def myclick(self): goods = self.lineEdit.text() depth = 4 start_url = 'https://s.taobao.com/search?q=' + goods for i in range(depth): # 循环3次 try: url = start_url + '&s=' + str(44 * i) # 淘宝商品页面列表从0,44,88。 html = Test1.getHTMLText(url) # 两个函数 plt = re.findall( r'\"view_price\"\:\"[\d\.]*\"', html ) # 以列表类型返回形如 "view_price":"186.2" ,反斜杠\" \"表示"view_price" tlt = re.findall(r'\"raw_title\"\:\".*?\"', html) for i in range(len(plt)): price = eval(plt[i].split(':')[1]) # 详见淘宝商品信息爬虫(1) title = eval(tlt[i].split(':')[1]) item = QtWidgets.QTreeWidgetItem() item.setText(0, title) item.setData(1, QtCore.Qt.DisplayRole, float(price)) self.treeWidget.addTopLevelItem(item) except: continue print("222")
def getTest1C2AsC2(self, current): return Test1.C2()
def getTest1C2AsObject(self, current): return Test1.C2()
#-------------------------------Main body #loading an image #I=cv2.imread("colorProblem.jpg") #R,G,B = I[:,:,0], I[:,:,1], I[:,:,2] #for i in [R,G,B]: # I_eq = cv2.equalizeHist(i) # n,bins = histogram(array(I_eq),256,normed=True) # displayHistogram(n,bins) #Equalizing blue channel and merging I = cv2.imread("colorProblem.jpg") R, G, B = I[:, :, 0], I[:, :, 1], I[:, :, 2] B_eq = cv2.equalizeHist(B) I_mod = cv2.merge([R, G, B_eq]) Test1.show2_OpenCV(I, I_mod) #------Example of histogram equalization #loading an image #I=cv2.imread("Eye3.jpg") #I=cv2.cvtColor(I, cv2.COLOR_RGB2GRAY) #I_eq = cv2.equalizeHist(I) #Test1.show2_OpenCV(I,I_eq) #n,bins = histogram(array(I),256,normed=True) #n2,bins2= histogram(array(I_eq),256,normed=True) #displayHistogram(n,bins) #displayHistogram(n2,bins2)
''' Created on 19-02-2013 @author: Wiktor ''' import Test1 import cv2 from pylab import * def greyLevelMapLambdaWay(II,b,a): (M,N)=II.shape cc=zeros((M,N),uint8) for i in range(len[II]): cc[i] = map(lambda x: max(min(x*a+b,255),0), II[i]) # MAP FUNCTION!!!!!!! return cc I1=cv2.imread("GreenTest.jpg") I2 = cv2.cvtColor(I1, cv2.COLOR_RGB2GRAY) I2 = greyLevelMapLambdaWay(I2, 2, 1) Test1.show1_OpenCV(I2)
with open('result.txt', 'a') as f: f.write('\n') f.write(video_name) # video_name='video.wmv' im_dir = os.path.join(osp.dirname(__file__), 'frame') head_dir = os.path.join(osp.dirname(__file__), 'head') eye_dir = os.path.join(osp.dirname(__file__), 'eye') cut_dir = os.path.join(osp.dirname(__file__), 'cut') if not os.path.exists(im_dir): os.mkdir(im_dir) vc = cv2.VideoCapture(os.path.join(video_dir, video_name)) if vc.isOpened(): totalFrameNumber = vc.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT) totalFrameNumber = Test1.interception_video( video_dir, video_name, im_dir, totalFrameNumber) print totalFrameNumber numpy = True for root, dirs, files in os.walk(im_dir): if len(files) == 0: numpy = False if numpy: print 'demo1~~~~~~~~~~~~~~~~~~~~~' board_datas = [] board_datas = demo1.main() gc.collect() print 'demo2~~~~~~~~~~~~~~~~~~~~~' head_datas = demo2.main() gc.collect()
video_name = 'video.wmv' im_dir = os.path.join(osp.dirname(__file__), 'frame') head_dir = os.path.join(osp.dirname(__file__), 'head') eye_dir = os.path.join(osp.dirname(__file__), 'eye') if not os.path.exists(im_dir): os.mkdir(im_dir) vc = cv2.VideoCapture(os.path.join(video_dir, video_name)) if vc.isOpened(): totalFrameNumber = vc.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT) shutil.rmtree(eye_dir) os.mkdir(eye_dir) shutil.rmtree(head_dir) os.mkdir(head_dir) shutil.rmtree(im_dir) os.mkdir(im_dir) Test1.interception_video(video_dir, video_name, im_dir) numpy = True for root, dirs, files in os.walk(im_dir): if len(files) == 0: numpy = False if numpy: print 'demo1~~~~~~~~~~~~~~~~~~~~~' board_datas = [] board_datas = demo1.main() print 'demo2~~~~~~~~~~~~~~~~~~~~~' head_datas = demo2.main() if not os.path.exists(head_dir): os.mkdir(head_dir) NAN.getHead(im_dir, head_dir, head_datas) numpy = True
def throwTest1Def(self, current): raise Test1._def()
ax.set_xlim(left[0], right[-1]) ax.set_ylim(bottom.min(), top.max()) plt.show() #-------------------------------Main body #loading an image I=cv2.imread("colorProblem.jpg") #Creating the image histogram n,bins = histogram(array(I),256,normed=True) #displaing the histogram displayHistogram(n,bins) #------Example of histogram equalization #loading an image I=cv2.imread("colorProblem.jpg") I=cv2.cvtColor(I, cv2.COLOR_RGB2GRAY) I_eq = cv2.equalizeHist(I) Test1.show2_OpenCV(I,I_eq)
def ChangeGreenToWhite(I, Gthr): R, G, B = cv2.split(I) I2 = nonzero((G >= Gthr)) # Zwraca indexy wszystkich elementow dla ktorych condition is true I[I2] = [255, 255, 255] return I def ChangeGreenScreenRGB(I, Ibcg, Gthr): R, G, B = cv2.split(I) I2 = nonzero((G >= Gthr)) I[I2] = Ibcg[I2] return I I = cv2.imread("GreenTest.jpg") Ibcg = cv2.imread("GreenTest_Background.jpg") t.show1_OpenCV(ChangeGreenScreenRGB(I, Ibcg, 180)) """ #Replace all numbers >2 and <10 with 1 T = np.array(range(0,20)) T1 = nonzero((T>3) & (T<10)) T[T1] = 1 print T """
end = time() temp = end - start print("Sample's median chosen with selectDet time -->", temp) # SMALL SAMPLE WITH SORTING A = B start = time() Test5.QuickSort(A, m) # Sample ordered with selectionSort end = time() temp = end - start print("Sample sorted with selectionSort time -->", temp) A = B start = time() Test1.QuickSort(A, m) # Sample ordered with inserctionSort end = time() temp = end - start print("Sample sorted with inserctionSort time -->", temp) A = B start = time() test9.QuickSort(A, m) # Sample ordered with bubbleSort end = time() temp = end - start print("Sample sorted with bubbleSort time -->", temp) # OTHER SORTING ALGHORITMS A = B start = time()
''' Created on 19-02-2013 @author: Wiktor ''' import Test1 import cv2 from pylab import * def greyLevelMapLambdaWay(II, b, a): (M, N) = II.shape cc = zeros((M, N), uint8) for i in range(len[II]): cc[i] = map(lambda x: max(min(x * a + b, 255), 0), II[i]) # MAP FUNCTION!!!!!!! return cc I1 = cv2.imread("GreenTest.jpg") I2 = cv2.cvtColor(I1, cv2.COLOR_RGB2GRAY) I2 = greyLevelMapLambdaWay(I2, 2, 1) Test1.show1_OpenCV(I2)
I2 = nonzero((G>=Gthr)) #Zwraca indexy wszystkich elementow dla ktorych condition is true I[I2] = [255, 255, 255] return I def ChangeGreenScreenRGB(I, Ibcg, Gthr): R,G,B = cv2.split(I) I2 = nonzero((G>=Gthr)) I[I2] = Ibcg[I2] return I I = cv2.imread("GreenTest.jpg") Ibcg = cv2.imread("GreenTest_Background.jpg") t.show1_OpenCV(ChangeGreenScreenRGB(I, Ibcg, 180)) """ #Replace all numbers >2 and <10 with 1 T = np.array(range(0,20)) T1 = nonzero((T>3) & (T<10)) T[T1] = 1 print T """
#-------------------------------Main body #loading an image #I=cv2.imread("colorProblem.jpg") #R,G,B = I[:,:,0], I[:,:,1], I[:,:,2] #for i in [R,G,B]: # I_eq = cv2.equalizeHist(i) # n,bins = histogram(array(I_eq),256,normed=True) # displayHistogram(n,bins) #Equalizing blue channel and merging I=cv2.imread("colorProblem.jpg") R,G,B = I[:,:,0], I[:,:,1], I[:,:,2] B_eq = cv2.equalizeHist(B) I_mod = cv2.merge([R,G, B_eq]) Test1.show2_OpenCV(I, I_mod) #------Example of histogram equalization #loading an image #I=cv2.imread("Eye3.jpg") #I=cv2.cvtColor(I, cv2.COLOR_RGB2GRAY) #I_eq = cv2.equalizeHist(I) #Test1.show2_OpenCV(I,I_eq) #n,bins = histogram(array(I),256,normed=True) #n2,bins2= histogram(array(I_eq),256,normed=True) #displayHistogram(n,bins) #displayHistogram(n2,bins2)
def throwTest1E2AsE2(self, current): raise Test1.E2()
def startIdentify(): recognizer = cv2.face.LBPHFaceRecognizer_create() recognizer.read('trainer/trainer.yml') #cascadePath = "haarcascade_frontalface_default.xml" #faceCascade = cv2.CascadeClassifier(cascadePath); faceCascade = cv2.CascadeClassifier( 'C:/Users/e3003895/AppData/Local/Continuum/anaconda3/Lib/site-packages/cv2/data/haarcascade_frontalface_default.xml' ) font = cv2.FONT_HERSHEY_SIMPLEX #iniciate id counter id = 0 # names related to ids: example ==> Prasanna: id=1, etc names = ['None', 'Prasanna', '', '', '', 'W'] # Initialize and start realtime video capture cam = cv2.VideoCapture(0) cam.set(3, 640) # set video widht cam.set(4, 480) # set video height # Define min window size to be recognized as a face minW = 0.1 * cam.get(3) minH = 0.1 * cam.get(4) while True: ret, img = cam.read() #img = cv2.flip(img, -1) # Flip vertically gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) faces = faceCascade.detectMultiScale( gray, scaleFactor=1.2, minNeighbors=5, minSize=(int(minW), int(minH)), ) print('Number of faces detected: ' + str(len(faces))) for (x, y, w, h) in faces: cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2) id, confidence = recognizer.predict(gray[y:y + h, x:x + w]) # Check if confidence is less them 100 ==> "0" is perfect match #pm#if (confidence < 100): if (confidence < 80): id = names[id] #pm#confidence = " {0}%".format(round(100 - confidence)) confidence = " {0}%".format(round(confidence)) else: id = "unknown" #pm#confidence = " {0}%".format(round(100 - confidence)) confidence = " {0}%".format(round(confidence)) cv2.putText(img, str(id), (x + 5, y - 5), font, 1, (255, 255, 255), 2) cv2.putText(img, str(confidence), (x + 5, y + h - 5), font, 1, (255, 255, 0), 1) cv2.imshow('camera', img) k = cv2.waitKey(10) & 0xff # Press 'ESC' for exiting video if k == 27: break # Do a bit of cleanup print("\n [INFO] Exiting Program and cleanup stuff") cam.release() cv2.destroyAllWindows() print('detected id is ' + id) #ID_INT = int(id) return Test1.render_static(id)
import Test1 Test1.test()