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+#===========================================================================#
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+# #
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+# Copyright (C) 2006 - 2018 #
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+# IDS Imaging Development Systems GmbH #
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+# Dimbacher Str. 6-8 #
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+# D-74182 Obersulm, Germany #
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+# #
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+# The information in this document is subject to change without notice #
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+# and should not be construed as a commitment by IDS Imaging Development #
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+# Systems GmbH. IDS Imaging Development Systems GmbH does not assume any #
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+# responsibility for any errors that may appear in this document. #
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+# #
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+# This document, or source code, is provided solely as an example #
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+# of how to utilize IDS software libraries in a sample application. #
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+# IDS Imaging Development Systems GmbH does not assume any responsibility #
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+# for the use or reliability of any portion of this document or the #
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+# described software. #
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+# #
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+# General permission to copy or modify, but not for profit, is hereby #
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+# granted, provided that the above copyright notice is included and #
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+# reference made to the fact that reproduction privileges were granted #
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+# by IDS Imaging Development Systems GmbH. #
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+# #
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+# IDS Imaging Development Systems GmbH cannot assume any responsibility #
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+# for the use or misuse of any portion of this software for other than #
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+# its intended diagnostic purpose in calibrating and testing IDS #
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+# manufactured cameras and software. #
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+# #
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+#===========================================================================#
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+
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+# Developer Note: I tried to let it as simple as possible.
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+# Therefore there are no functions asking for the newest driver software or freeing memory beforehand, etc.
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+# The sole purpose of this program is to show one of the simplest ways to interact with an IDS camera via the uEye API.
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+# (XS cameras are not supported)
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+#---------------------------------------------------------------------------------------------------------------------------------------
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+#coding=utf-8
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+#Libraries
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+from ctypes import *
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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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+import sys
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+import ctypes
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+import struct
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+import threading
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+import time
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+import datetime
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+import os
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+import pymysql
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+import tensorflow as tf
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+import requests as req
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+import sys
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+import mysql.connector
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+from mysql.connector import Error
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+from urllib import parse
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+from PIL import Image
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+
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+#---------------------------------------------------------------------------------------------------------------------------------------
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+
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+#Variables
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+hCam = ueye.HIDS(0) #0: first available camera; 1-254: The camera with the specified camera ID
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+sInfo = ueye.SENSORINFO()
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+cInfo = ueye.CAMINFO()
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+pcImageMemory = ueye.c_mem_p()
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+MemID = ueye.int()
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+rectAOI = ueye.IS_RECT()
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+pitch = ueye.INT()
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+nBitsPerPixel = ueye.INT(24) #24: bits per pixel for color mode; take 8 bits per pixel for monochrome
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+channels = 3 #3: channels for color mode(RGB); take 1 channel for monochrome
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+m_nColorMode = ueye.INT() # Y8/RGB16/RGB24/REG32
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+bytes_per_pixel = int(nBitsPerPixel / 8)
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+formatInfo=ueye.IMAGE_FORMAT_INFO()
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+now=ctypes.c_uint()
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+m_pcSeqImgMem=[]
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+m_lSeqMemId=[]
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+nNum=ueye.INT()
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+pcMem=ueye.c_mem_p()
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+pcMemLast=ueye.c_mem_p()
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+ImageFileParams=ueye.IMAGE_FILE_PARAMS()
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+
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+ueye.is_FreeImageMem(hCam, pcImageMemory, MemID)
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+conn = pymysql.connect(host="127.0.0.1", port=3306, user='root', passwd='g53743001',
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+ db='coffee_detection',
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+ charset='utf8')
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+
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+#---------------------------------------------------------------------------------------------------------------------------------------
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+print("START")
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+#print()
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+
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+# Starts the driver and establishes the connection to the camera
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+nRet = ueye.is_InitCamera(hCam, None)
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+if nRet != ueye.IS_SUCCESS:
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+ ueye.is_InitCamera(hCam, None)
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+ #print("is_InitCamera ERROR")
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+
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+ueye.is_ParameterSet(hCam,ueye.IS_PARAMETERSET_CMD_LOAD_EEPROM,None,0)
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+
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+# Reads out the data hard-coded in the non-volatile camera memory and writes it to the data structure that cInfo points to
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+nRet = ueye.is_GetCameraInfo(hCam, cInfo)
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+if nRet != ueye.IS_SUCCESS:
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+ print("is_GetCameraInfo ERROR")
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+
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+# You can query additional information about the sensor type used in the camera
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+nRet = ueye.is_GetSensorInfo(hCam, sInfo)
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+if nRet != ueye.IS_SUCCESS:
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+ print("is_GetSensorInfo ERROR")
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+
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+# Set display mode to DIB
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+nRet = ueye.is_SetDisplayMode(hCam, ueye.IS_SET_DM_DIB)
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+
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+# Set the right color mode
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+if int.from_bytes(sInfo.nColorMode.value, byteorder='big') == ueye.IS_COLORMODE_BAYER:
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+ # setup the color depth to the current windows setting
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+ ueye.is_GetColorDepth(hCam, nBitsPerPixel, m_nColorMode)
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+ bytes_per_pixel = int(nBitsPerPixel / 8)
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+ #print("IS_COLORMODE_BAYER: ", )
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+ #print("\tm_nColorMode: \t\t", m_nColorMode)
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+ #print("\tnBitsPerPixel: \t\t", nBitsPerPixel)
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+ #print("\tbytes_per_pixel: \t\t", bytes_per_pixel)
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+ #print()
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+
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+elif int.from_bytes(sInfo.nColorMode.value, byteorder='big') == ueye.IS_COLORMODE_CBYCRY:
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+ # for color camera models use RGB32 mode
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+ m_nColorMode = ueye.IS_CM_BGR8_PACKED
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+ nBitsPerPixel = ueye.INT(24)
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+ bytes_per_pixel = int(nBitsPerPixel / 8)
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+ #print("IS_COLORMODE_CBYCRY: ", )
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+ #print("\tm_nColorMode: \t\t", m_nColorMode)
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+ #print("\tnBitsPerPixel: \t\t", nBitsPerPixel)
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+ #print("\tbytes_per_pixel: \t\t", bytes_per_pixel)
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+ #print()
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+
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+elif int.from_bytes(sInfo.nColorMode.value, byteorder='big') == ueye.IS_COLORMODE_MONOCHROME:
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+ # for color camera models use RGB32 mode
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+ m_nColorMode = ueye.IS_CM_MONO8
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+ nBitsPerPixel = ueye.INT(8)
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+ bytes_per_pixel = int(nBitsPerPixel / 8)
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+ #print("IS_COLORMODE_MONOCHROME: ", )
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+ #print("\tm_nColorMode: \t\t", m_nColorMode)
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+ #print("\tnBitsPerPixel: \t\t", nBitsPerPixel)
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+ #print("\tbytes_per_pixel: \t\t", bytes_per_pixel)
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+ #print()
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+
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+else:
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+ # for monochrome camera models use Y8 mode
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+ m_nColorMode = ueye.IS_CM_MONO8
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+ nBitsPerPixel = ueye.INT(8)
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+ bytes_per_pixel = int(nBitsPerPixel / 8)
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+ print("else")
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+
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+
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+ueye.is_SetColorMode(hCam,ueye.IS_CM_BGRA8_PACKED)
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+nBitsPerPixel=ueye.INT(32)
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+# Can be used to set the size and position of an "area of interest"(AOI) within an image
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+nRet = ueye.is_AOI(hCam, ueye.IS_AOI_IMAGE_GET_AOI, rectAOI, ueye.sizeof(rectAOI))
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+if nRet != ueye.IS_SUCCESS:
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+ print("is_AOI ERROR")
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+
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+width =rectAOI.s32Width
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+height =rectAOI.s32Height
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+
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+# Prints out some information about the camera and the sensor
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+#print("Camera model:\t\t", sInfo.strSensorName.decode('utf-8'))
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+#print("Camera serial no.:\t", cInfo.SerNo.decode('utf-8'))
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+#print("Maximum image width:\t", width)
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+#print("Maximum image height:\t", height)
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+#print()
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+
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+#---------------------------------------------------------------------------------------------------------------------------------------
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+
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+for i in range(1,5):
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+# Allocates an image memory for an image having its dimensions defined by width and height and its color depth defined by nBitsPerPixel
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+ nRet = ueye.is_AllocImageMem(hCam, width, height, nBitsPerPixel, pcImageMemory, MemID)
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+ # m_pcSeqImgMem.append(pcImageMemory)
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+ # m_lSeqMemId.append(MemID)
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+ ueye.is_AddToSequence(hCam,pcImageMemory,MemID)
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+
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+#path=ueye.c_wchar_p()
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+#print(type(path))
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+#nRet = ueye.is_ParameterSet(hCam, ueye.IS_PARAMETERSET_CMD_LOAD_FILE, path, 0)
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+
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+# Activates the camera's live video mode (free run mode)
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+nRet = ueye.is_CaptureVideo(hCam, ueye.IS_DONT_WAIT)
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+if nRet != ueye.IS_SUCCESS:
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+ print("is_CaptureVideo ERROR")
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+
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+nRet = ueye.is_InquireImageMem(hCam, pcImageMemory, MemID, width, height, nBitsPerPixel, pitch)
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+if nRet != ueye.IS_SUCCESS:
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+ print("is_InquireImageMem ERROR")
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+else:
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+ print("Press q to leave the programm")
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+
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+Count=0
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+
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+
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+def cut_rectangle():
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+ image_size = 150
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+ # img = cv2.imread("D:\\fatwolf\\company_files\\opencv\\2021-05-05-11_13_47.png")
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+ # img = cv2.imread("D:\\fatwolf\\company_files\\opencv\\2.png")
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+ #img = cv2.imread("C:\\Users\\User\\Desktop\\tfcoffebean\\test\\1.png")
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+ # img = cv2.imread("C:\\Users\\User\\Desktop\\IDS\\p\\12033_248.png")
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+ # img_size = img.shape
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+ # print(img_size)
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+ img = cv2.imread('D:\\fatwolf\\company_files\\python_code\\test_code\\test_pic\\12033_267.png')
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+
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+ # img = cv2.resize(img1,(968,548))
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+ point_color = (0, 0, 255)
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+ command1 = "SELECT Name,X, X1 ,Y ,Y1 FROM `cut` WHERE Name LIKE 'roi1'"
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+ l = conn.cursor()
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+ l.execute(command1)
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+ conn.commit()
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+ r1 = l.fetchone()
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+
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+ # print(r1[0])
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+ count = 1
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+ def roi1():
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+ # x = r1[1]
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+ # x1 = r1[2]
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+ # y = r1[3]
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+ # y1 = r1[4]
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+ #x = 743
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+ #x1 = 892
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+ #y = 17
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+ #y1 = 164
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+ x = 1257
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+ x1 = 1355
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+ y = 185
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+ y1 = 278
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+ i = 1
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+ i1 = 1
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+ i2 = 1
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+ i3 = 1
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+ i4 = 1
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+ i5 = 1
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+ i6 = 1
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+ number = count
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+
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+ for i in range(6):
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+ roi = img[y:y1, x:x1]
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+ cv2.rectangle(img, (x, y), (x1, y1), point_color, 1)
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+ roi = cv2.resize(roi, (image_size, image_size), 0, 0, cv2.INTER_LINEAR)
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+ cv2.imwrite('D:\\fatwolf\\company_files\\python_code\\test_code\\test_pic\\pic' + '00_' + str(number) + '.png', roi)
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+ number = number + 1
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+ y = y + 150
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+ y1 = y1 + 150
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+ x = x + 145
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+ x1 = x1 + 145
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+ y = 1355
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+ y1 = 278
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+ '''
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+ for i in range(6):
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+ roi = img[y:y1, x:x1]
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+ cv2.rectangle(img, (x, y), (x1, y1), point_color, 1)
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+ roi = cv2.resize(roi, (image_size, image_size), 0, 0, cv2.INTER_LINEAR)
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+ cv2.imwrite('D:\\fatwolf\\company_files\\paper_coffee\\pic\\' + '00_' + str(number) + '.png', roi)
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+ number = number + 1
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+ x = x + 150
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+ x1 = x1 + 150
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+ y = y + 145
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+ y1 = y1 + 145
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+ x = 1257
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+ x1 = 1355
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+ '''
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+ '''
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+ for i1 in range(6):
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+ roi = img[y:y1, x:x1]
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+ cv2.rectangle(img, (x, y), (x1, y1), point_color, 1)
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+ roi = cv2.resize(roi, (image_size, image_size), 0, 0, cv2.INTER_LINEAR)
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+ cv2.imwrite('D:\\fatwolf\\company_files\\paper_coffee\\pic\\' + '00_' + str(number) + '.png', roi)
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+ number = number + 1
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+ x = x + 150
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+ x1 = x1 + 150
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+ y = y + 145
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+ y1 = y1 + 145
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+ x = 743
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+ x1 = 892
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+
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+ for i2 in range(6):
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+ roi = img[y:y1, x:x1]
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+ cv2.rectangle(img, (x, y), (x1, y1), point_color, 1)
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+ roi = cv2.resize(roi, (image_size, image_size), 0, 0, cv2.INTER_LINEAR)
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+ cv2.imwrite('D:\\fatwolf\\company_files\\paper_coffee\\pic\\' + '00_' + str(number) + '.png', roi)
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+ number = number + 1
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+ x = x + 150
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+ x1 = x1 + 150
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+ y = y + 145
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+ y1 = y1 + 145
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+ x = 743
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+ x1 = 892
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+
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+ for i3 in range(6):
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+ roi = img[y:y1, x:x1]
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+ cv2.rectangle(img, (x, y), (x1, y1), point_color, 1)
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+ roi = cv2.resize(roi, (image_size, image_size), 0, 0, cv2.INTER_LINEAR)
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+ cv2.imwrite('D:\\fatwolf\\company_files\\paper_coffee\\pic\\' + '00_' + str(number) + '.png', roi)
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+ number = number + 1
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+ x = x + 150
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+ x1 = x1 + 150
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+ y = y + 145
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+ y1 = y1 + 145
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+ x = 743
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+ x1 = 892
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+
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+ for i4 in range(6):
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+ roi = img[y:y1, x:x1]
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+ cv2.rectangle(img, (x, y), (x1, y1), point_color, 1)
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+ roi = cv2.resize(roi, (image_size, image_size), 0, 0, cv2.INTER_LINEAR)
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+ cv2.imwrite('D:\\fatwolf\\company_files\\paper_coffee\\pic\\' + '00_' + str(number) + '.png', roi)
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+ number = number + 1
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+ x = x + 150
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+ x1 = x1 + 150
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+ y = y + 145
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+ y1 = y1 + 145
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+ x = 743
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+ x1 = 892
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+
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+ for i5 in range(6):
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+ roi = img[y:y1, x:x1]
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+ cv2.rectangle(img, (x, y), (x1, y1), point_color, 1)
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+ roi = cv2.resize(roi, (image_size, image_size), 0, 0, cv2.INTER_LINEAR)
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+ cv2.imwrite('D:\\fatwolf\\company_files\\paper_coffee\\pic\\' + '00_' + str(number) + '.png', roi)
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+ number = number + 1
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+ x = x + 150
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+ x1 = x1 + 150
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+ y = y + 145
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+ y1 = y1 + 145
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+ x = 743
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+ x1 = 892
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+
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+ for i6 in range(6):
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+ roi = img[y:y1, x:x1]
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+ cv2.rectangle(img, (x, y), (x1, y1), point_color, 1)
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+ roi = cv2.resize(roi, (image_size, image_size), 0, 0, cv2.INTER_LINEAR)
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+ cv2.imwrite('D:\\fatwolf\\company_files\\paper_coffee\\pic\\' + '00_' + str(number) + '.png', roi)
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+ number = number + 1
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+ x = x + 150
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+ x1 = x1 + 150
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+ y = y + 145
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+ y1 = y1 + 145
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+ x = 743
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+ x1 = 892
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+ '''
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+ start = datetime.datetime.now()
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+ roi1()
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+ end = datetime.datetime.now()
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+ print("cut_rectangle Run Time：", end - start)
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+
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+
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+def cnn():
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+ # data file
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+
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+ data_dir = r"D:\\fatwolf\\company_files\\python_code\\test_code\\test_pic\\pic"
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+
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+ print(data_dir)
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+ allName = os.listdir(data_dir)
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+
|
|
|
+ # train or test
|
|
|
+ train = False
|
|
|
+ # model address
|
|
|
+ model_path = "model/image_model"
|
|
|
+ allTestDataName = []
|
|
|
+
|
|
|
+ def read_data(data_dir):
|
|
|
+ datas = []
|
|
|
+ labels = []
|
|
|
+ fpaths = []
|
|
|
+ for filename in os.listdir(data_dir):
|
|
|
+ fpath = os.path.join(data_dir, filename)
|
|
|
+ allTestDataName.append(filename)
|
|
|
+ image = Image.open(fpath)
|
|
|
+ data = np.array(image) / 255.0
|
|
|
+ label = int(filename.split("_")[0])
|
|
|
+ datas.append(data)
|
|
|
+ labels.append(label)
|
|
|
+ # allTestDataName.append(filename)
|
|
|
+
|
|
|
+ datas = np.array(datas)
|
|
|
+ labels = np.array(labels)
|
|
|
+ allTestDataName.sort(key=lambda x: int(x[:-4]))
|
|
|
+ # print(allTestDataName)
|
|
|
+
|
|
|
+ # print("shape of datas: {}\tshape of labels: {}".format(datas.shape, labels.shape))
|
|
|
+ return allTestDataName, datas, labels
|
|
|
+
|
|
|
+ allTestDataName, datas, labels = read_data(data_dir)
|
|
|
+ # num_classes = len(set(labels))
|
|
|
+ num_classes = 4
|
|
|
+
|
|
|
+ datas_placeholder = tf.compat.v1.placeholder(tf.float32, [None, 150, 150, 3])
|
|
|
+
|
|
|
+ labels_placeholder = tf.compat.v1.placeholder(tf.int32, [None])
|
|
|
+
|
|
|
+ dropout_placeholdr = tf.compat.v1.placeholder(tf.float32)
|
|
|
+
|
|
|
+ conv0 = tf.layers.conv2d(datas_placeholder, 20, 5, activation=tf.nn.relu)
|
|
|
+ pool0 = tf.layers.max_pooling2d(conv0, [2, 2], [2, 2])
|
|
|
+
|
|
|
+ conv1 = tf.layers.conv2d(pool0, 40, 4, activation=tf.nn.relu)
|
|
|
+ pool1 = tf.layers.max_pooling2d(conv1, [2, 2], [2, 2])
|
|
|
+
|
|
|
+ flatten = tf.layers.flatten(pool1)
|
|
|
+
|
|
|
+ fc = tf.layers.dense(flatten, 400, activation=tf.nn.relu)
|
|
|
+
|
|
|
+ dropout_fc = tf.layers.dropout(fc, dropout_placeholdr)
|
|
|
+
|
|
|
+ logits = tf.layers.dense(dropout_fc, num_classes)
|
|
|
+
|
|
|
+ predicted_labels = tf.arg_max(logits, 1)
|
|
|
+
|
|
|
+ losses = tf.nn.softmax_cross_entropy_with_logits(
|
|
|
+ labels=tf.one_hot(labels_placeholder, num_classes),
|
|
|
+ logits=logits
|
|
|
+ )
|
|
|
+ mean_loss = tf.reduce_mean(losses)
|
|
|
+
|
|
|
+ optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=1e-2).minimize(losses)
|
|
|
+
|
|
|
+ saver = tf.compat.v1.train.Saver()
|
|
|
+
|
|
|
+ with tf.compat.v1.Session() as sess:
|
|
|
+
|
|
|
+ if train:
|
|
|
+ print("train mode")
|
|
|
+
|
|
|
+ sess.run(tf.global_variables_initializer())
|
|
|
+
|
|
|
+ train_feed_dict = {
|
|
|
+ datas_placeholder: datas,
|
|
|
+ labels_placeholder: labels,
|
|
|
+ dropout_placeholdr: 0.25
|
|
|
+ }
|
|
|
+ for step in range(500):
|
|
|
+ _, mean_loss_val = sess.run([optimizer, mean_loss], feed_dict=train_feed_dict)
|
|
|
+
|
|
|
+ if step % 50 == 0:
|
|
|
+ print("step = {}\tmean loss = {}".format(step, mean_loss_val))
|
|
|
+ saver.save(sess, model_path)
|
|
|
+ print("train done save model{}".format(model_path))
|
|
|
+ else:
|
|
|
+ # print("reloading model")
|
|
|
+ saver.restore(sess, model_path)
|
|
|
+ # print("{}reload model".format(model_path))
|
|
|
+
|
|
|
+ label_name_dict = {
|
|
|
+ 0: "Brokenbeans",
|
|
|
+ 1: "Peaberry",
|
|
|
+ 2: "shellbean",
|
|
|
+ 3: "Worms"
|
|
|
+ }
|
|
|
+ test_feed_dict = {
|
|
|
+ datas_placeholder: datas,
|
|
|
+ labels_placeholder: labels,
|
|
|
+ dropout_placeholdr: 0
|
|
|
+ }
|
|
|
+ predicted_labels_val = sess.run(predicted_labels, feed_dict=test_feed_dict)
|
|
|
+
|
|
|
+ for fpath, real_label, predicted_label in zip(allTestDataName, labels, predicted_labels_val):
|
|
|
+ real_label_name = label_name_dict[real_label]
|
|
|
+ # print("訓練前",real_label_name)
|
|
|
+ predicted_label_name = label_name_dict[predicted_label]
|
|
|
+ # print("訓練後",predicted_label_name)
|
|
|
+ # print("{}\t => {}".format(fpath, predicted_label_name))
|
|
|
+ fpath = os.path.basename(fpath)
|
|
|
+
|
|
|
+ print(f"{fpath}\t => {predicted_label_name}")
|
|
|
+ path1 = 'output.txt'
|
|
|
+ f = open(path1, 'a+')
|
|
|
+ f.write(f"{fpath} => {predicted_label_name}""\n")
|
|
|
+ f.close()
|
|
|
+ if predicted_label_name == "shellbean" or "Peaberry" or "Worms":
|
|
|
+ print("觸發噴嘴")
|
|
|
+ else:
|
|
|
+ print('沒有觸發')
|
|
|
+ '''
|
|
|
+ sqlStuff = "INSERT INTO result(picname,identify)""VALUES (%s,%s)"
|
|
|
+ data = [(fpath, predicted_label_name)]
|
|
|
+ a = conn.cursor()
|
|
|
+ a.executemany(sqlStuff, data)
|
|
|
+ conn.commit()
|
|
|
+
|
|
|
+ try:
|
|
|
+ connection = mysql.connector.connect(
|
|
|
+ host='127.0.0.1', # 主機名稱
|
|
|
+ database='coffee_detection', # 資料庫名稱
|
|
|
+ user='root', # 帳號
|
|
|
+ password='g53743001') # 密碼
|
|
|
+ # 新增資料
|
|
|
+ sql = "INSERT INTO result(picname,identify)""VALUES (%s,%s)"
|
|
|
+ new_data = ("test", "test2")
|
|
|
+ cursor = connection.cursor()
|
|
|
+ cursor.execute(sql, new_data)
|
|
|
+
|
|
|
+ # 確認資料有存入資料庫
|
|
|
+ connection.commit()
|
|
|
+
|
|
|
+ except Error as e:
|
|
|
+ print("資料庫連接失敗：", e)
|
|
|
+
|
|
|
+ finally:
|
|
|
+ if (connection.is_connected()):
|
|
|
+ cursor.close()
|
|
|
+ connection.close()
|
|
|
+
|
|
|
+ '''
|
|
|
+ dirListing = os.listdir(data_dir)
|
|
|
+ # print(len(dirListing))
|
|
|
+
|
|
|
+
|
|
|
+
|
|
|
+while(nRet == ueye.IS_SUCCESS):
|
|
|
+ start = datetime.datetime.now()
|
|
|
+ ueye.is_GetActSeqBuf(hCam, nNum, pcMem, pcMemLast)
|
|
|
+ array = ueye.get_data(pcMemLast, width, height, nBitsPerPixel, pitch, copy=False)
|
|
|
+
|
|
|
+ bytes_per_pixel = int(nBitsPerPixel / 8)
|
|
|
+ # ...reshape it in an numpy array...
|
|
|
+ frame = np.reshape(array,(height.value, width.value, bytes_per_pixel))
|
|
|
+ # ...resize the image by a half
|
|
|
+
|
|
|
+ frame = cv2.resize(frame,(0,0),fx=0.5, fy=0.5)
|
|
|
+
|
|
|
+ #---------------------------------------------------------------------------------------------------------------------------------------
|
|
|
+ #Include image data processing here
|
|
|
+ Count = Count + 1
|
|
|
+
|
|
|
+ '''
|
|
|
+ if cv2.waitKey(2) & 0xFF == ord('p'):
|
|
|
+ Count=Count+1
|
|
|
+ ImageFileParams.pwchFileName = ueye.c_wchar_p('C:\\Users\\User\\Desktop\\test\\1.png')
|
|
|
+ #ImageFileParams.pnImageID = &nID;
|
|
|
+ #ImageFileParams.ppcImageMem = &pcMemory;
|
|
|
+ ImageFileParams.nFileType = ueye.IS_IMG_PNG
|
|
|
+ #ImageFileParams.ppcImageMem = pcMemLast;
|
|
|
+ ImageFileParams.nQuality = 75
|
|
|
+ nRet = ueye.is_ImageFile(hCam, ueye.IS_IMAGE_FILE_CMD_SAVE, ImageFileParams, ueye.sizeof(ImageFileParams));
|
|
|
+ print(Count)
|
|
|
+ print(nRet)
|
|
|
+ '''
|
|
|
+#---------------------------------------------------------------------------------------------------------------------------------------
|
|
|
+ #...and finally display it
|
|
|
+ start3 = datetime.datetime.now()
|
|
|
+ #cv2.imshow("SimpleLive_Python_uEye_OpenCV", frame)
|
|
|
+ time.sleep(0.3)
|
|
|
+ ImageFileParams.pwchFileName = ueye.c_wchar_p('C:\\Users\\User\\Desktop\\tfcoffebean\\test\\1.png')
|
|
|
+ ImageFileParams.nFileType = ueye.IS_IMG_PNG
|
|
|
+ ImageFileParams.nQuality = 75
|
|
|
+ nRet = ueye.is_ImageFile(hCam, ueye.IS_IMAGE_FILE_CMD_SAVE, ImageFileParams, ueye.sizeof(ImageFileParams));
|
|
|
+ print(Count)
|
|
|
+ print(nRet)
|
|
|
+ end3 = datetime.datetime.now()
|
|
|
+ print("拍照執行時間：", end3 - start3)
|
|
|
+
|
|
|
+ cut_rectangle()
|
|
|
+ start2 = datetime.datetime.now()
|
|
|
+ cnn()
|
|
|
+ end2 = datetime.datetime.now()
|
|
|
+ print("辨識執行時間：", end2 - start2)
|
|
|
+ end = datetime.datetime.now()
|
|
|
+
|
|
|
+ print("完整執行時間：", end - start)
|
|
|
+ print('-----------------------------------------------------------')
|
|
|
+ tf.reset_default_graph()
|
|
|
+
|
|
|
+ # Press q if you want to end the loop
|
|
|
+ if cv2.waitKey(1) & 0xFF == ord('q'):
|
|
|
+ break
|
|
|
+
|
|
|
+#---------------------------------------------------------------------------------------------------------------------------------------
|
|
|
+
|
|
|
+# Releases an image memory that was allocated using is_AllocImageMem() and removes it from the driver management
|
|
|
+ueye.is_FreeImageMem(hCam, pcImageMemory, MemID)
|
|
|
+
|
|
|
+# Disables the hCam camera handle and releases the data structures and memory areas taken up by the uEye camera
|
|
|
+ueye.is_ExitCamera(hCam)
|
|
|
+
|
|
|
+# Destroys the OpenCv windows
|
|
|
+cv2.destroyAllWindows()
|
|
|
+
|
|
|
+#print()
|
|
|
+print("END")
|
|
|
+
|
