{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "4d40a8c1", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "05de5b3e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "fcolor.py as a main\n", "imgi.shape= (3024, 4032, 3)\n", "1 (577, 1353, 166, 93) 13908 15438 0.56\n", "2 (72, 1253, 186, 115) 14214 21390 0.62\n", "3 (959, 1230, 165, 97) 13324 16005 0.59\n", "4 (1335, 1226, 150, 84) 11571 12600 0.56\n", "5 (1642, 1172, 173, 108) 13528 18684 0.62\n", "6 (599, 1132, 183, 78) 6084 14274 0.43\n", "7 (1247, 891, 148, 70) 906 10360 0.47\n", "8 (1608, 839, 180, 103) 5074 18540 0.57\n", "N1= 8 8 [[15438, (577, 1353, 166, 93), (659, 1397)], [21390, (72, 1253, 186, 115), (167, 1309)], [16005, (959, 1230, 165, 97), (1040, 1277)], [12600, (1335, 1226, 150, 84), (1409, 1266)], [18684, (1642, 1172, 173, 108), (1727, 1225)], [14274, (599, 1132, 183, 78), (688, 1160)], [10360, (1247, 891, 148, 70), (1325, 924)], [18540, (1608, 839, 180, 103), (1700, 885)]]\n", "imgt.shape= (1920, 2560, 3)\n", "imgi.shape= (3024, 4032, 3)\n", "1 (678, 1326, 167, 88) 13926 14696 0.53\n", "2 (1440, 1229, 155, 86) 12150 13330 0.55\n", "3 (1064, 1221, 162, 92) 13512 14904 0.57\n", "4 (181, 1202, 186, 109) 14021 20274 0.59\n", "5 (1757, 1195, 175, 103) 14091 18025 0.59\n", "6 (1468, 1104, 156, 106) 5307 16536 0.68\n", "7 (1090, 856, 125, 78) 4633 9750 0.62\n", "8 (0, 853, 197, 88) 6046 17336 0.45\n", "9 (1732, 852, 189, 99) 7079 18711 0.52\n", "N1= 9 9 [[14696, (678, 1326, 167, 88), (760, 1368)], [13330, (1440, 1229, 155, 86), (1516, 1271)], [14904, (1064, 1221, 162, 92), (1144, 1265)], [20274, (181, 1202, 186, 109), (277, 1259)], [18025, (1757, 1195, 175, 103), (1843, 1245)], [16536, (1468, 1104, 156, 106), (1557, 1158)], [9750, (1090, 856, 125, 78), (1149, 888)], [17336, (0, 853, 197, 88), (106, 903)], [18711, (1732, 852, 189, 99), (1824, 897)]]\n", "imgt.shape= (1920, 2560, 3)\n", "imgi.shape= (3024, 4032, 3)\n", "1 (1398, 1467, 196, 135) 6617 26460 0.69\n", "2 (1473, 1454, 163, 65) 3309 10595 0.4\n", "3 (675, 1321, 147, 83) 4454 12201 0.56\n", "4 (962, 1045, 334, 186) 58365 62124 0.56\n", "5 (328, 749, 184, 102) 2829 18768 0.55\n", "6 (1004, 684, 253, 124) 15894 31372 0.49\n", "7 (841, 403, 123, 83) 585 10209 0.67\n", "8 (1044, 306, 144, 96) 8050 13824 0.67\n", "9 (770, 292, 332, 163) 3829 54116 0.49\n", "10 (1976, 159, 143, 64) 1420 9152 0.45\n", "11 (1267, 127, 168, 84) 3466 14112 0.5\n", "12 (1934, 0, 153, 77) 8945 11781 0.5\n", "N1= 12 12 [[26460, (1398, 1467, 196, 135), (1485, 1536)], [10595, (1473, 1454, 163, 65), (1554, 1480)], [12201, (675, 1321, 147, 83), (732, 1356)], [62124, (962, 1045, 334, 186), (1128, 1136)], [18768, (328, 749, 184, 102), (451, 806)], [31372, (1004, 684, 253, 124), (1131, 752)], [10209, (841, 403, 123, 83), (920, 445)], [13824, (1044, 306, 144, 96), (1117, 355)], [54116, (770, 292, 332, 163), (946, 370)], [9152, (1976, 159, 143, 64), (2052, 191)], [14112, (1267, 127, 168, 84), (1333, 157)], [11781, (1934, 0, 153, 77), (2003, 31)]]\n", "imgt.shape= (1920, 2560, 3)\n", "imgi.shape= (3024, 4032, 3)\n", "1 (425, 1401, 170, 98) 14403 16660 0.58\n", "2 (813, 1268, 167, 102) 13608 17034 0.61\n", "3 (1193, 1254, 148, 88) 11684 13024 0.59\n", "4 (1497, 1194, 171, 110) 13221 18810 0.64\n", "5 (1147, 1139, 170, 98) 5230 16660 0.58\n", "6 (0, 1086, 183, 87) 1247 15921 0.48\n", "7 (1098, 924, 149, 68) 891 10132 0.46\n", "8 (1467, 865, 172, 104) 6278 17888 0.6\n", "9 (1903, 0, 197, 97) 16235 19109 0.49\n", "N1= 9 9 [[16660, (425, 1401, 170, 98), (509, 1448)], [17034, (813, 1268, 167, 102), (895, 1317)], [13024, (1193, 1254, 148, 88), (1266, 1297)], [18810, (1497, 1194, 171, 110), (1581, 1247)], [16660, (1147, 1139, 170, 98), (1225, 1187)], [15921, (0, 1086, 183, 87), (99, 1116)], [10132, (1098, 924, 149, 68), (1176, 956)], [17888, (1467, 865, 172, 104), (1546, 911)], [19109, (1903, 0, 197, 97), (1996, 43)]]\n", "imgt.shape= (1920, 2560, 3)\n" ] } ], "source": [ "\n", "##################GET the color code on the bottom line########\n", "def bottclr(img):\n", " img1=np.copy(img)\n", " img2=cv2.resize(img1,(160,120))\n", " hsv=cv2.cvtColor(img2,cv2.COLOR_BGR2HSV)\n", " Y1=90; Y2=86; Y3=82\n", " for x in range(160):\n", " print(x,img2[Y1,x,:],hsv[Y1,x,:],img2[Y2,x,:],hsv[Y2,x,:],\n", " img2[Y3,x,:],hsv[Y3,x,:])\n", " cv2.line(img2,(0,Y1),(159,Y1),(0,255,0),1)\n", " cv2.line(img2,(0,Y2),(159,Y2),(0,255,0),1)\n", " cv2.line(img2,(0,Y3),(159,Y3),(0,255,0),1)\n", " img2=cv2.resize(img2,(640,480))\n", " cv2.imshow('fcolor',img2)\n", " #cv2.waitKey(0)\n", " \n", " \n", "def tarbox(image):\n", " Lx=image.shape[1]; Ly=image.shape[0];\n", " Lxg=Lx; Lyg=Ly; Fg=1\n", " img2 = cv2.resize(image, (Lxg,Lyg), interpolation=cv2.INTER_AREA)\n", " img2=np.copy(image)\n", " gray = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)\n", " cnts1,_ = cv2.findContours(gray.copy(), cv2.RETR_EXTERNAL,\n", " cv2.CHAIN_APPROX_SIMPLE) # cv2.RETR_LIST\n", " lcnts1=len(cnts1); \n", " #print('lcnts1=',lcnts1)\n", " N1=0; ax=0; cx=0; px=0; mx1=0; Kc1=0; xx1=[0 for i in range(5)]\n", " clone1=img2.copy(); data1=[]\n", " for c in cnts1:\n", " M=cv2.moments(c)\n", " if(M['m00']==0): M['m00']=1\n", " cX=int(M['m10']/M['m00']); cY=int(M['m01']/M['m00'])\n", " area = cv2.contourArea(c); area3=round(area,3); #print(j,area3)\n", " area=int(area)\n", " (x,y,w,h)=cv2.boundingRect(c); Ac=w*h; asp=round(h/w,2)\n", " if(Ac<2000*4 or Ac>8000*10): continue\n", " if(asp>0.7 or asp<0.4): continue\n", " #if(area<10000): continue\n", " Kc1=1; N1+=1; \n", " xx1=(x,y,w,h); mx1=(cX,cY)\n", " d=[Ac,xx1,mx1]; data1.append(d)\n", " print(N1,xx1,area,Ac,asp)\n", " cv2.circle(clone1,mx1,6,(0,125,255),-1)\n", " x,y,w,h=xx1[0],xx1[1],xx1[2],xx1[3]\n", " cv2.rectangle(clone1,(x,y),(x+w,y+h),(255,125,0),8)\n", " #cv2.imshow('tarbox',clone1) \n", " lenda=len(data1)\n", " print('N1=',N1,lenda,data1)\n", " #cv2.waitKey(0)\n", " return lenda,data1,clone1\n", "\n", "\n", "def whitebox(image):\n", " #---------------------------- white color----------\n", " low=np.array([10,0 ,150 ])\n", " upp=np.array([250,40 ,255 ])\n", " #img=cv2.resize(image,(640,480))\n", " img=cv2.resize(image,(1280*2,960*2))\n", " hsv=cv2.cvtColor(img,cv2.COLOR_BGR2HSV)\n", " mask1=cv2.inRange(hsv,low,upp)\n", " imgmask=cv2.bitwise_and(img,img,mask=mask1)\n", " img3=np.copy(imgmask)\n", " #img3=cv2.resize(imgmask,(640,480))\n", " lenda,data1,imgt=tarbox(img3)\n", " imgts=cv2.resize(imgt,(1280,960))\n", " print('imgt.shape=',imgt.shape)\n", " #cv2.imshow('mask1',mask1)\n", " cv2.imshow('imgts',imgts)\n", " blue=[]; \n", " if(lenda>0):\n", " for i in range(lenda):\n", " (x,y,w,h)=data1[i][1]\n", " imgi=img3[y:y+h,x:x+w,:]\n", " imgi=img[y:y+h,x:x+w,:]\n", " imgir=cv2.resize(imgi,(128,128))\n", " cv2.imshow('tar_'+str(i),imgi)\n", " #cv2.waitKey(0)\n", " blue.append(imgi)\n", " #return lenda,data1,imgt,blue\n", "\n", "\n", "\n", "if __name__ == '__main__':\n", " from time import sleep\n", " import cv2\n", " import numpy as np\n", " print('fcolor.py as a main')\n", " low=np.array([10,0 ,150 ])\n", " upp=np.array([250,40 ,255 ])\n", " PIC=['P2.JPG','P3.JPG','P4.JPG','P5.JPG','P6.JPG']\n", " for j in range(5):\n", " #ret,img=cap.read()\n", " #if(j==0): fn='P2A.png'\n", " #if(j==0): fn='P2.JPG'\n", " #if(j==1): fn='P3.JPG'\n", " fn=PIC[j]\n", " imgi=cv2.imread(fn)\n", " print('imgi.shape=',imgi.shape)\n", " #bottclr(imgi)\n", " whitebox(imgi)\n", " #if cv2.waitKey(1) & 0xFF == ord('q'): break\n", " cv2.waitKey(0)\n" ] }, { "cell_type": "code", "execution_count": null, "id": "13964cca", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "0735e624", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import cv2\n", "from motorm import turn\n", "from time import sleep\n", "\n", "def tarbox(image):\n", " Lx=image.shape[1]; Ly=image.shape[0];\n", " Lxg=Lx; Lyg=Ly; Fg=1\n", " img2 = cv2.resize(image, (Lxg,Lyg), interpolation=cv2.INTER_AREA)\n", " img2=np.copy(image)\n", " gray = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)\n", " cnts1,_ = cv2.findContours(gray.copy(), cv2.RETR_EXTERNAL,\n", " cv2.CHAIN_APPROX_SIMPLE) # cv2.RETR_LIST\n", " lcnts1=len(cnts1); #print('lcnts1=',lcnts1)\n", " N1=0; ax=0; cx=0; px=0; mx1=0; Kc1=0; xx1=[0 for i in range(5)]\n", " clone1=img2.copy(); data1=[]\n", "\n", " #---------------only the largest contour concerned------\n", " if(lcnts1>0):\n", " cx=max(cnts1,key=cv2.contourArea)\n", " areax=cv2.contourArea(cx); areax=round(areax,3); \n", " if(areax>1000): \n", " M=cv2.moments(cx)\n", " if(M['m00']==0): M['m00']=1\n", " cX=int(M['m10']/M['m00']); cY=int(M['m01']/M['m00'])\n", " area = cv2.contourArea(cx); area3=round(area,3); \n", " area=int(area)\n", " Kc1=1; N1+=1; (x,y,w,h)=cv2.boundingRect(cx);\n", " xx1=(x,y,w,h); mx1=(cX,cY)\n", " d=[area,xx1,mx1]; data1.append(d)\n", " cv2.circle(clone1,mx1,6,(0,125,255),-1)\n", " x,y,w,h=xx1[0],xx1[1],xx1[2],xx1[3]\n", " cv2.rectangle(clone1,(x,y),(x+w,y+h),(255,125,0),1)\n", " \n", " for c in cnts1:\n", " M=cv2.moments(c)\n", " if(M['m00']==0): M['m00']=1\n", " cX=int(M['m10']/M['m00']); cY=int(M['m01']/M['m00'])\n", " area = cv2.contourArea(c); area3=round(area,3); #print(j,area3)\n", " area=int(area)\n", " if(area<1000): continue\n", " Kc1=1; N1+=1; (x,y,w,h)=cv2.boundingRect(c);\n", " xx1=(x,y,w,h); mx1=(cX,cY)\n", " d=[area,xx1,mx1]; #data1.append(d)\n", " cv2.circle(clone1,mx1,6,(0,125,255),-1)\n", " x,y,w,h=xx1[0],xx1[1],xx1[2],xx1[3]\n", " #cv2.rectangle(clone1,(x,y),(x+w,y+h),(255,125,0),1)\n", " #cv2.imshow('tarbox',clone1)\n", " \n", " lenda=len(data1)\n", " #print(lenda,data1)\n", " #cv2.waitKey(0)\n", " return lenda,data1,clone1\n", "\n", "\n", "def bluebox(image):\n", " #---------------------------- blue color----------\n", " low=np.array([105,110 ,80 ])\n", " upp=np.array([115,255 ,255 ])\n", " #---------------------------- green color----------\n", " low=np.array([40,80 ,60 ])\n", " upp=np.array([60,255 ,255 ])\n", " img=cv2.resize(image,(640,480))\n", " hsv=cv2.cvtColor(img,cv2.COLOR_BGR2HSV)\n", " mask1=cv2.inRange(hsv,low,upp)\n", " imgmask=cv2.bitwise_and(img,img,mask=mask1)\n", " img3=cv2.resize(imgmask,(640,480))\n", " lenda,data1,imgt=tarbox(img3)\n", " cv2.imshow('mask1',mask1)\n", " cv2.imshow('imgt',imgt)\n", " blue=[]; \n", " if(lenda>0):\n", " for i in range(lenda):\n", " (x,y,w,h)=data1[i][1]\n", " imgi=img3[y:y+h,x:x+w,:]\n", " imgir=cv2.resize(imgi,(128,128))\n", " #cv2.imshow('tar_'+str(i),imgir)\n", " #cv2.waitKey(0)\n", " blue.append(imgir)\n", " return lenda,data1,imgt,blue\n", "\n", "\n", "\n", "Lx=640; Ly=480; F=4; LX2=int(Lx/F); LY2=int(Ly/F)\n", "#.................2 masks for red color\n", "low1=np.array([170,60 ,90 ])\n", "upp1=np.array([180,255,220])\n", "low2=np.array([0, 60, 90 ])\n", "upp2=np.array([10, 255,255])\n", "print('low1=',low1,upp1,' low2=',low2,upp2)\n", "FW2=open('KBS.txt','w')\n", "tini=time.time()\n", "ncat=9; isx=128; isy=128\n", "mod1='model_NUM9C110.h5'\n", "mod1='model_GRN10C40.h5'\n", "LAB=['0','1','2','3','4','5','6','7','8','9']\n", "if(TS==1): model=keras.models.load_model(mod1)\n", "t00=time.time(); t02=t00\n", "tmodel=round(t00-tini,3)\n", "\n", "cap = cv2.VideoCapture(0)\n", "cap.set(cv2.CAP_PROP_FRAME_WIDTH,640)\n", "cap.set(cv2.CAP_PROP_FRAME_HEIGHT,480)\n", "fourcc = cv2.VideoWriter_fourcc(*'XVID')\n", "out = cv2.VideoWriter('redM.avi', fourcc, 20.0, (640, 480))\n", "sleep(2)\n", "\n", "COLOR=0\n", "#--------------------- REDLINE under car------\n", "def REDLT(img):\n", " #dri=input('dri=')\n", " img1=np.copy(img)\n", " imgA=cv2.resize(img1,(320,240))\n", " img2=cv2.resize(img1,(80,60))\n", " hsv=cv2.cvtColor(img2,cv2.COLOR_BGR2HSV)\n", " mask1=cv2.inRange(hsv,low1,upp1)\n", " mask2=cv2.inRange(hsv,low2,upp2)\n", " mask3=mask1 | mask2\n", " #mlow=int(60/2); mask3[0:mlow,:]=0; \n", " red=[]; red2=[]; red3=[]; red4=[]; Y=60-1; blk=[]\n", " Y1=59; Y2=49; Y3=39; Y4=29\n", " for x in range(80): \n", " if(mask3[Y1,x]>100): red.append(x)\n", " if(mask3[Y2,x]>100): red2.append(x)\n", " if(mask3[Y3,x]>100): red3.append(x)\n", " if(mask3[Y4,x]>100): red4.append(x)\n", " if(1525): \n", " cv2.line(imgA,(0,Y3*4),(79*4,Y3*4),(255,255,0),2)\n", " cv2.imshow('blk_U_turn',imgA)\n", " return -100,0,0,0,0,imgA\n", " #return dr,mid1,mid2,mid3,mid4,redmask\n", " lred1=len(red); lred2=len(red2); lred3=len(red3); lred4=len(red4)\n", " if(lred1>1): mid1=int(np.median(red))\n", " else: mid1=-1; \n", " if(lred2>1): mid2=int(np.median(red2))\n", " else: mid2=-1\n", " if(lred3>1): mid3=int(np.median(red3))\n", " else: mid3=-1\n", " if(lred4>1): mid4=int(np.median(red4))\n", " else: mid4=-1\n", " #--------------------drdrdr------\n", " dr=-10\n", " if(mid1<=30): dr=3\n", " if(30=50): dr=7\n", " mask3=cv2.resize(mask3,(320,240))\n", " redmask=cv2.bitwise_and(imgA,imgA,mask=mask3)\n", " dri=dr; a=[dri,mid1,mid2,mid3,mid4]; sa=str(a)\n", " #--------------------------------------------------\n", " cv2.line(redmask,(0,Y4*4),(79*4,Y4*4),(0,255,0),1)\n", " cv2.circle(redmask,(mid1*4,Y1*4),8,(0,255,0),-1)\n", " cv2.circle(redmask,(mid2*4,Y2*4),8,(0,255,0),-1)\n", " cv2.circle(redmask,(mid3*4,Y3*4),8,(0,255,0),-1)\n", " cv2.circle(redmask,(mid4*4,Y4*4),8,(0,255,0),-1)\n", " cv2.putText(redmask,sa,(1,20),cv2.FONT_HERSHEY_SIMPLEX,\n", " 0.5, (0,255,255), 1, cv2.LINE_AA)\n", " if(COLOR==1):\n", " a=[dr,mid1,mid2,mid3,mid4,lred1,lred2,lred3,lred4]\n", " print('a=',a)\n", " cv2.imshow('redmask',redmask)\n", " cv2.waitKey(0)\n", " input('1111')\n", " return dr,mid1,mid2,mid3,mid4,redmask\n", "\n", "\n", "\n", "\n", "#--------------------- REDLINE on the LefT-hand side of car------\n", "#---------------------find red pixels from hsv \n", "COLOR=0\n", "def cREDLT(img):\n", " #dri=input('dri=')\n", " img1=np.copy(img)\n", " imgA=cv2.resize(img1,(320,240))\n", " img2=cv2.resize(img1,(80,60))\n", " hsv=cv2.cvtColor(img2,cv2.COLOR_BGR2HSV)\n", " mask1=cv2.inRange(hsv,low1,upp1)\n", " mask2=cv2.inRange(hsv,low2,upp2)\n", " mask3=mask1 | mask2\n", " #mlow=int(60/2); mask3[0:mlow,:]=0; \n", " red=[]; red2=[]; red3=[]; red4=[]; Y=60-1; blk=[]\n", " Y1=59; Y2=49; Y3=39; Y4=29\n", " for x in range(65): #since we focus on the left-hand side of the view\n", " if(mask3[Y1,x]>100): red.append(x)\n", " if(mask3[Y2,x]>100): red2.append(x)\n", " if(mask3[Y3,x]>100): red3.append(x)\n", " if(mask3[Y4,x]>100): \n", " if(len(red4)>0 and red4[-1]25): \n", " cv2.line(imgA,(0,Y3*4),(79*4,Y3*4),(255,255,0),2)\n", " cv2.imshow('blk_U_turn',imgA)\n", " return -100,0,0,0,0,imgA\n", " #return dr,mid1,mid2,mid3,mid4,redmask\n", " lred1=len(red); lred2=len(red2); lred3=len(red3); lred4=len(red4)\n", " if(lred1>1): mid1=int(np.median(red))\n", " else: mid1=-1; \n", " if(lred2>1): mid2=int(np.median(red2))\n", " else: mid2=-1\n", " if(lred3>1): mid3=int(np.median(red3))\n", " else: mid3=-1\n", " if(lred4>1): mid4=int(np.median(red4))\n", " else: mid4=-1\n", " #--------------------drdrdr------\n", " dr=-10\n", " if(mid1>3 and -120): dr=71\n", " if(mid1>1 and mid2>mid1+12 or mid3>mid1+22): dr=72\n", " if(mid1<10 and mid2>0 and (mid4-mid3)>(mid3-mid2)+5): dr=72\n", " if(mid1<5 and mid2<5 and mid3>-1 and mid4>mid3+15): dr=73\n", " if(mid1<5 and mid2<5 and mid3<5 and 3030 and mid2>mid1+4): dr=74\n", " if(-18): dr=71\n", " mask3=cv2.resize(mask3,(320,240))\n", " redmask=cv2.bitwise_and(imgA,imgA,mask=mask3)\n", " dri=dr; a=[dri,mid1,mid2,mid3,mid4]; sa=str(a)\n", " cv2.circle(imgA,(mid1*4,Y1*4),8,(0,255,0),-1)\n", " cv2.circle(imgA,(mid2*4,Y2*4),8,(0,255,0),-1)\n", " cv2.circle(imgA,(mid3*4,Y3*4),8,(0,255,0),-1)\n", " cv2.circle(imgA,(mid4*4,Y4*4),8,(0,255,0),-1)\n", " cv2.line(imgA,(0,Y4*4),(79*4,Y4*4),(0,255,0),2)\n", " cv2.line(redmask,(0,Y4*4),(79*4,Y4*4),(0,255,0),1)\n", " cv2.circle(redmask,(mid1*4,Y1*4),8,(0,255,0),-1)\n", " cv2.circle(redmask,(mid2*4,Y2*4),8,(0,255,0),-1)\n", " cv2.circle(redmask,(mid3*4,Y3*4),8,(0,255,0),-1)\n", " cv2.circle(redmask,(mid4*4,Y4*4),8,(0,255,0),-1)\n", " cv2.putText(imgA,sa,(1,20),cv2.FONT_HERSHEY_SIMPLEX,\n", " 0.5, (0,255,255), 1, cv2.LINE_AA)\n", " cv2.putText(redmask,sa,(1,20),cv2.FONT_HERSHEY_SIMPLEX,\n", " 0.5, (0,255,255), 1, cv2.LINE_AA)\n", " if(COLOR==1):\n", " a=[dr,mid1,mid2,mid3,mid4,lred1,lred2,lred3,lred4]\n", " print('a=',a)\n", " cv2.imshow('redmask',redmask)\n", " cv2.waitKey(0)\n", " input('1111')\n", " return dr,mid1,mid2,mid3,mid4,redmask\n", "\n", "\n", "\n", "KB=open('KB.txt','w')\n", "break_program = True\n", "sp='A'\n", "def on_press(key):\n", " global break_program\n", " global sp\n", " sp=str(key).strip(\"'\")\n", " #KB.write(KS+'\\n')\n", " if sp == 'w' and break_program: turn(0.3,0.3,0.1); turn(0,0,0.1)\n", " if sp == 's' and break_program: turn(-0.3,-0.3,0.1); turn(0,0,0.1)\n", " if sp == 'a' and break_program: turn(0.0,0.3,0.1); turn(0,0,0.1)\n", " if sp == 'd' and break_program: turn(0.3,0.0,0.1); turn(0,0,0.1)\n", " if key == keyboard.Key.f1 and break_program:\n", " print ('end pressed')\n", " break_program = False\n", "\n", "\n", "\n", "listener = keyboard.Listener(on_press=on_press)\n", "listener.start()\n", "\n", "\n", "#=====================KKKKKKKKKKKKKKKKKK\n", "if(1==2):\n", " print('into KY_RED...')\n", " t=0\n", " while(t < 50 and cap.isOpened()):\n", " ret,img=cap.read(); \n", " if ret == False: break\n", " dr,mid1,mid2,mid3,mid4,img1=RED(img)\n", " ta=[mid1,mid2,mid3,lred1,lred2,lred3]\n", " ws=str(t)+' '+sp+' '+str(ta)\n", " if(t>0): print(ws)\n", " KB.write(ws+'\\n')\n", " out.write(img1)\n", " cv2.imshow('img',img)\n", " if cv2.waitKey(1) & 0xFF == ord('q'): break\n", " cv2.waitKey(0)\n", " t+=1\n", " cap.release()\n", " out.release()\n", " cv2.destroyAllWindows()\n", " KB.close()\n", " input('sssss')\n", "\n", "\n", "\n", "\n", "def QLT33(cap):\n", " turn(0.7,0.2,0.2); turn(0.3,0.3,0.1); turn(-0.3,0.3,0.1);\n", " #turn(0.4,0.0,0.15); turn(-0.3,0.3,0.35); turn(0,0,0.05);\n", " for j in range(100):\n", " ret,img=cap.read()\n", " DRU,mid1,mid2,mid3,mid4,img1=REDLT(img)\n", " if(int(DRU/10)==7): DRU=7\n", " #print('QLT33,j=',j,DRU,mid1,mid2,mid3,mid4)\n", " img3s=cv2.resize(img1,(320,240))\n", " cv2.imshow('LT',img3s)\n", " if cv2.waitKey(1) & 0xFF == ord('q'): break\n", " #cv2.waitKey(0)\n", " #if(DRU==1 or DRU==7): \n", " if(DRU==1 or DRU==-10 or DRU==7 or DRU==-100): \n", " turn(0.5,0.0,0.05); turn(0,0,0.10);\n", " print('QLT33,end_j=',j,DRU,mid1,mid2,mid3,mid4)\n", " #sleep(2)\n", " return\n", " turn(-0.4,0.4,0.05); turn(0,0,0.05);\n", " print('Unfortunate QLT33()...')\n", " return\n", "\n", "def GODR(DR):\n", " #.............................................. central redline....\n", " if(DR==0): turn(0.0,0.0,0.10); turn(0,0,0.05);\n", " if(DR==1): turn(0.3,0.3,0.10); turn(0,0,0.30);\n", " if(DR==3): turn(0.0,0.4,0.10); turn(0,0,0.30);\n", " if(DR==7): turn(0.4,0.0,0.10); turn(0,0,0.30);\n", " #.............................................. LEFT redline....\n", " if(DR==-100): turn(-0.4,0.4,0.50);turn(0.4,0.4,0.6);turn(-0.4,0.4,0.5)\n", " if(DR==-10): turn(0.0,0.0,0.10); turn(0,0,0.05);\n", " if(DR==30): turn(0.0,0.4,0.15); turn(0,0,0.00);\n", " if(DR==31): turn(0.5,0.2,0.20); turn(0.2,0.5,0.05);\n", " #if(DR==32): sleep(2); turn(0.0,0.4,0.10); turn(0,0,0.05);\n", " if(DR==33): QLT33(cap)\n", " if(DR==70): turn(0.4,0.0,0.10); turn(0,0,0.0);\n", " if(DR==71): turn(0.4,0.0,0.10); turn(0,0,0.0);\n", " if(DR==72): turn(0.4,-0.0,0.10); turn(0,0,0.0);\n", " if(DR==73): turn(0.4,-0.2,0.10); turn(0,0,0.05);\n", " #if(DR==-2): turn(-0.3,-0.3,0.05); turn(0,0,0.05);\n", "\n", "\n", "def load_images_from_folder(folder,nfile):\n", " isz=128\n", " images = []; N=0\n", " afs=os.listdir(folder)\n", " afso=sorted(afs)\n", " for filename in afso:\n", " N+=1\n", " print(N,filename)\n", " img = cv2.imread(os.path.join(folder,filename), cv2.IMREAD_GRAYSCALE)\n", " if img is not None:\n", " img = cv2.resize(img,(isz,isz))/255\n", " images.append(img)\n", " return np.array(images[:nfile])\n", "\n", "\n", "ML=0\n", "if(ML==1):\n", " print('In ML block test...')\n", " import os\n", " path='PNG'; ncat=10; nfile=40;\n", " ntest=6; pathT=path+'/S1/'\n", " LAB=['0','1','2','3','4','5','6','7','8','9']\n", " test=load_images_from_folder(pathT,ntest)\n", " print(type(test),test.shape)\n", " N=test.shape[0]\n", " for i in range(N):\n", " imgg=test[i,:,:]\n", " cv2.imshow('imgg',imgg)\n", " L1=modh5(model,LAB,imgg)\n", " print(N,' L1=',L1)\n", " cv2.waitKey(0)\n", " #input('iiii')\n", "\n", "\n", "\n", "ret,img=cap.read()\n", "#cv2.imshow('img_in',img)\n", "if(1==1):\n", " isz=128\n", " ret,img=cap.read()\n", " DRj,mid1,mid2,mid3,mid4,m0=REDLT(img)\n", " m0[:,316:319,:]=[255,255,255]\n", " m1=np.copy(m0);m2=np.copy(m0);m3=np.copy(m0);m4=np.copy(m0);m5=np.copy(m0);\n", " imgM=np.hstack((m0,m1,m2,m3,m4,m5))\n", " #cv2.imshow('img',img)\n", " #cv2.imshow('imgM',imgM)\n", " #cv2.waitKey(0)\n", " FW1=open('redM.txt','w')\n", " t00=time.time(); t02=t00\n", " tmodel=round(t00-tini,3)\n", " #-------------------RRRRRRR------\n", " for j in range(1,200):\n", " ret,img=cap.read()\n", " #fnr='PNG/S3/N'+str(j).zfill(1)+'.png'\n", " #imgr=cv2.imread(fnr)\n", " #--------------------------------------\n", " lenda,data1,imgt,blue=bluebox(img)\n", " fn='PNG/MLd_'+str(j).zfill(3)+'.png'\n", " fn2='PNG/DCa_'+str(j).zfill(3)+'.png'\n", " lenblue=len(blue)\n", " #print(j,fnr,' lenblue=',lenblue)\n", " #print(j,' lenblue=',lenblue)\n", " if(lenblue==1): \n", " imgb=blue[0]; cv2.imwrite(fn,imgb)\n", " elif(len(blue)>1): print('error len(blue)=',len(blue)); break\n", " else: pass\n", " cv2.imshow('imgt',imgt)\n", " if(lenda>0):\n", " BX=data1[0][1][0];BY=data1[0][1][1];\n", " BW=data1[0][1][2];BH=data1[0][1][3]\n", " else:\n", " BX=0; BY=0; BW=0; BH=0; area=0; aspr=0\n", " if(len(blue)==0): L1='X'\n", " if(lenblue==1):\n", " #bluein=np.copy(imgr)\n", " #imggi=test[j-1,:,:]\n", " #Li=modh5(model,LAB,imggi)\n", " #cv2.imshow('imggi',imggi)\n", " bluein=blue[0]\n", " imgg=cv2.cvtColor(bluein,cv2.COLOR_BGR2GRAY)\n", " imgg=cv2.resize(imgg,(isz,isz))/255\n", " L1=modh5(model,LAB,imgg)\n", " #cv2.imshow('imgg',imgg)\n", " # Using cv2.putText() method\n", " #color = 2**16-1 # 65535 is white color for 16 bis image\n", " #imgg1=np.copy(imgg)\n", " #cv2.putText(imgg1, 'Out:16', (40, 15), \n", " # cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 1, cv2.LINE_AA)\n", " #cv2.imshow('imgg1',imgg1)\n", " imgg2=np.copy(imgb)\n", " cv2.putText(imgg2,L1,(90,80),cv2.FONT_HERSHEY_SIMPLEX,\n", " 0.8, (0,255,255), 1, cv2.LINE_AA)\n", " cv2.imshow('imgg2',imgg2)\n", " cv2.imwrite(fn2,imgg2)\n", " t01=time.time(); t=round(t01-t00,3); dt=round(t01-t02,3);\n", " t02=t01\n", " #print(' imgg.shape=',imgg.shape)\n", " #print(j,t,dt,' L1=',L1,' Li=',Li)\n", " area=data1[0][0]; \n", " aspr=round(data1[0][1][2]/data1[0][1][3],2)\n", " #print(j,'bluein.shape=',bluein.shape)\n", " #cv2.waitKey(0)\n", " #continue\n", " #input('11111')\n", " #--------------------------------------\n", "\n", " imgi2=cv2.resize(img,(320,240))\n", " cv2.imshow('imgin',imgi2)\n", " DRj,mid1,mid2,mid3,mid4,img1=REDLT(img)\n", " '''\n", " j6=j%6\n", " if(j6==0): m0=np.copy(img1); m0[:,316:319,:]=[255,255,255]\n", " if(j6==1): m1=np.copy(img1); m1[:,316:319,:]=[255,255,255]\n", " if(j6==2): m2=np.copy(img1); m2[:,316:319,:]=[255,255,255]\n", " if(j6==3): m3=np.copy(img1); m3[:,316:319,:]=[255,255,255]\n", " if(j6==4): m4=np.copy(img1); m4[:,316:319,:]=[255,255,255]\n", " if(j6==5): m5=np.copy(img1); m5[:,316:319,:]=[255,255,255]\n", " imgM=np.hstack((m0,m1,m2,m3,m4,m5))\n", " cv2.imshow('imgM',imgM)\n", " '''\n", " #cv2.imwrite('DIR_'+str(j).zfill(3)+'.png',img1)\n", " DR=DRj\n", " a=[j,DRj,DR,mid1,mid2,mid3,mid4]; sa=str(a)\n", " #if(int(DR/10)==7): DR=7\n", " #if(int(DR/10)==8): DR=8\n", " #if(DR==-10): DR=1\n", " FW1.write(sa+'\\n')\n", " img2=cv2.resize(img1,(640,480))\n", " out.write(img2)\n", " if cv2.waitKey(1) & 0xFF == ord('q'): break\n", " #......................CCCCCC\n", " #DR=1\n", " #if(lenda==0): DR=0\n", " #if(area<1000): DR=0\n", " #print('data1=',data1,data1[0][1][2],data1[0][1][3],aspr)\n", " #if(lenda>0 and aspr<0.5): DR=0\n", " if(TS==0): L1=0\n", " #print('j=',j,DRj,DR,mid1,mid2,mid3,mid4,(lenda,BX,BY,BW,BH,aspr,L1))\n", " print('j,DR,L1=',j,DR,L1)\n", " #cv2.waitKey(0)\n", " GODR(DR)\n", " #sleep(1)\n", " FW1.close()\n", " FW2.close()\n", " cv2.destroyAllWindows()\n", " cap.release()\n" ] }, { "cell_type": "code", "execution_count": null, "id": "e5a2d322", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "09eac59d", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "df07bdbf", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "a1c530b2", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "6860d108", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import cv2\n", "from motorm import turn\n", "from time import sleep\n", "from pynput import keyboard\n", "TS=1\n", "if(TS==1):\n", " from tensorflow import keras\n", " from trackh5 import modh5\n", "import time\n", "#from trackblue import tarbox\n", "#from trackblue import bluebox\n", "\n", "\n", "def modh5(model,LAB,img):\n", " test=[]\n", " test.append(img)\n", " testa=np.array(test)\n", " X_test=testa\n", " X_test=X_test.reshape(X_test.shape[0],128,128,1).astype(\"float32\")\n", " pred = np.argmax(model.predict(X_test),axis=1)\n", " p1=pred[0]; L1=LAB[p1]\n", " #print('pred=',pred,p1,L1)\n", " return L1\n", "\n", "\n", "\n", "def tarbox(image):\n", " Lx=image.shape[1]; Ly=image.shape[0];\n", " Lxg=Lx; Lyg=Ly; Fg=1\n", " img2 = cv2.resize(image, (Lxg,Lyg), interpolation=cv2.INTER_AREA)\n", " img2=np.copy(image)\n", " gray = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)\n", " cnts1,_ = cv2.findContours(gray.copy(), cv2.RETR_EXTERNAL,\n", " cv2.CHAIN_APPROX_SIMPLE) # cv2.RETR_LIST\n", " lcnts1=len(cnts1); #print('lcnts1=',lcnts1)\n", " N1=0; ax=0; cx=0; px=0; mx1=0; Kc1=0; xx1=[0 for i in range(5)]\n", " clone1=img2.copy(); data1=[]\n", "\n", " #---------------only the largest contour concerned------\n", " if(lcnts1>0):\n", " cx=max(cnts1,key=cv2.contourArea)\n", " areax=cv2.contourArea(cx); areax=round(areax,3); \n", " if(areax>1000): \n", " M=cv2.moments(cx)\n", " if(M['m00']==0): M['m00']=1\n", " cX=int(M['m10']/M['m00']); cY=int(M['m01']/M['m00'])\n", " area = cv2.contourArea(cx); area3=round(area,3); \n", " area=int(area)\n", " Kc1=1; N1+=1; (x,y,w,h)=cv2.boundingRect(cx);\n", " xx1=(x,y,w,h); mx1=(cX,cY)\n", " d=[area,xx1,mx1]; data1.append(d)\n", " cv2.circle(clone1,mx1,6,(0,125,255),-1)\n", " x,y,w,h=xx1[0],xx1[1],xx1[2],xx1[3]\n", " cv2.rectangle(clone1,(x,y),(x+w,y+h),(255,125,0),1)\n", " \n", " for c in cnts1:\n", " M=cv2.moments(c)\n", " if(M['m00']==0): M['m00']=1\n", " cX=int(M['m10']/M['m00']); cY=int(M['m01']/M['m00'])\n", " area = cv2.contourArea(c); area3=round(area,3); #print(j,area3)\n", " area=int(area)\n", " if(area<1000): continue\n", " Kc1=1; N1+=1; (x,y,w,h)=cv2.boundingRect(c);\n", " xx1=(x,y,w,h); mx1=(cX,cY)\n", " d=[area,xx1,mx1]; #data1.append(d)\n", " cv2.circle(clone1,mx1,6,(0,125,255),-1)\n", " x,y,w,h=xx1[0],xx1[1],xx1[2],xx1[3]\n", " #cv2.rectangle(clone1,(x,y),(x+w,y+h),(255,125,0),1)\n", " #cv2.imshow('tarbox',clone1)\n", " \n", " lenda=len(data1)\n", " #print(lenda,data1)\n", " #cv2.waitKey(0)\n", " return lenda,data1,clone1\n", "\n", "\n", "def bluebox(image):\n", " #---------------------------- blue color----------\n", " low=np.array([105,110 ,80 ])\n", " upp=np.array([115,255 ,255 ])\n", " #---------------------------- green color----------\n", " low=np.array([40,80 ,60 ])\n", " upp=np.array([60,255 ,255 ])\n", " img=cv2.resize(image,(640,480))\n", " hsv=cv2.cvtColor(img,cv2.COLOR_BGR2HSV)\n", " mask1=cv2.inRange(hsv,low,upp)\n", " imgmask=cv2.bitwise_and(img,img,mask=mask1)\n", " img3=cv2.resize(imgmask,(640,480))\n", " lenda,data1,imgt=tarbox(img3)\n", " cv2.imshow('mask1',mask1)\n", " cv2.imshow('imgt',imgt)\n", " blue=[]; \n", " if(lenda>0):\n", " for i in range(lenda):\n", " (x,y,w,h)=data1[i][1]\n", " imgi=img3[y:y+h,x:x+w,:]\n", " imgir=cv2.resize(imgi,(128,128))\n", " #cv2.imshow('tar_'+str(i),imgir)\n", " #cv2.waitKey(0)\n", " blue.append(imgir)\n", " return lenda,data1,imgt,blue\n", "\n", "\n", "\n", "Lx=640; Ly=480; F=4; LX2=int(Lx/F); LY2=int(Ly/F)\n", "#.................2 masks for red color\n", "low1=np.array([170,60 ,90 ])\n", "upp1=np.array([180,255,220])\n", "low2=np.array([0, 60, 90 ])\n", "upp2=np.array([10, 255,255])\n", "print('low1=',low1,upp1,' low2=',low2,upp2)\n", "FW2=open('KBS.txt','w')\n", "tini=time.time()\n", "ncat=9; isx=128; isy=128\n", "mod1='model_NUM9C110.h5'\n", "mod1='model_GRN10C40.h5'\n", "LAB=['0','1','2','3','4','5','6','7','8','9']\n", "if(TS==1): model=keras.models.load_model(mod1)\n", "t00=time.time(); t02=t00\n", "tmodel=round(t00-tini,3)\n", "\n", "cap = cv2.VideoCapture(0)\n", "cap.set(cv2.CAP_PROP_FRAME_WIDTH,640)\n", "cap.set(cv2.CAP_PROP_FRAME_HEIGHT,480)\n", "fourcc = cv2.VideoWriter_fourcc(*'XVID')\n", "out = cv2.VideoWriter('redM.avi', fourcc, 20.0, (640, 480))\n", "sleep(2)\n", "\n", "COLOR=0\n", "#--------------------- REDLINE under car------\n", "def REDLT(img):\n", " #dri=input('dri=')\n", " img1=np.copy(img)\n", " imgA=cv2.resize(img1,(320,240))\n", " img2=cv2.resize(img1,(80,60))\n", " hsv=cv2.cvtColor(img2,cv2.COLOR_BGR2HSV)\n", " mask1=cv2.inRange(hsv,low1,upp1)\n", " mask2=cv2.inRange(hsv,low2,upp2)\n", " mask3=mask1 | mask2\n", " #mlow=int(60/2); mask3[0:mlow,:]=0; \n", " red=[]; red2=[]; red3=[]; red4=[]; Y=60-1; blk=[]\n", " Y1=59; Y2=49; Y3=39; Y4=29\n", " for x in range(80): \n", " if(mask3[Y1,x]>100): red.append(x)\n", " if(mask3[Y2,x]>100): red2.append(x)\n", " if(mask3[Y3,x]>100): red3.append(x)\n", " if(mask3[Y4,x]>100): red4.append(x)\n", " if(1525): \n", " cv2.line(imgA,(0,Y3*4),(79*4,Y3*4),(255,255,0),2)\n", " cv2.imshow('blk_U_turn',imgA)\n", " return -100,0,0,0,0,imgA\n", " #return dr,mid1,mid2,mid3,mid4,redmask\n", " lred1=len(red); lred2=len(red2); lred3=len(red3); lred4=len(red4)\n", " if(lred1>1): mid1=int(np.median(red))\n", " else: mid1=-1; \n", " if(lred2>1): mid2=int(np.median(red2))\n", " else: mid2=-1\n", " if(lred3>1): mid3=int(np.median(red3))\n", " else: mid3=-1\n", " if(lred4>1): mid4=int(np.median(red4))\n", " else: mid4=-1\n", " #--------------------drdrdr------\n", " dr=-10\n", " if(mid1<=30): dr=3\n", " if(30=50): dr=7\n", " mask3=cv2.resize(mask3,(320,240))\n", " redmask=cv2.bitwise_and(imgA,imgA,mask=mask3)\n", " dri=dr; a=[dri,mid1,mid2,mid3,mid4]; sa=str(a)\n", " #--------------------------------------------------\n", " cv2.line(redmask,(0,Y4*4),(79*4,Y4*4),(0,255,0),1)\n", " cv2.circle(redmask,(mid1*4,Y1*4),8,(0,255,0),-1)\n", " cv2.circle(redmask,(mid2*4,Y2*4),8,(0,255,0),-1)\n", " cv2.circle(redmask,(mid3*4,Y3*4),8,(0,255,0),-1)\n", " cv2.circle(redmask,(mid4*4,Y4*4),8,(0,255,0),-1)\n", " cv2.putText(redmask,sa,(1,20),cv2.FONT_HERSHEY_SIMPLEX,\n", " 0.5, (0,255,255), 1, cv2.LINE_AA)\n", " if(COLOR==1):\n", " a=[dr,mid1,mid2,mid3,mid4,lred1,lred2,lred3,lred4]\n", " print('a=',a)\n", " cv2.imshow('redmask',redmask)\n", " cv2.waitKey(0)\n", " input('1111')\n", " return dr,mid1,mid2,mid3,mid4,redmask\n", "\n", "\n", "\n", "\n", "#--------------------- REDLINE on the LefT-hand side of car------\n", "#---------------------find red pixels from hsv \n", "COLOR=0\n", "def cREDLT(img):\n", " #dri=input('dri=')\n", " img1=np.copy(img)\n", " imgA=cv2.resize(img1,(320,240))\n", " img2=cv2.resize(img1,(80,60))\n", " hsv=cv2.cvtColor(img2,cv2.COLOR_BGR2HSV)\n", " mask1=cv2.inRange(hsv,low1,upp1)\n", " mask2=cv2.inRange(hsv,low2,upp2)\n", " mask3=mask1 | mask2\n", " #mlow=int(60/2); mask3[0:mlow,:]=0; \n", " red=[]; red2=[]; red3=[]; red4=[]; Y=60-1; blk=[]\n", " Y1=59; Y2=49; Y3=39; Y4=29\n", " for x in range(65): #since we focus on the left-hand side of the view\n", " if(mask3[Y1,x]>100): red.append(x)\n", " if(mask3[Y2,x]>100): red2.append(x)\n", " if(mask3[Y3,x]>100): red3.append(x)\n", " if(mask3[Y4,x]>100): \n", " if(len(red4)>0 and red4[-1]25): \n", " cv2.line(imgA,(0,Y3*4),(79*4,Y3*4),(255,255,0),2)\n", " cv2.imshow('blk_U_turn',imgA)\n", " return -100,0,0,0,0,imgA\n", " #return dr,mid1,mid2,mid3,mid4,redmask\n", " lred1=len(red); lred2=len(red2); lred3=len(red3); lred4=len(red4)\n", " if(lred1>1): mid1=int(np.median(red))\n", " else: mid1=-1; \n", " if(lred2>1): mid2=int(np.median(red2))\n", " else: mid2=-1\n", " if(lred3>1): mid3=int(np.median(red3))\n", " else: mid3=-1\n", " if(lred4>1): mid4=int(np.median(red4))\n", " else: mid4=-1\n", " #--------------------drdrdr------\n", " dr=-10\n", " if(mid1>3 and -120): dr=71\n", " if(mid1>1 and mid2>mid1+12 or mid3>mid1+22): dr=72\n", " if(mid1<10 and mid2>0 and (mid4-mid3)>(mid3-mid2)+5): dr=72\n", " if(mid1<5 and mid2<5 and mid3>-1 and mid4>mid3+15): dr=73\n", " if(mid1<5 and mid2<5 and mid3<5 and 3030 and mid2>mid1+4): dr=74\n", " if(-18): dr=71\n", " mask3=cv2.resize(mask3,(320,240))\n", " redmask=cv2.bitwise_and(imgA,imgA,mask=mask3)\n", " dri=dr; a=[dri,mid1,mid2,mid3,mid4]; sa=str(a)\n", " cv2.circle(imgA,(mid1*4,Y1*4),8,(0,255,0),-1)\n", " cv2.circle(imgA,(mid2*4,Y2*4),8,(0,255,0),-1)\n", " cv2.circle(imgA,(mid3*4,Y3*4),8,(0,255,0),-1)\n", " cv2.circle(imgA,(mid4*4,Y4*4),8,(0,255,0),-1)\n", " cv2.line(imgA,(0,Y4*4),(79*4,Y4*4),(0,255,0),2)\n", " cv2.line(redmask,(0,Y4*4),(79*4,Y4*4),(0,255,0),1)\n", " cv2.circle(redmask,(mid1*4,Y1*4),8,(0,255,0),-1)\n", " cv2.circle(redmask,(mid2*4,Y2*4),8,(0,255,0),-1)\n", " cv2.circle(redmask,(mid3*4,Y3*4),8,(0,255,0),-1)\n", " cv2.circle(redmask,(mid4*4,Y4*4),8,(0,255,0),-1)\n", " cv2.putText(imgA,sa,(1,20),cv2.FONT_HERSHEY_SIMPLEX,\n", " 0.5, (0,255,255), 1, cv2.LINE_AA)\n", " cv2.putText(redmask,sa,(1,20),cv2.FONT_HERSHEY_SIMPLEX,\n", " 0.5, (0,255,255), 1, cv2.LINE_AA)\n", " if(COLOR==1):\n", " a=[dr,mid1,mid2,mid3,mid4,lred1,lred2,lred3,lred4]\n", " print('a=',a)\n", " cv2.imshow('redmask',redmask)\n", " cv2.waitKey(0)\n", " input('1111')\n", " return dr,mid1,mid2,mid3,mid4,redmask\n", "\n", "\n", "\n", "KB=open('KB.txt','w')\n", "break_program = True\n", "sp='A'\n", "def on_press(key):\n", " global break_program\n", " global sp\n", " sp=str(key).strip(\"'\")\n", " #KB.write(KS+'\\n')\n", " if sp == 'w' and break_program: turn(0.3,0.3,0.1); turn(0,0,0.1)\n", " if sp == 's' and break_program: turn(-0.3,-0.3,0.1); turn(0,0,0.1)\n", " if sp == 'a' and break_program: turn(0.0,0.3,0.1); turn(0,0,0.1)\n", " if sp == 'd' and break_program: turn(0.3,0.0,0.1); turn(0,0,0.1)\n", " if key == keyboard.Key.f1 and break_program:\n", " print ('end pressed')\n", " break_program = False\n", "\n", "\n", "\n", "listener = keyboard.Listener(on_press=on_press)\n", "listener.start()\n", "\n", "\n", "#=====================KKKKKKKKKKKKKKKKKK\n", "if(1==2):\n", " print('into KY_RED...')\n", " t=0\n", " while(t < 50 and cap.isOpened()):\n", " ret,img=cap.read(); \n", " if ret == False: break\n", " dr,mid1,mid2,mid3,mid4,img1=RED(img)\n", " ta=[mid1,mid2,mid3,lred1,lred2,lred3]\n", " ws=str(t)+' '+sp+' '+str(ta)\n", " if(t>0): print(ws)\n", " KB.write(ws+'\\n')\n", " out.write(img1)\n", " cv2.imshow('img',img)\n", " if cv2.waitKey(1) & 0xFF == ord('q'): break\n", " cv2.waitKey(0)\n", " t+=1\n", " cap.release()\n", " out.release()\n", " cv2.destroyAllWindows()\n", " KB.close()\n", " input('sssss')\n", "\n", "\n", "\n", "\n", "def QLT33(cap):\n", " turn(0.7,0.2,0.2); turn(0.3,0.3,0.1); turn(-0.3,0.3,0.1);\n", " #turn(0.4,0.0,0.15); turn(-0.3,0.3,0.35); turn(0,0,0.05);\n", " for j in range(100):\n", " ret,img=cap.read()\n", " DRU,mid1,mid2,mid3,mid4,img1=REDLT(img)\n", " if(int(DRU/10)==7): DRU=7\n", " #print('QLT33,j=',j,DRU,mid1,mid2,mid3,mid4)\n", " img3s=cv2.resize(img1,(320,240))\n", " cv2.imshow('LT',img3s)\n", " if cv2.waitKey(1) & 0xFF == ord('q'): break\n", " #cv2.waitKey(0)\n", " #if(DRU==1 or DRU==7): \n", " if(DRU==1 or DRU==-10 or DRU==7 or DRU==-100): \n", " turn(0.5,0.0,0.05); turn(0,0,0.10);\n", " print('QLT33,end_j=',j,DRU,mid1,mid2,mid3,mid4)\n", " #sleep(2)\n", " return\n", " turn(-0.4,0.4,0.05); turn(0,0,0.05);\n", " print('Unfortunate QLT33()...')\n", " return\n", "\n", "def GODR(DR):\n", " #.............................................. central redline....\n", " if(DR==0): turn(0.0,0.0,0.10); turn(0,0,0.05);\n", " if(DR==1): turn(0.3,0.3,0.10); turn(0,0,0.30);\n", " if(DR==3): turn(0.0,0.4,0.10); turn(0,0,0.30);\n", " if(DR==7): turn(0.4,0.0,0.10); turn(0,0,0.30);\n", " #.............................................. LEFT redline....\n", " if(DR==-100): turn(-0.4,0.4,0.50);turn(0.4,0.4,0.6);turn(-0.4,0.4,0.5)\n", " if(DR==-10): turn(0.0,0.0,0.10); turn(0,0,0.05);\n", " if(DR==30): turn(0.0,0.4,0.15); turn(0,0,0.00);\n", " if(DR==31): turn(0.5,0.2,0.20); turn(0.2,0.5,0.05);\n", " #if(DR==32): sleep(2); turn(0.0,0.4,0.10); turn(0,0,0.05);\n", " if(DR==33): QLT33(cap)\n", " if(DR==70): turn(0.4,0.0,0.10); turn(0,0,0.0);\n", " if(DR==71): turn(0.4,0.0,0.10); turn(0,0,0.0);\n", " if(DR==72): turn(0.4,-0.0,0.10); turn(0,0,0.0);\n", " if(DR==73): turn(0.4,-0.2,0.10); turn(0,0,0.05);\n", " #if(DR==-2): turn(-0.3,-0.3,0.05); turn(0,0,0.05);\n", "\n", "\n", "def load_images_from_folder(folder,nfile):\n", " isz=128\n", " images = []; N=0\n", " afs=os.listdir(folder)\n", " afso=sorted(afs)\n", " for filename in afso:\n", " N+=1\n", " print(N,filename)\n", " img = cv2.imread(os.path.join(folder,filename), cv2.IMREAD_GRAYSCALE)\n", " if img is not None:\n", " img = cv2.resize(img,(isz,isz))/255\n", " images.append(img)\n", " return np.array(images[:nfile])\n", "\n", "\n", "ML=0\n", "if(ML==1):\n", " print('In ML block test...')\n", " import os\n", " path='PNG'; ncat=10; nfile=40;\n", " ntest=6; pathT=path+'/S1/'\n", " LAB=['0','1','2','3','4','5','6','7','8','9']\n", " test=load_images_from_folder(pathT,ntest)\n", " print(type(test),test.shape)\n", " N=test.shape[0]\n", " for i in range(N):\n", " imgg=test[i,:,:]\n", " cv2.imshow('imgg',imgg)\n", " L1=modh5(model,LAB,imgg)\n", " print(N,' L1=',L1)\n", " cv2.waitKey(0)\n", " #input('iiii')\n", "\n", "\n", "\n", "ret,img=cap.read()\n", "#cv2.imshow('img_in',img)\n", "if(1==1):\n", " isz=128\n", " ret,img=cap.read()\n", " DRj,mid1,mid2,mid3,mid4,m0=REDLT(img)\n", " m0[:,316:319,:]=[255,255,255]\n", " m1=np.copy(m0);m2=np.copy(m0);m3=np.copy(m0);m4=np.copy(m0);m5=np.copy(m0);\n", " imgM=np.hstack((m0,m1,m2,m3,m4,m5))\n", " #cv2.imshow('img',img)\n", " #cv2.imshow('imgM',imgM)\n", " #cv2.waitKey(0)\n", " FW1=open('redM.txt','w')\n", " t00=time.time(); t02=t00\n", " tmodel=round(t00-tini,3)\n", " #-------------------RRRRRRR------\n", " for j in range(1,200):\n", " ret,img=cap.read()\n", " #fnr='PNG/S3/N'+str(j).zfill(1)+'.png'\n", " #imgr=cv2.imread(fnr)\n", " #--------------------------------------\n", " lenda,data1,imgt,blue=bluebox(img)\n", " fn='PNG/MLd_'+str(j).zfill(3)+'.png'\n", " fn2='PNG/DCa_'+str(j).zfill(3)+'.png'\n", " lenblue=len(blue)\n", " #print(j,fnr,' lenblue=',lenblue)\n", " #print(j,' lenblue=',lenblue)\n", " if(lenblue==1): \n", " imgb=blue[0]; cv2.imwrite(fn,imgb)\n", " elif(len(blue)>1): print('error len(blue)=',len(blue)); break\n", " else: pass\n", " cv2.imshow('imgt',imgt)\n", " if(lenda>0):\n", " BX=data1[0][1][0];BY=data1[0][1][1];\n", " BW=data1[0][1][2];BH=data1[0][1][3]\n", " else:\n", " BX=0; BY=0; BW=0; BH=0; area=0; aspr=0\n", " if(len(blue)==0): L1='X'\n", " if(lenblue==1):\n", " #bluein=np.copy(imgr)\n", " #imggi=test[j-1,:,:]\n", " #Li=modh5(model,LAB,imggi)\n", " #cv2.imshow('imggi',imggi)\n", " bluein=blue[0]\n", " imgg=cv2.cvtColor(bluein,cv2.COLOR_BGR2GRAY)\n", " imgg=cv2.resize(imgg,(isz,isz))/255\n", " L1=modh5(model,LAB,imgg)\n", " #cv2.imshow('imgg',imgg)\n", " # Using cv2.putText() method\n", " #color = 2**16-1 # 65535 is white color for 16 bis image\n", " #imgg1=np.copy(imgg)\n", " #cv2.putText(imgg1, 'Out:16', (40, 15), \n", " # cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 1, cv2.LINE_AA)\n", " #cv2.imshow('imgg1',imgg1)\n", " imgg2=np.copy(imgb)\n", " cv2.putText(imgg2,L1,(90,80),cv2.FONT_HERSHEY_SIMPLEX,\n", " 0.8, (0,255,255), 1, cv2.LINE_AA)\n", " cv2.imshow('imgg2',imgg2)\n", " cv2.imwrite(fn2,imgg2)\n", " t01=time.time(); t=round(t01-t00,3); dt=round(t01-t02,3);\n", " t02=t01\n", " #print(' imgg.shape=',imgg.shape)\n", " #print(j,t,dt,' L1=',L1,' Li=',Li)\n", " area=data1[0][0]; \n", " aspr=round(data1[0][1][2]/data1[0][1][3],2)\n", " #print(j,'bluein.shape=',bluein.shape)\n", " #cv2.waitKey(0)\n", " #continue\n", " #input('11111')\n", " #--------------------------------------\n", "\n", " imgi2=cv2.resize(img,(320,240))\n", " cv2.imshow('imgin',imgi2)\n", " DRj,mid1,mid2,mid3,mid4,img1=REDLT(img)\n", " '''\n", " j6=j%6\n", " if(j6==0): m0=np.copy(img1); m0[:,316:319,:]=[255,255,255]\n", " if(j6==1): m1=np.copy(img1); m1[:,316:319,:]=[255,255,255]\n", " if(j6==2): m2=np.copy(img1); m2[:,316:319,:]=[255,255,255]\n", " if(j6==3): m3=np.copy(img1); m3[:,316:319,:]=[255,255,255]\n", " if(j6==4): m4=np.copy(img1); m4[:,316:319,:]=[255,255,255]\n", " if(j6==5): m5=np.copy(img1); m5[:,316:319,:]=[255,255,255]\n", " imgM=np.hstack((m0,m1,m2,m3,m4,m5))\n", " cv2.imshow('imgM',imgM)\n", " '''\n", " #cv2.imwrite('DIR_'+str(j).zfill(3)+'.png',img1)\n", " DR=DRj\n", " a=[j,DRj,DR,mid1,mid2,mid3,mid4]; sa=str(a)\n", " #if(int(DR/10)==7): DR=7\n", " #if(int(DR/10)==8): DR=8\n", " #if(DR==-10): DR=1\n", " FW1.write(sa+'\\n')\n", " img2=cv2.resize(img1,(640,480))\n", " out.write(img2)\n", " if cv2.waitKey(1) & 0xFF == ord('q'): break\n", " #......................CCCCCC\n", " #DR=1\n", " #if(lenda==0): DR=0\n", " #if(area<1000): DR=0\n", " #print('data1=',data1,data1[0][1][2],data1[0][1][3],aspr)\n", " #if(lenda>0 and aspr<0.5): DR=0\n", " if(TS==0): L1=0\n", " #print('j=',j,DRj,DR,mid1,mid2,mid3,mid4,(lenda,BX,BY,BW,BH,aspr,L1))\n", " print('j,DR,L1=',j,DR,L1)\n", " #cv2.waitKey(0)\n", " GODR(DR)\n", " #sleep(1)\n", " FW1.close()\n", " FW2.close()\n", " cv2.destroyAllWindows()\n", " cap.release()\n" ] }, { "cell_type": "code", "execution_count": null, "id": "4f352572", "metadata": {}, "outputs": [], "source": [ "#-----AI2.py\n", "\n", "import cv2\n", "import random\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from tensorflow.keras.preprocessing import image\n", "from tensorflow.keras.optimizers import RMSprop\n", "from tensorflow import keras\n", "import tensorflow as tf\n", "import os \n", "import math\n", "import shutil\n", "from tensorflow.keras.models import Sequential\n", "from tensorflow.keras.layers import Dense\n", "from tensorflow.keras.layers import Flatten\n", "from tensorflow.keras.layers import Conv2D\n", "from tensorflow.keras.layers import MaxPooling2D\n", "from tensorflow.keras.layers import Dropout\n", "from tensorflow.keras.utils import to_categorical\n", "from sklearn.model_selection import train_test_split\n", "\n", "# 指定亂數種子\n", "seed = 7\n", "np.random.seed(seed)\n", "print('1st cell done...')\n", "\n", "# read image\n", "image_size=128\n", "\n", "def load_images_from_folder(folder,nfile):\n", " images = []\n", " for filename in os.listdir(folder):\n", " img = cv2.imread(os.path.join(folder,filename), cv2.IMREAD_GRAYSCALE)\n", " if img is not None:\n", " img = cv2.resize(img,(image_size,image_size))/255\n", " images.append(img)\n", " return np.array(images[:nfile])\n", "\n", "\n", "\n", "# set no. of images for each lable\n", "\n", "path='PNG'; ncat=10; nfile=40; \n", "mod1='model_GRN10C40.h5'\n", "ntest=ncat*5; pathT=path+'/T/'\n", "LAB=['0','1','2','3','4','5','6','7','8','9']\n", "\n", "print('load path=',path)\n", "train = load_images_from_folder(path+'/N0/',nfile)\n", "print('type(train)=',type(train),train.shape)\n", "Y_train = np.zeros(train.shape[0])\n", "X_train = train\n", "train = load_images_from_folder(path+'/N1/',nfile)\n", "Y_train = np.append(Y_train, np.ones(train.shape[0])*1)\n", "X_train = np.append(X_train, train, axis=0)\n", "\n", "if(ncat>2):\n", " train = load_images_from_folder(path+'/N2/',nfile)\n", " Y_train = np.append(Y_train, np.ones(train.shape[0])*2)\n", " X_train = np.append(X_train, train, axis=0)\n", "\n", "if(ncat>3):\n", " train = load_images_from_folder(path+'/N3/',nfile)\n", " Y_train = np.append(Y_train, np.ones(train.shape[0])*3)\n", " X_train = np.append(X_train, train, axis=0)\n", "\n", "if(ncat>4):\n", " train = load_images_from_folder(path+'/N4/',nfile)\n", " Y_train = np.append(Y_train, np.ones(train.shape[0])*4)\n", " X_train = np.append(X_train, train, axis=0)\n", "\n", "if(ncat>5):\n", " train = load_images_from_folder(path+'/N5/',nfile)\n", " Y_train = np.append(Y_train, np.ones(train.shape[0])*5)\n", " X_train = np.append(X_train, train, axis=0)\n", "\n", "if(ncat>6):\n", " train = load_images_from_folder(path+'/N6/',nfile)\n", " Y_train = np.append(Y_train, np.ones(train.shape[0])*6)\n", " X_train = np.append(X_train, train, axis=0)\n", "\n", "if(ncat>7):\n", " train = load_images_from_folder(path+'/N7/',nfile)\n", " Y_train = np.append(Y_train, np.ones(train.shape[0])*7)\n", " X_train = np.append(X_train, train, axis=0)\n", "\n", "if(ncat>8):\n", " train = load_images_from_folder(path+'/N8/',nfile)\n", " Y_train = np.append(Y_train, np.ones(train.shape[0])*8)\n", " X_train = np.append(X_train, train, axis=0)\n", "\n", "if(ncat>9):\n", " train = load_images_from_folder(path+'/N9/',nfile)\n", " Y_train = np.append(Y_train, np.ones(train.shape[0])*9)\n", " X_train = np.append(X_train, train, axis=0)\n", "\n", "\n", "\n", "print('X_train.shape_5=',X_train.shape); \n", "print('y_train.shape_5=',Y_train.shape)\n", "#print(Y_train)\n", "\n", "# One-hot編碼\n", "Y_train = to_categorical(Y_train)\n", "print('Y_train.shape2=',Y_train.shape)\n", "#print('Y2_train=',Y_train)\n", "#print(Y_train)\n", "\n", "# 將圖片轉換成 4D 張量\n", "X_train = X_train.reshape(X_train.shape[0], image_size, image_size, 1).astype(\"float32\")\n", "\n", "X_train, X_test, Y_train, Y_test = train_test_split(X_train, Y_train, test_size=0.1, random_state=42)\n", "\n", "\n", "'''\n", "print('show TEST dat : Fig and Label')\n", "for i in range(0,10):\n", " img = X_train[i,:,:,0]\n", " print(i,img.shape)\n", " cv2.imshow('img',img)\n", " cv2.waitKey(0)\n", "'''\n", "\n", "# 定義模型\n", "model = Sequential()\n", "model.add(Conv2D(16,(3,3),activation = 'relu', input_shape = (image_size, image_size,1)))\n", "model.add(MaxPooling2D(pool_size=(2,2)))\n", "model.add(Conv2D(32,(3,3),activation = 'relu'))\n", "model.add(MaxPooling2D(pool_size=(2,2)))\n", "model.add(Flatten())\n", "model.add(Dense(64,activation='relu'))\n", "model.add(Dropout(0.2))\n", "model.add(Dense(32,activation='relu'))\n", "model.add(Dense(ncat,activation='softmax'))\n", "\n", "model.summary()\n", "\n", "model.compile(loss = 'categorical_crossentropy',\n", " optimizer = \"adam\",\n", " metrics = ['accuracy'])\n", "\n", "print(X_train.shape)\n", "print(Y_train.shape)\n", "\n", "history = model.fit(X_train,Y_train,shuffle=True, validation_split=0.1,\n", " batch_size=128, epochs=60) \n", "\n", "# 顯示訓練和驗證準確度 使用accuracy instead of acc\n", "acc = history.history[\"accuracy\"]\n", "epochs = range(1, len(acc)+1)\n", "val_acc = history.history[\"val_accuracy\"]\n", "\n", "'''\n", "plt.plot(epochs, acc, \"bo-\", label=\"Training Acc\")\n", "plt.plot(epochs, val_acc, \"ro--\", label=\"Validation Acc\")\n", "plt.title(\"Training and Validation Accuracy\")\n", "plt.xlabel(\"Epochs\")\n", "plt.ylabel(\"Accuracy\")\n", "plt.legend()\n", "plt.show()\n", "'''\n", "\n", "\n", "# 顯示訓練和驗證損失\n", "loss = history.history[\"loss\"]\n", "epochs = range(1, len(loss)+1)\n", "val_loss = history.history[\"val_loss\"]\n", "\n", "'''\n", "plt.plot(epochs, loss, \"bo-\", label=\"Training Loss\")\n", "plt.plot(epochs, val_loss, \"ro--\", label=\"Validation Loss\")\n", "plt.title(\"Training and Validation Loss\")\n", "plt.xlabel(\"Epochs\")\n", "plt.ylabel(\"Loss\")\n", "plt.legend()\n", "plt.show()\n", "'''\n", "\n", "# 評估模型\n", "#print(X_train.shape)\n", "#print(Y_train.shape)\n", "#print(X_test.shape)\n", "#print(Y_test.shape)\n", "print(\"\\nTesting ...\")\n", "loss, accuracy = model.evaluate(X_train, Y_train, verbose=0)\n", "print(\"訓練資料集的準確度 = {:.2f}\".format(accuracy))\n", "loss, accuracy = model.evaluate(X_test, Y_test, verbose=0)\n", "print(\"測試資料集的準確度 = {:.2f}\".format(accuracy))\n", "\n", "model.save(mod1)\n", "print('model ',mod1,' is saved...')\n", "\n", "#------- you split this into a new cell if you want a frequent test run-------\n", "\n", "model = keras.models.load_model(mod1)\n", "print('load ',mod1,' ... done')\n", "\n", "# read images\n", "\n", "test = load_images_from_folder(pathT,ntest)\n", "print('load is done path=',pathT)\n", "X_test = test\n", "X_test = X_test.reshape(X_test.shape[0], image_size, image_size, 1).astype(\"float32\")\n", "print('X_test:type,len,shape=',type(X_test),len(X_test),X_test.shape)\n", "pred = np.argmax(model.predict(X_test),axis=1) \n", "print()\n", "#print(X_test)\n", "print('pred=',pred)\n", "for i in range(ntest):\n", " img = X_test[i,:,:,0]\n", " print(i,'pred[i]=',pred[i],' LAB=',LAB[pred[i]])\n", " cv2.imshow('img',img)\n", " cv2.waitKey(0)\n", " \n", " \n", " \n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.13" } }, "nbformat": 4, "nbformat_minor": 5 }