Computer vision and image processing in openCV

  Computer vision: Run your code on your phone camera + Face bluring  import numpy as np  import cv2  import os  os.chdir(r"E:\faceeyedetection") cap =cv2.VideoCapture(0) address = 'put your address here' cap.open(address) face_cascade =cv2.CascadeClassifier("haarcascade_frontalface_default.xml") #eyes_cascade =cv2.CascadeClassifier("haarcascade_eye.xml") while(cap.isOpened()):     ret ,frame =cap.read()                      gray =cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)                          faces =face_cascade.detectMultiScale(gray ,1.3 , 4)                     for (x,y,h,w) in faces :                        img = cv2.rectangle(frame ,(x,y),(x+w ,y+h),(0,255,0),3)            ...

  Computer vision:eye detection using open CV  import numpy as np  import cv2  import os  os.chdir(r"E:\faceeyedetection") cap =cv2.VideoCapture(0) face_cascade =cv2.CascadeClassifier("haarcascade_frontalface_default.xml") eyes_cascade =cv2.CascadeClassifier("haarcascade_eye.xml") while(cap.isOpened()):     ret ,frame =cap.read()                    gray =cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)                     faces =face_cascade.detectMultiScale(gray ,1.3 , 4)     for (x,y,h,w) in faces :         cv2.rectangle(frame ,(x,y),(x+w ,y+h),(0,255,0),3)                roi_gray=gray[y:y+h ,x:x+w]                roi_color=frame[y:y+h ,x:x+w]                eyes =eyes_cascade.detectMultiScale(roi_gray)...

Computer vision:Face Detection using Haar Cascade Classifiers  import numpy as np  import cv2  import os  os.chdir(r"E:\faceeyedetection") cap =cv2.VideoCapture(0) face_cascade =cv2.CascadeClassifier("haarcascade_frontalface_default.xml") eyes_cascade =cv2.CascadeClassifier("haarcascade_eye.xml") while(cap.isOpened()):     ret ,frame =cap.read()          gray =cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)          faces =face_cascade.detectMultiScale(gray ,1.3 , 4)     for (x,y,h,w) in faces :         cv2.rectangle(frame ,(x,y),(x+w ,y+h),(0,255,0),3)                roi_gray=gray[y:y+h ,x:x+w]                roi_color=frame[y:y+h ,x:x+w]                eyes =eyes_cascade.detectMultiScale(roi_gray)                for(ex,ey,eh,ew)...

Computer Vision: image Histograms and contrast stretching using OpenCV Python  import numpy as np import cv2 as cv from matplotlib import pyplot as plt img = cv.imread("lena.jpg") #img = np.zeros((200,200), np.uint8) #cv.rectangle(img, (0, 100), (200, 200), (255), -1) #cv.rectangle(img, (0, 50), (100, 100), (127), -1) b, g, r = cv.split(img) cv.imshow("img", img) cv.imshow("b", b) cv.imshow("g", g) cv.imshow("r", r) plt.hist(b.ravel(), 256, [0, 256]) plt.hist(g.ravel(), 256, [0, 256]) plt.hist(r.ravel(), 256, [0, 256]) hist = cv.calcHist([img], [0], None, [256], [0, 256]) plt.plot(hist) plt.show() cv.waitKey(0) cv.destroyAllWindows() ============================== Data used in this video   ============================= if  you faced any issue contact me via  what 's app : +201210894349 or   facebook

Computer vision: Detect Simple Geometric Shapes using OpenCV in Python | arabic   import numpy as np import cv2 img = cv2.imread('shapes.jpg') imgGrey = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) _, thrash = cv2.threshold(imgGrey, 240, 255, cv2.THRESH_BINARY) contours, _ = cv2.findContours(thrash, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) cv2.imshow("img", img) for contour in contours:     approx = cv2.approxPolyDP(contour, 0.01* cv2.arcLength(contour, True), True)               cv2.drawContours(img, [approx], 0, (0, 0, 0), 5)               x = approx.ravel()[0]               y = approx.ravel()[1] - 5                if len(approx) == 3:                   cv2.putText(img, "Triangle", (x, y), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0))            ...

Computer vision:Vehicle detection and  counting using openCV | Arabic import cv2 import numpy as np from time import sleep largura_min = 80 altura_min = 80 offset = 6 pos_linha = 550 # FPS to vídeo delay = 60 detec = [] carros = 0 def pega_centro(x, y, w, h):     x1 = int(w / 2)     y1 = int(h / 2)     cx = x + x1     cy = y + y1     return cx, cy # video source input cap = cv2.VideoCapture('video.mp4') subtracao = cv2.bgsegm.createBackgroundSubtractorMOG() while True:     ret, frame1 = cap.read()             tempo = float(1/delay)              sleep(tempo)              grey = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)              blur = cv2.GaussianBlur(grey, (3, 3), 5)               img_sub = subtracao.apply(blur)        ...

  Computer vision: detect , track and count car and moto in video using openCV  computer vision  import cv2 from tracker import * import os os.chdir("D:\object traking\object_tracking") result = cv2.VideoWriter('ahmedd.mp4',                           cv2.VideoWriter_fourcc(*'XVID'),                          20, (250,250)) # Create tracker object tracker = EuclideanDistTracker() cap = cv2.VideoCapture("highway.mp4") # Object detection from Stable camera object_detector = cv2.createBackgroundSubtractorMOG2(history=100, varThreshold=50) # if history is big number it will be hight  while True:     ret, frame = cap.read()             if ret is not True:                 break                   height, w...

  Computer vision:Drowsiness Detection using YOLOv5   #!pip install torch==1.12.1+cpu torchvision==0.13.1+cpu torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cpu #!git clone https://github.com/ultralytics/yolov5 #!cd yolov5 & pip install -r requirements.txt import os  os.chdir(r"C:\Users\amb\Downloads\drownsenesyolov5") import torch from matplotlib import pyplot as plt import numpy as np import cv2 model = torch.hub.load('ultralytics/yolov5', 'yolov5s') img = 'https://resources.stuff.co.nz/content/dam/images/1/k/q/r/d/c/image.related.StuffLandscapeSixteenByNine.1420x800.1kseqt.png/1501707394942.jpg' results = model(img) results.print() results.render() %matplotlib inline  plt.imshow(np.squeeze(results.render())) plt.show() results.show() results.xyxy np.squeeze(results.render()).shape cap = cv2.VideoCapture(0) while cap.isOpened():     ret, frame = cap.read()          # Make detections      r...

  Computer vision: Motion Detection and Tracking Using Opencv  import cv2 import numpy as np cap = cv2.VideoCapture('vtest.avi') frame_width = int( cap.get(cv2.CAP_PROP_FRAME_WIDTH)) frame_height =int( cap.get( cv2.CAP_PROP_FRAME_HEIGHT)) fourcc = cv2.VideoWriter_fourcc('X','V','I','D') out = cv2.VideoWriter("output.avi", fourcc, 5.0, (1280,720)) ret, frame1 = cap.read()# 1st frame ret, frame2 = cap.read() #2nd frame print(frame1.shape) while cap.isOpened():     diff = cv2.absdiff(frame1, frame2)             gray = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY)             blur = cv2.GaussianBlur(gray, (5,5), 0)             _, thresh = cv2.threshold(blur, 20, 255, cv2.THRESH_BINARY)             dilated = cv2.dilate(thresh, None, iterations=3)             contours, _ = cv2.findContours(dilated, cv2.RETR_...

  Computer vision course: find and draw contours in open CV  import numpy as np import cv2 img = cv2.imread('baseball.png') imgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) ret, thresh = cv2.threshold(imgray, 127, 255, 0) contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) print("Number of contours = " + str(len(contours))) print(contours[0]) cv2.drawContours(img, contours, -1, (0, 255, 0), 3) cv2.drawContours(imgray, contours, -1, (0, 255, 0), 3) cv2.imshow('Image', img) cv2.imshow('Image GRAY', imgray) cv2.waitKey(0) cv2.destroyAllWindows()#close window #===================================# if you faced any issue contact me via  what's app :  +201210894349  or  facebook

  Computer Vision Course: Image Blending using Pyramids in open CV  import cv2 import numpy as np apple = cv2.imread('apple.jpg') orange = cv2.imread('orange.jpg') print(apple.shape) print(orange.shape) apple_orange = np.hstack((apple[:, :256], orange[:, 256:])) # generate Gaussian pyramid for apple apple_copy = apple.copy() gp_apple = [apple_copy] for i in range(6):     apple_copy = cv2.pyrDown(apple_copy)            gp_apple.append(apple_copy)        # generate Gaussian pyramid for orange orange_copy = orange.copy() gp_orange = [orange_copy] for i in range(6):     orange_copy = cv2.pyrDown(orange_copy)           gp_orange.append(orange_copy)       # generate Laplacian Pyramid for apple apple_copy = gp_apple[5] lp_apple = [apple_copy] for i in range(5, 0, -1):     gaussian_expanded = cv2.pyrUp(gp_apple[i])           la...

  Computer Vision Course: image pyramids (Gaussian pyramid and Laplacian pyramid)   import cv2 import numpy as np img = cv2.imread("lena.jpg") layer = img.copy() gaussian_pyramid_list = [layer] for i in range(6):     layer = cv2.pyrDown(layer)          gaussian_pyramid_list.append(layer)          #cv2.imshow(str(i), layer)      layer = gaussian_pyramid_list[5] cv2.imshow('upper level Gaussian Pyramid', layer) laplacian_pyramid_list = [layer] for i in range(5, 0, -1):     gaussian_extended = cv2.pyrUp(gaussian_pyramid_list[i])          laplacian = cv2.subtract(gaussian_pyramid_list[i-1], gaussian_extended)          cv2.imshow(str(i), laplacian)      cv2.imshow("Original image", img) cv2.waitKey(0) cv2.destroyAllWindows()#close window ============================ data used in this video #===================================# if...

   Computer vision: calculte the vehicle  speed using open CV | Computer vision  arab import cv2 import time import os  os.chdir(r"C:\Users\amb\Downloads") cascade_src = r'C:\Users\amb\Downloads\cars1.xml' video_src = r'C:\Users\amb\Downloads\video3.MP4' #line a ax1=70 ay=90 ax2=230 #line b bx1=15 by=125 bx2=225 def Speed_Cal(time):       try:                 Speed = (9.144/1000)/(time/3600)                         return Speed                      except ZeroDivisionError :                 print ("can not devide by zero")                           #car num i = 1 start_time = time.time() #video .... cap = cv2.VideoCapture(video_src) car_cascade = cv2.CascadeClassifier(casc...