Nanopc T4 使用OpenCV

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识别长方形： 

import cv2
import cv2 as cv
import time
import platform
import os# 获取操作系统类型
os_type = platform.system()
if os_type == "Windows":# Windows系统cap = cv.VideoCapture(0)  # 使用第零个摄像头
elif os_type == "Linux":# Linux系统cap = cv.VideoCapture(10)  # 使用第十个摄像头if not cap.isOpened():print("Cannot capture from camera. Exiting.")os._exit(1)  # 退出程序
last_time = time.time()while (True):ret, frame = cap.read()imgContour = frame.copy()imgCanny = cv2.Canny(frame, 60, 60)  # Canny算子边缘检测contours, hierarchy = cv2.findContours(imgCanny, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)  # 寻找轮廓点for obj in contours:area = cv2.contourArea(obj)  # 计算轮廓内区域的面积# cv2.drawContours(imgContour, obj, -1, (255, 0, 0), 4)  # 绘制轮廓线perimeter = cv2.arcLength(obj, True)  # 计算轮廓周长approx = cv2.approxPolyDP(obj, 0.02 * perimeter, True)  # 获取轮廓角点坐标CornerNum = len(approx)  # 轮廓角点的数量x, y, w, h = cv2.boundingRect(approx)  # 获取坐标值和宽度、高度if CornerNum == 4:if 90 < w != h > 50:objType = "ChangFangXing"cv2.rectangle(imgContour, (x, y), (x + w, y + h), (0, 0, 255), 2)  # 绘制边界框cv2.putText(imgContour, objType, (x + (w // 2), y + (h // 2)), cv2.FONT_HERSHEY_COMPLEX, 0.6, (0, 0, 0),1)  # 绘制文字cv2.imshow("shape Detection", imgContour)if cv.waitKey(1) & 0xFF == ord('q'):breakcap.release()
cv.destroyAllWindows()

识别人脸1：

import cv2
import cv2 as cv
import time
import platform
import os# 获取操作系统类型
os_type = platform.system()
if os_type == "Windows":# Windows系统cap = cv.VideoCapture(0)  # 使用第零个摄像头
elif os_type == "Linux":# Linux系统cap = cv.VideoCapture(10)  # 使用第十个摄像头if not cap.isOpened():print("Cannot capture from camera. Exiting.")os._exit(1)  # 退出程序
last_time = time.time()img = cv.imread("D:\WorkSpace\Python\qsc.png")def template_matching(img_match, img, arithmetic_model):'''【作用】进行图片模板匹配【参数1】模板图片【参数2】进行匹配的图片【参数3】算法模型【返回】无'''# 进行模板匹配result = cv.matchTemplate(img, img_match, arithmetic_model)# 获取最小最大匹配值，还有对应的坐标min_value, max_value, min_coordinate, max_coordinate = cv.minMaxLoc(result)# 默认最佳最大值，当算法为CV_TM_SQDIFF或CV_TM_SQDIFF_NORMED时改为最小值best_coordinate = max_coordinateif arithmetic_model == cv.TM_SQDIFF or arithmetic_model == cv.TM_SQDIFF_NORMED:best_coordinate = min_coordinate# 获取匹配图片的高和宽m_height, m_width = img_match.shape[:2]# 矩形的起始点和结束点r_start = best_coordinater_end = (best_coordinate[0] + m_width, best_coordinate[1] + m_height);# 矩形的颜色和线的宽度r_color = (0, 100, 40)r_line_width = 2# 绘制矩形并展示cv.rectangle(img, r_start, r_end, r_color, r_line_width)cv.imshow("Qu ShiChao", img)while (True):ret, frame = cap.read()template_matching(img, frame, cv.TM_SQDIFF)if cv.waitKey(1) & 0xFF == ord('q'):breakcap.release()
cv.destroyAllWindows()

通模型识别人脸

import cv2
import cv2 as cv
import time
import platform
import os# 获取操作系统类型
os_type = platform.system()
if os_type == "Windows":# Windows系统cap = cv.VideoCapture(0)  # 使用第零个摄像头
elif os_type == "Linux":# Linux系统cap = cv.VideoCapture(10)  # 使用第十个摄像头if not cap.isOpened():print("Cannot capture from camera. Exiting.")os._exit(1)  # 退出程序
last_time = time.time()while (True):ret, frame = cap.read()# 这里是你的xml存放路径face_cascade = cv2.CascadeClassifier("D:\WorkSpace\Python\lbpcascade_frontalface.xml")# 开始人脸检测faces = face_cascade.detectMultiScale(frame, scaleFactor=1.03, minNeighbors=6)# 先复制一张图片frame1 = frame.copy()# 在检测到的人脸中操作for x, y, w, h in faces:# 画出人脸框frame1 = cv2.rectangle(frame1, (x, y), (x + w, y + h), (0, 255, 0), 2)# 找出人脸区域face_area = frame1[y:y + h, x:x + w]# 在人脸区域检测人眼cv2.imshow('face', frame1)if cv.waitKey(1) & 0xFF == ord('q'):breakcap.release()
cv.destroyAllWindows()

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