367 lines
14 KiB
Plaintext
367 lines
14 KiB
Plaintext
import time
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from PyQt5.QtGui import QPixmap, QImage
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from PyQt5.QtWidgets import QWidget, QFileDialog,QMainWindow, QLabel, QSizePolicy, QApplication, QAction, QHBoxLayout
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from PyQt5.QtCore import *
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from PyQt5 import QtCore, QtGui, QtWidgets
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import sys
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import traceback
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import ctypes as C
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import numpy as np
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import cv2
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import os
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from test_0415_ui import Ui_MainWindow
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from pyueye import ueye
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import numpy as np
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import cv2
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# 串口通信
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import serial
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import time
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import threading
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import torch
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from Class.Camera import Camera_class
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from Class.Motor import Motor_class
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from Class.Pan import Pan_class
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from Class.Yolo import yolo_class
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class img_yolo(QtCore.QThread):
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sinOut = pyqtSignal(str) # 聲明一個帶字串參數的信號
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def __init__(self,yolo_model,parent=None):
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super().__init__(parent)
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self.yolo = yolo_model
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def normalize_image(self, img):
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if img is not None:
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normalized_image = cv2.normalize(img, None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX)
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return normalized_image
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return None
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def run(self):
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print('run')
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self.img_list = []
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self.filename_list = []
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for filename in os.listdir(r"./image_folder"):
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if filename.endswith('.jpg') or filename.endswith('.bmp'):
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img = cv2.imread('image_folder' + "/" + filename)
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self.img_list.append(img)
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self.filename_list.append(filename)
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# self.img_test(img, filename)
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print(f'img讀取完成')
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for i in range(0,len(self.filename_list)):
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img = self.img_list[i]
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file_name = self.filename_list[i]
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t1 = time.time()
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results = self.yolo.YoloDetect(img)
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t2 = time.time()
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print(f'推論時間 = {t2 - t1}')
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# print(results)
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print(f'{file_name}')
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# 檢查是否有預測結果
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if results.pred[0] is None or len(results.pred[0]) == 0:
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continue
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# 獲取預測的信心值和類別
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conf = results.pred[0][0, 4] # 取最高信心度
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predicted_classes = results.names[int(results.pred[0][0, -1])]
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# 獲取 bounding box 的座標
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box = results.pred[0][0, :4].cpu().numpy().astype(int)
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# 提取ROI (Region of Interest)
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roi = img[box[1]:box[3], box[0]:box[2]]
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# 正規化ROI圖像
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normalized_roi = self.normalize_image(roi)
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# 將正規化的ROI轉換為灰度圖像
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gray_roi = cv2.cvtColor(normalized_roi, cv2.COLOR_BGR2GRAY)
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# 創建CLAHE對象
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clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(3, 3))
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# 對灰度圖像應用CLAHE
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clahe_image = clahe.apply(gray_roi)
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# # 二值化處理
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# _, binary_image = cv2.threshold(sharpened_image, 30, 255, cv2.THRESH_BINARY)
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# 形態學膨脹
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# kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (8, 8)) #用這個
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# dilated_image = cv2.dilate(binary_image, kernel, iterations=2)
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# closed_image = cv2.morphologyEx(binary_image, cv2.MORPH_CLOSE, kernel) #用這個
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# opened_image = cv2.morphologyEx(binary_image, cv2.MORPH_OPEN, kernel)
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# 將圖像轉換為灰度格式
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# gray_image = cv2.cvtColor(closed_image, cv2.COLOR_BGR2GRAY)
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# 找到輪廓
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# contours, _ = cv2.findContours(closed_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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# 取得原圖檔名稱
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base_name, ext = os.path.splitext(file_name)
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# 另存繪製後的ROI，檔名加上 '_2'
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new_file_name = f'{base_name}_2{ext}'
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print(f'Saving {new_file_name}')
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cv2.imwrite(f'img_2/{new_file_name}', clahe_image)
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# # 獲取最外圍輪廓
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# if len(contours) > 0:
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# # 擬合最外圍輪廓為橢圓
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# ellipse = cv2.fitEllipse(contours[0])
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#
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# # 在原圖上繪製橢圓
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# contour_image = roi.copy()
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# cv2.ellipse(contour_image, ellipse, (0, 255, 0), 1) # 使用綠色線條繪製橢圓
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#
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# # 取得原圖檔名稱
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# base_name, ext = os.path.splitext(file_name)
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#
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# # 另存繪製後的ROI，檔名加上 '_2'
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# new_file_name = f'{base_name}_2{ext}'
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# print(f'Saving {new_file_name}')
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# cv2.imwrite(f'img_2/{new_file_name}', sharpened_image)
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#
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# # 如果信心值超過0.6，則保存圖片
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# if conf > 0.6:
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# resized_frame_high_conf = cv2.resize(results.render()[0], (1280, 1280))
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# print(f'Saving {file_name}')
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# cv2.imwrite(f'img_2/{file_name}', resized_frame_high_conf)
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# else:
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# print("No contours found.")
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# # 如果信心值超過0.6，則保存圖片
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# if conf > 0.6:
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# # 獲取 bounding box 的座標
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# box = results.pred[0][0, :4].cpu().numpy().astype(int)
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#
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# # 保留原本的推論結果，另存原圖像
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# print(f'Saving {file_name}')
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# cv2.imwrite(f'img_2/{file_name}', img)
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#
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# # 繪製 bounding box 在原圖上
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# cv2.rectangle(img, (box[0], box[1]), (box[2], box[3]), (0, 255, 0), 2) # 使用綠色線條繪製
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#
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# # 調整圖像大小
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# resized_frame = cv2.resize(img, (1280, 1280))
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#
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# # 取得原圖檔名稱
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# base_name, ext = os.path.splitext(file_name)
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#
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# # 另存繪製後的圖像，檔名加上 '_2'
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# new_file_name = f'{base_name}_2{ext}'
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# print(f'Saving {new_file_name}')
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# cv2.imwrite(f'img_2/{new_file_name}', resized_frame)
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#
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# else:
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# # 保留原本的推論結果，另存原圖像
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# resized_frame = cv2.resize(results.render()[0], (1280, 1280))
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# print(f'Saving {file_name}')
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# cv2.imwrite(f'img_2/{file_name}', resized_frame)
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def img_to_view(img):#原圖
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img= cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # QT顏色顯示轉換
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Ny, Nx, _ = img.shape
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img = QtGui.QImage(img.data, Nx, Ny,Nx*3, QtGui.QImage.Format_RGB888) #須改格式
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return img
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class MainWindow(QMainWindow,Ui_MainWindow):
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def __init__(self, parent=None): #按鍵設定
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super(MainWindow, self).__init__(parent)
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self.setupUi(self)
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# self.view_1.setScaledContents(True)
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# self.view_2.setScaledContents(True)
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# self.view_3.setScaledContents(True)
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# self.view_4.setScaledContents(True)
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self.bt_1.clicked.connect(self.bt_1_clicked)
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# self.bt_2.clicked.connect(self.bt_2_clicked) # 連接按鈕到新的功能
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self.bt_3.clicked.connect(self.bt_3_clicked)
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self.cam = Camera_class()
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self.cam.start()
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self.cam.rawdata.connect(self.show_img)
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self.run_flag = True
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self.com5 = Motor_class()
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self.com5.start()
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self.com5.sinOut.connect(self.com5_str)
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self.com4 = Pan_class()
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self.com4.start()
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self.com4.sinOut.connect(self.com4_str)
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self.count = 0
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self.img_count=0
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self.yolo = yolo_class('weight/best.pt')
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#self.yolo.start()
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# self.img_test_2()
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def show_img(self,img):
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self.img = img
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h,w,_ = self.img.shape
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img= img_to_view(img)
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self.view_1.setPixmap(QtGui.QPixmap.fromImage(img))
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def bt_1_clicked(self):
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self.time_2= time.time()
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self.com5.start_run()
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self.run_flag = True
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self.time_1 = time.time()
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def com5_str(self,str_1):
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if str_1 =='move':
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self.count = self.count+1
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if self.count< 6:
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print(self.count)
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if str_1 == 'move':
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self.img_count = 0
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self.com4.move()
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if self.count == 5:
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self.run_flag = False
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if str_1 =='2':
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# self.img_test()
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#self.time_1 = time.time()
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#print(f's={self.time_1-self.time_2}')
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# print('save_img')
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self.img_count = self.img_count+1
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cv2.imwrite(f'image_folder\\{str(self.count)}_{self.img_count}.jpg',self.img)
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#self.time_2= time.time()
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def com4_str(self,str_1):
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if str_1 =='move_end' and self.run_flag == True:
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self.com5.start_run()
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if str_1 == 'Motor emergency stop 010':
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print('010')
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self.run_flag = False
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self.count = 0
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time_2 = time.time()
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print(f'all_time - {time_2 - self.time_1}')
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self.bt_3_clicked()
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# def bt_3_clicked(self):
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# options = QFileDialog.Options()
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# options |= QFileDialog.ReadOnly
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# filePath, _ = QFileDialog.getOpenFileName(self, "選擇圖片", "", "圖片文件 (*.bmp *.jpg *.png);;所有文件 (*)",
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# options=options)
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# # print(filePath)
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# img = cv2.imread(filePath)
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# t1 = time.time()
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# results = self.yolo.YoloDetect(img)
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# t2=time.time()
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# # print(f'推論時間 = {t2-t1}')
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# # print(results)
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# # 檢查是否有預測結果
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# if results.pred[0] is None or len(results.pred[0]) == 0:
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# # self.label1.setText("沒有瑕疵物件")
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# return
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#
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# # 獲取預測的類別
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# predicted_classes = results.names[int(results.pred[0][0, -1])] # 取最高信心度的類別
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# resized_frame = cv2.resize(results.render()[0], (1920, 1280))
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# # print(resized_frame)
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# img = img_to_view(resized_frame)
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# self.view_2.setPixmap(QtGui.QPixmap.fromImage(img))
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#
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# def img_test(self,img,file_name):
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# t1 = time.time()
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# results = self.yolo.YoloDetect(img)
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# t2 = time.time()
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# print(f'推論時間 = {t2 - t1}')
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# # print(results)
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# print(f'{file_name}')
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#
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# # 檢查是否有預測結果
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# if results.pred[0] is None or len(results.pred[0]) == 0:
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# # self.label1.setText("沒有瑕疵物件")
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# return
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#
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# # 獲取預測的類別
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# predicted_classes = results.names[int(results.pred[0][0, -1])] # 取最高信心度的類別
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# resized_frame = cv2.resize(results.render()[0], (1920, 1280))
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# # print(resized_frame)
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# # img = img_to_view(resized_frame)
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# # self.view_2.setPixmap(QtGui.QPixmap.fromImage(img))
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# print(f'{file_name}')
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# cv2.imwrite(f'img_2/{file_name}', resized_frame)
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def img_test_2(self):
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img_list=[]
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for filename in os.listdir(r"./image_folder"):
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if filename.endswith('.jpg'):
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img = cv2.imread('image_folder' + "/" + filename)
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img_list.append(img)
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# self.img_test(img, filename)
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print(f'img讀取完成')
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run_list=[]
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for img in img_list:
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run_list.append(img_yolo( self.yolo,img))
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for i in range (0,len(run_list)):
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run_list[i].start()
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def bt_3_clicked(self):
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self.T1 = img_yolo(self.yolo)
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self.T1.finished.connect(self.on_yolo_finished) # Connect to finished signal
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self.T1.start()
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def on_yolo_finished(self):
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img_confidence = {} # Dictionary to store confidence values for images
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# Load images from img_3 folder
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for filename in os.listdir(r"./img_2"):
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if filename.endswith('.jpg'):
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img_path = os.path.join("./img_2", filename)
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img = cv2.imread(img_path)
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img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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h, w, _ = img_rgb.shape
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qimg = QtGui.QImage(img_rgb.data, w, h, w * 3, QtGui.QImage.Format_RGB888)
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# Perform YOLO detection and get confidence value
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results = self.yolo.YoloDetect(img)
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if results.pred[0] is not None and len(results.pred[0]) > 0:
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conf = results.pred[0][0, 4] # Confidence value
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else:
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conf = 0
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img_confidence[filename] = conf
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# Sort images by confidence value in descending order
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sorted_imgs = sorted(img_confidence.items(), key=lambda x: x[1], reverse=True)
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count = 0 # Count the number of displayed images
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# Display images in view_2, view_3, and view_4 based on confidence value
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for filename, _ in sorted_imgs:
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img_path = os.path.join("./img_2", filename)
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img = cv2.imread(img_path)
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img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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h, w, _ = img_rgb.shape
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qimg = QtGui.QImage(img_rgb.data, w, h, w * 3, QtGui.QImage.Format_RGB888)
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if not self.view_2.pixmap():
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self.view_2.setPixmap(QtGui.QPixmap.fromImage(qimg))
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count += 1
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elif not self.view_3.pixmap():
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self.view_3.setPixmap(QtGui.QPixmap.fromImage(qimg))
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count += 1
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elif not self.view_4.pixmap():
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self.view_4.setPixmap(QtGui.QPixmap.fromImage(qimg))
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count += 1
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# Break the loop if displayed 3 images
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if count == 3:
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break
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if __name__ == "__main__":
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app = QApplication(sys.argv)
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window = MainWindow()
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window.show()
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sys.exit(app.exec_()) |