init

Author: by-hwa

.DS_Store

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+# -*- coding: utf-8 -*-
+"""
+Created on Fri May 28 16:05:58 2021
+
+@author: 이병화
+"""
+
+import cv2
+
+face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades +"haarcascade_frontalface_default.xml")
+
+img = cv2.imread('Lena.jpg')
+
+gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
+
+faces = face_cascade.detectMultiScale(gray, 1.1,4)
+
+for (x,y,w,h) in faces:
+    cv2.rectangle(img, (x,y),(x+w, y+h), (255,0,0), 2)
+
+cv2.imshow('img',img)
+cv2.waitKey()
+cv2.destroyAllWindows()

2021 Vision system midterm.pdf

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+# -*- coding: utf-8 -*-
+"""
+Created on Sat Jun 12 13:25:00 2021
+
+@author: 이병화
+"""
+
+import cv2
+import matplotlib.pyplot as plt
+
+imgRGB = cv2.imread('cube.png')
+cv2.imshow('cube',imgRGB)
+
+b,g,r=cv2.split(imgRGB)
+
+cv2.imshow('b',b)
+cv2.imshow('g',g)
+cv2.imshow('r',r)
+
+imgHSV = cv2.cvtColor(imgRGB, cv2.COLOR_BGR2HSV)
+
+h,s,v = cv2.split(imgHSV)
+
+cv2.imshow('h',h)
+cv2.imshow('s',s)
+cv2.imshow('v',v)
+
+cv2.waitKey(0)
+cv2.destroyAllWindows()

Face detection 예제/Face detection.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Wed Jun  9 14:22:21 2021
+
+@author: 이병화
+"""
+
+import numpy as np
+import cv2
+import glob
+import sys
+import os
+import cv2.aruco
+
+
+'''
+criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
+
+objp = np.zeros((6*7,3), np.float32)
+objp[:,:2] = np.mgrid[0:7,0:6].T.reshape(-1,2)
+
+objpoints = []
+imgpoints = []
+
+images = glob.glob('./chess/*.jpg')
+#images = glob.glob('./calibration/*.png')
+
+
+for fname in images:
+    img = cv2.imread(fname)
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    ret, corners = cv2.findChessboardCorners(gray, (7,6), None)
+    
+    if ret == True:
+        objpoints.append(objp)
+        corners2 = cv2.cornerSubPix(gray,corners, (11,11), (-1,-1), criteria)
+        imgpoints.append(corners)
+        
+        img = cv2.drawChessboardCorners(img, (7,6), corners2, ret)
+        cv2.imshow('img', img)
+        cv2.waitKey(500)
+        
+cv2.destroyAllWindows()
+
+ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
+
+img = cv2.imread('./chess/KakaoTalk_20210512_224651717_15.jpg')
+#img = cv2.imread('./calibration/00010.png')
+h,  w = img.shape[:2]
+newcameramtx, roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (w,h), 1, (w,h))
+
+dst = cv2.undistort(img, mtx, dist, None, newcameramtx)
+
+x, y, w, h = roi
+dst = dst[y:y+h, x:x+w]
+cv2.imwrite('calibresult.png', dst)
+
+tot_error = 0
+for i in range(len(objpoints)):
+    imgpoints2, _ = cv2.projectPoints(objpoints[i], rvecs[i], tvecs[i], mtx, dist)
+    error = cv2.norm(imgpoints[i],imgpoints2, cv2.NORM_L2)/len(imgpoints2)
+    tot_error += error
+print("total error: ", tot_error/len(objpoints))
+'''
+
+# linedetection and following
+
+# edge검출 ROI처리
+# 카메라 사용
+#cap1 = cv2.VideoCapture(0)
+
+#if not cap1.isOpened():
+    #print('Camera open failed!')
+    #sys.exit()
+
+#mtx = np.load('mtx.npy')
+#dist = np.load('dist.npy')
+    
+#img = cv2.imread('./Inteld435i/00006_image.png')
+#h,  w = img.shape[:2]
+#newcameramtx, roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (w,h), 1, (w,h))
+
+#dst = cv2.undistort(img, mtx, dist, None, newcameramtx)
+
+#x, y, w, h = roi
+#dst = dst[y:y+h, x:x+w]
+#cap1 = cv2.imwrite('calibresult.png', dst)
+
+
+cap1 = cv2.imread('./Inteld435i/00070_image.png')
+#cap1 = cv2.imread('./image/image_018.jpg')
+
+caphsv = cv2.cvtColor(cap1, cv2.COLOR_BGR2HSV)
+h,s,v = cv2.split(caphsv)
+
+edges = cv2.Canny(s,100,200)
+
+roi_h = edges.shape[0]
+roi_w = edges.shape[1]
+
+region = np.array([
+        [[60, roi_h], [285, 240],[330, 230], [roi_w, 430]]
+    ], dtype = np.int32)
+
+mask = np.zeros_like(s)
+cv2.fillPoly(mask, region, 255)
+region = np.array([
+        [[140, roi_h], [290, 230],[350, 230], [600, 480]]
+    ], dtype = np.int32)
+cv2.fillPoly(mask,region, 0)
+
+roimg = cv2.bitwise_and(edges, mask)
+
+cv2.imshow('Canny',edges)
+cv2.imshow('ROI',roimg)
+
+#hought transform 차선검출
+lines = cv2.HoughLines(roimg, 1, np.pi/180, 180)
+#lines = cv2.HoughLinesP(roimg, 1,np.pi/180,50,100,10)
+cap2 = cap1
+
+if lines is not None:
+    for line in lines :
+        #x1,y1,x2,y2 = line[0]
+        r, theta = line[0]
+        tx, ty = np.cos(theta), np.sin(theta)
+        x0, y0 = tx*r, ty*r
+        x1, y1 = int(x0 + 1000*(-ty)), int(y0 + 1000 * tx)
+        x2, y2 = int(x0 - 1000*(-ty)), int(y0 - 1000 * tx)
+        print(theta*(180/3.14))
+        cap2 = cv2.line(cap2, (x1,y1), (x2,y2), (0,0,255), 1)
+    
+    
+#cv2.circle(cap1,(310,210),5,(0,0,255),-1)
+cv2.imshow('roimg',cap2)
+
+
+#object detection and localization
+
+#hog = cv2.HOGDescriptor((48,96),(16,16),(8,8),(8,8),9)
+
+
+#hog=cv2.HOGDescriptor()
+#hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
+
+#detected, _ = hog.detectMultiScale(cap1)
+
+#for (x,y,w,h) in detected: 
+#    cv2.rectangle(cap1, (x,y),(x+w,y+h), (50,200,50),3)
+#cv2.imshow('HOG', cap2)
+
+
+
+net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")
+classes = []
+with open("coco.names", "r") as f:
+    classes = [line.strip() for line in f.readlines()]
+layer_names = net.getLayerNames()
+output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
+colors = np.random.uniform(0, 255, size=(len(classes), 3))
+
+cap3 = cv2.resize(cap1, None, fx=0.4, fy=0.4)
+height, width, channels = cap3.shape
+
+blob = cv2.dnn.blobFromImage(cap3, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
+net.setInput(blob)
+outs = net.forward(output_layers)
+
+class_ids = []
+confidences = []
+boxes = []
+for out in outs:
+    for detection in out:
+        scores = detection[5:]
+        class_id = np.argmax(scores)
+        confidence = scores[class_id]
+        if confidence > 0.5:
+            # Object detected
+            center_x = int(detection[0] * width)
+            center_y = int(detection[1] * height)
+            w = int(detection[2] * width)
+            h = int(detection[3] * height)
+            # Rectangle coordinates
+            x = int(center_x - w / 2)
+            y = int(center_y - h / 2)
+            boxes.append([x, y, w, h])
+            confidences.append(float(confidence))
+            class_ids.append(class_id)
+
+indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
+
+font = cv2.FONT_HERSHEY_PLAIN
+for i in range(len(boxes)):
+    if i in indexes:
+        x, y, w, h = boxes[i]
+        label = str(classes[class_ids[i]])
+        #color = colors[i]
+        color = colors[10]
+        x = round(x*2.5)
+        y = round(y*2.5)
+        w = round(w*2.5)
+        h = round(h*2.5)
+        cv2.rectangle(cap2, (x, y), ((x + w), (y + h)), color, 2)
+        
+        
+cv2.imshow("Image", cap2)
+# Markerdetection
+
+#독자기능
+
+pts1 = np.float32([[285, 240],[380, 260],[60, 480], [640, 450]])
+pts2 = np.float32([[160,0],[480,0],[160,480],[480,480]])
+
+M = cv2.getPerspectiveTransform(pts1,pts2)
+
+dst = cv2.warpPerspective(cap1,M,(640,480))
+
+cv2.imshow("birdeye",dst)
+
+cv2.waitKey()
+cv2.destroyAllWindows()