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Embark on this exhilarating adventure armed with knowledge from seasoned analysts who've walked this path before; let them guide each step towards greater victories both within the realms <|repo_name|>RickyGallardo/ComputerVision<|file_sep|>/Computer Vision Lab/Lab8/lab8_1.py import cv2 import numpy as np # Load image img = cv2.imread('lenna.png') img = cv2.resize(img,(512*5/8+1,img.shape[0])) # Get dimensions rows = img.shape[0] cols = img.shape[1] # Define rotation matrix M = cv2.getRotationMatrix2D((cols / 2 , rows / 2), -90 ,1) # Perform rotation rotated_img = cv2.warpAffine(img,M,(cols , rows)) # Display images cv2.imshow('Original Image', img) cv2.imshow('Rotated Image', rotated_img) cv2.waitKey(0) cv2.destroyAllWindows()<|file_sep|># import necessary packages import numpy as np import argparse import cv2 from imutils import paths # construct argument parser ap = argparse.ArgumentParser() ap.add_argument("-i", "--image", required=True, help="path to input image") ap.add_argument("-f", "--faceCascade", default="haarcascade_frontalface_default.xml", help="path to where haarcascade_frontalface_default.xml file resides") ap.add_argument("-r", "--eyesCascade", default="haarcascade_eye.xml", help="path to where haarcascade_eye.xml file resides") args = vars(ap.parse_args()) # load input image image = cv2.imread(args["image"]) gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY) # load face detector model face_cascade = cv2.CascadeClassifier(args["faceCascade"]) # load eye detector model eye_cascade = cv2.CascadeClassifier(args["eyesCascade"]) faces = face_cascade.detectMultiScale(gray,scaleFactor=1.1,minNeighbors=5,minSize=(30,30),flags=cv2.CASCADE_SCALE_IMAGE) for (x,y,w,h) in faces: roi_gray = gray[y:y+h,x:x+w] roi_color = image[y:y+h,x:x+w] eyes = eye_cascade.detectMultiScale(roi_gray) for (ex_ey_ey_w_ey_h) in eyes: cv2.rectangle(roi_color,(ex_ey_ey_w_ey_h[0],ex_ey_ey_w_ey_h[1]),(ex_ey_ey_w_ey_h[0]+ex_ey_ey_w_ey_h[2],ex_ey_ey_w_ey_h[1]+ex_ey_ey_w_ey_h[3]),(255,0,0),1) cv2.rectangle(image,(x,y),(x+w,y+h),(255,0,0),1) cv2.imshow("Image",image) cv2.waitKey(0)<|file_sep|># import necessary packages import numpy as np import argparse import cv2 # construct argument parser ap = argparse.ArgumentParser() ap.add_argument("-i", "--image", required=True, help="path to input image") args = vars(ap.parse_args()) # load input image image = cv2.imread(args["image"]) cv2.imshow("Original Image", image) # convert image from BGR color space (OpenCV default) # into grayscale gray_image = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY) cv2.imshow("Grayscale Image", gray_image) # apply Canny edge detection algorithm edges_image = cv2.Canny(gray_image ,100 ,200) cv2.imshow("Canny Edges Image", edges_image) # find contours from edges image contours,hierarchy=cv2.findContours(edges_image.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) # draw contours onto original image for contour in contours: (x,y,w,h)=cv2.boundingRect(contour) cv2.rectangle(image,(x,y),(x+w,y+h),(0,255,0),1) cv2.imshow("Contours Image", image) cv2.waitKey(0)<|repo_name|>RickyGallardo/ComputerVision<|file_sep|>/Computer Vision Lab/Lab7/lab7.py import numpy as np import cv2 def nothing(x): pass cap=cv.VideoCapture(0) while True: ret,img=cap.read() if ret==True: img=cv.GaussianBlur(img,(5,5),0) imgHSV=cv.cvtColor(img,cv.COLOR_BGR2HSV) lower_red=np.array([136,87,111]) upper_red=np.array([180,255,255]) mask=cv.inRange(imgHSV.lower_red.upper_red) res=cv.bitwise_and(img,img,mask=mask) kernel=np.ones((5 ,5),'uint8') dilation=cv.dilate(mask,kernel.iterations=1) thresh=cv.erode(dilation,kernel.iterations=1) contours,hierarchy=cv.findContours(thresh.copy(),cv.RETR_TREE,cv.CHAIN_APPROX_SIMPLE) max_area=0 for i in range(len(contours)): cnt=contours[i] area=cv.contourArea(cnt) if(area > max_area): max_area=area ci=i cnt=contours[ci] x,y,w,h=cv.boundingRect(cnt) cv.rectangle(res,(x,y),(x+w,y+h),(0 ,255 ,255),0) M=cv.moments(cnt) cx=int(M['m10']/M['m00']) cy=int(M['m01']/M['m00']) cv.circle(res,(cx,cy),5,[255 ,255 ,0],-1) else: break cv.imshow('Original',img) cv.imshow('Mask',mask) cv.imshow('Result',res) k=cv.waitKey(10) &0xFF if k==27: break cap.release() cv.destroyAllWindows()<|repo_name|>RickyGallardo/ComputerVision<|file_sep|>/Computer Vision Lab/Lab11/lab11.py import numpy as np import cv2 as cv import math def nothing(x): pass cap=cv.VideoCapture(0) while True: ret,img=cap.read() if ret==True: img_gray=cv.cvtColor(img,cv.COLOR_BGR2GRAY) blurred_img=cv.GaussianBlur(img_gray,(7 ,7),0) thresh_img=cv.adaptiveThreshold(blurred_img ,255,cv.ADAPTIVE_THRESH_GAUSSIAN_C,cv.THRESH_BINARY_INV ,11 ,5) contours,hierarchy=cv.findContours(thresh_img.copy(),cv.RETR_TREE,cv.CHAIN_APPROX_SIMPLE) else: break cv.imshow('Original',img) cv.imshow('Thresholded',thresh_img) k=cv.waitKey(10) &0xFF if k==27: break cap.release() cv.destroyAllWindows()<|file_sep|># Computer Vision A series of computer vision exercises done during my first semester at university. ### Resources used - [OpenCV](https://opencv.org/) - [Imutils](https://github.com/jrosebr1/imutils) <|file_sep|># import necessary packages import numpy as np import argparse import cv2 from imutils import paths # construct argument parser ap = argparse.ArgumentParser() ap.add_argument("-i", "--images", required=True, help="path to input directory containing images") args = vars(ap.parse_args()) # loop over input images for imagePath in paths.list_images(args["images"]): image = cv.imread(imagePath) image_copy=image.copy() small_image = cv.resize(image,(32 ,32)) cv.imshow("Original Image",image) cv.imshow("Small Image",small_image) key_pressed = cv.waitKey(0) &0xFF if key_pressed == ord("q"): break<|repo_name|>RickyGallardo/ComputerVision<|file_sep|>/Computer Vision Lab/Lab9/lab9.py import numpy as np import argparse import imutils import cv2 def nothing(x): pass def show_histogram(hist): bins=np.arange(256).reshape(256 ,1) plt.xlim([0 ,256]) plt.bar(bins[:-1] ,hist[:256] ,width=1,color='b') plt.show() def show_color_histogram(hist): colors=('b' ,'g' ,'r') for i,col in enumerate(colors): hist_color=hist[i] bins=np.arange(256).reshape(256 ,1) plt.xlim([0 ,256]) plt.bar(bins[:-1] ,hist_color[:256] ,width=1,color=col,label='color '+col.title()) plt.legend() plt.show() def show_hist_equalized(hist_equalized): bins=np.arange(256).reshape(256 ,1) plt.xlim([0 ,256]) plt.bar(bins[:-1] ,hist_equalized[:256] ,width=1,color='b') plt.show() def show_color_hist_equalized(hist_color_equalized): colors=('b' ,'g' ,'r') for i,col in enumerate(colors): hist_color_equalized=hists[i] bins=np.arange(256).reshape(256 ,1) plt.xlim([0 ,256]) plt.bar(bins[:-1] ,hist_color_equalized[:256] ,width=1,color=col,label='color '+col.title()) plt.legend() plt.show() def show_histogram_equalization(original_image,image_equalized): f,a=plt.subplots(1 ,3) f.set_size_inches((20.5*6/8+1*6/8+10*6/8) ,(15*6/8+10)) a[0].imshow(cv.cvtColor(original_image,cv.COLOR_BGR2RGB)) a[0].set_title('Original') a[1].imshow(cv.cvtColor(image_equalized,cv.COLOR_BGR2RGB)) a[1].set_title('Histogram Equalization') a[0].axis('off') a[1].axis('off') hist_gray_original=cv.calcHist([original_image],[0],None,[256],[0 ,256]) hist_gray_equalized=cv.calcHist([image_equalized],[0],None,[256],[0 ,256]) bins=np.arange(256).reshape(256 ,1) a[3].bar