_ratio)if new_height canvas_height:new_height canvas_heightnew_width int(new_height * aspect_ratio)# 调整图片大小image image.resize((new_width, new_height), Image.Resampling.LANCZOS)# 居中图片photo ImageTk.PhotoImage(image)canvas_left.create_image((canvas_width - new_width) / 2, (canvas_height - new_height) / 2, anchornw, imagephoto)canvas_left.image photo # Keep a reference!# 创建图片的标签if label_text is None:label_text tk.Label(window, text, font(Arial, 16))label_text.grid(row3, column0, columnspan2, padx10, pady10)def start_detection():global right_canvas_imageif selected_image_path is not None:image Image.open(selected_image_path)input_image transform(image).unsqueeze(0)with torch.no_grad():outputs model(input_image)_, predicted torch.max(outputs, 1)label classes[predicted.item()]probabilities torch.nn.functional.softmax(outputs, dim1)max_probability probabilities[0][predicted].item()label_text.config(textf{label} - {max_probability:.2f})# 显示图片在右侧画布image Image.open(selected_image_path) # 重新打开图片以避免被转换影响original_width, original_height image.sizeaspect_ratio original_width / original_heightcanvas_width 500canvas_height 300new_width canvas_widthnew_height int(new_width / aspect_ratio)if new_height canvas_height:new_height canvas_heightnew_width int(new_height * aspect_ratio)image image.resize((new_width, new_height), Image.Resampling.LANCZOS)photo ImageTk.PhotoImage(image)# 检查是否已经创建了右侧画布的图片if right_canvas_image is None:right_canvas_image canvas_right.create_image((canvas_width - new_width) / 2, (canvas_height - new_height) / 2, anchornw, imagephoto)else:canvas_right.itemconfig(right_canvas_image, imagephoto)canvas_right.image photo # Keep a reference!else:messagebox.showwarning(警告, 请先选择一张图像)# 将标签放置在图片上label_text.grid(row1, column1, padx10, pady10, stickyn)def replaceZeroes(data):min_nonzero min(data[np.nonzero(data)])data[data 0] min_nonzeroreturn datadef MSR(img, scales):weight 1 / 3.0scales_size len(scales)h, w img.shape[:2]log_R np.zeros((h, w), dtypenp.float32)for i in range(scales_size):img replaceZeroes(img)L_blur cv2.GaussianBlur(img, (scales[i], scales[i]), 0)L_blur replaceZeroes(L_blur)dst_Img cv2.log(img / 255.0)dst_Lblur cv2.log(L_blur / 255.0)dst_Ixl cv2.multiply(dst_Img, dst_Lblur)log_R weight * cv2.subtract(dst_Img, dst_Ixl)dst_R cv2.normalize(log_R, None, 0, 255, cv2.NORM_MINMAX)log_uint8 cv2.convertScaleAbs(dst_R)return log_uint8def img_inten():global selected_image_path, right_canvas_imageif selected_image_path is not None:# 读取图像image Image.open(selected_image_path)# 转换为OpenCV格式image cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)# 应用MSR算法scales [15, 101, 301] # 可根据需要调整b_gray, g_gray, r_gray cv2.split(image)b_gray MSR(b_gray, scales)g_gray MSR(g_gray, scales)r_gray MSR(r_gray, scales)enhanced_image cv2.merge([b_gray, g_gray, r_gray])# 转换回PIL图像格式enhanced_image Image.fromarray(cv2.cvtColor(enhanced_image, cv2.COLOR_BGR2RGB))# 调整图像大小以适应模型输入enhanced_image enhanced_image.resize((224, 224), Image.Resampling.LANCZOS)# 转换图像为模型可以接受的格式input_image transform(enhanced_image).unsqueeze(0)# 使用模型进行检测with torch.no_grad():outputs model(input_image)_, predicted torch.max(outputs, 1)label classes[predicted.item()]probabilities torch.nn.functional.softmax(outputs, dim1)max_probability probabilities[0][predicted].item()# 显示增强后的图像和检测结果在右侧画布enhanced_image enhanced_image.resize((500, 300), Image.Resampling.LANCZOS)photo ImageTk.PhotoImage(enhanced_image)if right_canvas_image is None:right_canvas_image canvas_right.create_image((500 - 500) / 2, (300 - 300) / 2, anchornw, imagephoto)else:canvas_right.itemconfig(right_canvas_image, imagephoto)canvas_right.image photo # Keep a reference!# 更新标签文本label_text.config(textf{label} - {max_probability:.2f})else:messagebox.showwarning(警告, 请先选择一张图像)# ... [剩余的代码] ...# 创建主窗口
window tk.Tk()
window.title(图像目标检测系统)
window.geometry(1000x650) # 设置窗口大小# 创建背景画布并使用grid布局管理器
canvas_background tk.Canvas(window, width1000, height650, bg#e6f2ff)
canvas_background.grid(row0, column0, columnspan2, rowspan4, stickynsew) # 使用grid管理器# 加载背景图像
background_image Image.open(r图标/5.jpg) # 替换为您的背景图像路径
background_image background_image.resize((1000, 650), Image.Resampling.LANCZOS) # 调整图像大小以适应窗口
background_photo ImageTk.PhotoImage(background_image)# 在背景画布上绘制背景图像
canvas_background.create_image(0, 0, anchornw, imagebackground_photo)
canvas_background.image background_photo # 保持对图像的引用# 加载按钮图片
upload_img Image.open(r图标/3.jpg) # 替换为您的上传按钮图片路径
upload_img upload_img.resize((100, 50), Image.Resampling.LANCZOS) # 调整图片大小
upload_img ImageTk.PhotoImage(upload_img)start_detection_img Image.open(r图标/2.jpg) # 替换为您的开始检测按钮图片路径
start_detection_img start_detection_img.resize((100, 50), Image.Resampling.LANCZOS) # 调整图片大小
start_detection_img ImageTk.PhotoImage(start_detection_img)img_intensification_img Image.open(r图标/1.jpg) # 替换为您的图像增强按钮图片路径
img_intensification_img img_intensification_img.resize((100, 50), Image.Resampling.LANCZOS) # 调整图片大小
img_intensification_img ImageTk.PhotoImage(img_intensification_img)# 创建两个画布区域
canvas_left tk.Canvas(window, width224, height224, bg#e6f2ff)
canvas_right tk.Canvas(window, width224, height224, bg#e6f2ff)
# 加载图片
waiting_image Image.open(r图标/8.jpg) # 替换为您的图片路径
detected_image Image.open(r图标/7.jpg) # 替换为您的图片路径
waiting_image waiting_image.resize((200, 50), Image.Resampling.LANCZOS)
detected_image detected_image.resize((200, 50), Image.Resampling.LANCZOS)
waiting_photo ImageTk.PhotoImage(waiting_image)
detected_photo ImageTk.PhotoImage(detected_image)
# 将两个画布区域放置在主窗口中
canvas_left.grid(row1, column0, padx10, pady10, stickynsew)
canvas_right.grid(row1, column1, padx10, pady10, stickynsew)# 创建标签
label_waiting_image tk.Label(window, imagewaiting_photo, font(Arial, 16), bg#e6f2ff)
label_detected_image tk.Label(window, imagedetected_photo, font(Arial, 16), bg#e6f2ff)# 定位标签
label_waiting_image.grid(row0, column0, padx10, pady10, stickynsew)
label_detected_image.grid(row0, column1, padx10, pady10, stickynsew)# 设置标签的图片
label_waiting_image.config(imagewaiting_photo)
label_detected_image.config(imagedetected_photo)# 创建按钮并放置在主窗口上
button_upload tk.Button(window, imageupload_img, commandupload_image, reliefflat, borderwidth0, background#e6f2ff, activebackground#e6f2ff)
button_start_detection tk.Button(window, imagestart_detection_img, commandstart_detection, reliefflat, borderwidth0, background#e6f2ff, activebackground#e6f2ff)
img_intensification tk.Button(window, imageimg_intensification_img, commandimg_inten, reliefflat, borderwidth0, background#e6f2ff, activebackground#e6f2ff)# 将按钮放置在主窗口上
button_upload.place(x155,y495)
button_start_detection.place(x755,y495)
img_intensification.place(x455,y495)# 运行主窗口
window.mainloop()代码实现效果 图像增强效果 这里图像增强本应该导致准确率增强可能代码实现错误不过不太重要只是给大家提供思路
