есть такой код
import cv2
import numpy as np
import os
import time
# Load the MobileNet SSD model
model = cv2.dnn.readNetFromCaffe('MobileNetSSD_deploy.prototxt.txt', 'MobileNetSSD_deploy.caffemodel')
# Define classes of objects to detect
classNames = {0: 'background', 1: 'aeroplane', 2: 'bicycle', 3: 'bird', 4: 'boat',
5: 'bottle', 6: 'bus', 7: 'car', 8: 'cat', 9: 'chair', 10: 'cow',
11: 'diningtable', 12: 'dog', 13: 'horse', 14: 'motorbike', 15: 'person',
16: 'pottedplant', 17: 'sheep', 18: 'sofa', 19: 'train', 20: 'tvmonitor'}
# открываем видеопоток
cap = cv2.VideoCapture(0)
while True:
# Считываем кадр с видеопотока
ret, frame = cap.read()
img2=cv2.flip(frame, 1)
frame_re1 = img2[1:200, 1:180]
frame_re2 = img2[1:200, 180:360]
frame_re3 = img2[1:200, 360:540]
frame_re4 = img2[1:200, 540:720]
frame_re5 = img2[200:500, 1:180]
frame_re6 = img2[200:500, 180:360]
frame_re7 = img2[200:500, 360:540]
frame_re8 = img2[200:500, 540:720]
# Resize the frame to a fixed size for processing
frame_resized1 = cv2.resize(frame_re1, (300, 300))
frame_resized2 = cv2.resize(frame_re2, (300, 300))
frame_resized3 = cv2.resize(frame_re3, (300, 300))
frame_resized4 = cv2.resize(frame_re4, (300, 300))
frame_resized5 = cv2.resize(frame_re5, (300, 300))
frame_resized6 = cv2.resize(frame_re6, (300, 300))
frame_resized7 = cv2.resize(frame_re6, (300, 300))
frame_resized8 = cv2.resize(frame_re7, (300, 300))
# Конвертирование файла в большой массив двоичного кода
blob1 = cv2.dnn.blobFromImage(frame_resized1, 0.007843, (300, 300), (127.5, 127.5, 127.5), False)
blob2 = cv2.dnn.blobFromImage(frame_resized2, 0.007843, (300, 300), (127.5, 127.5, 127.5), False)
blob3 = cv2.dnn.blobFromImage(frame_resized3, 0.007843, (300, 300), (127.5, 127.5, 127.5), False)
blob4 = cv2.dnn.blobFromImage(frame_resized4, 0.007843, (300, 300), (127.5, 127.5, 127.5), False)
blob5 = cv2.dnn.blobFromImage(frame_resized5, 0.007843, (300, 300), (127.5, 127.5, 127.5), False)
blob6 = cv2.dnn.blobFromImage(frame_resized6, 0.007843, (300, 300), (127.5, 127.5, 127.5), False)
blob7 = cv2.dnn.blobFromImage(frame_resized7, 0.007843, (300, 300), (127.5, 127.5, 127.5), False)
blob8 = cv2.dnn.blobFromImage(frame_resized8, 0.007843, (300, 300), (127.5, 127.5, 127.5), False)
# Начинаем распознавание объектов
model.setInput(blob1)
detections1 = model.forward()
model.setInput(blob2)
detections2 = model.forward()
model.setInput(blob3)
detections3 = model.forward()
model.setInput(blob4)
detections4 = model.forward()
model.setInput(blob5)
detections5 = model.forward()
model.setInput(blob6)
detections6 = model.forward()
model.setInput(blob7)
detections7 = model.forward()
model.setInput(blob8)
detections8 = model.forward()
# Рисуем рамки вокруг объектов
# for i in range(detections.shape[2]):
# confidence = detections[0, 0, i, 2]
# if confidence > 0.5:
# class_id = int(detections[0, 0, i, 1])
# class_name = classNames[class_id]
# box = detections[0, 0, i, 3:7] * np.array([frame.shape[1], frame.shape[0], frame.shape[1], frame.shape[0]])
# x1, y1, x2, y2 = box.astype('int')
# cv2.rectangle(img2, (x1, y1), (x2, y2), (0, 0, 255), 2)
# cv2.putText(img2, class_name, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
# Показываем изображение в специальном окне
#cv2.imshow('Object Detection', img2)
for i in range(detections1.shape[2]):
confidence = detections1[0, 0, i, 2]
if confidence > 0.5:
class_id = int(detections1[0, 0, i, 1])
class_name = classNames[class_id]
box = detections1[0, 0, i, 3:7] * np.array([frame_re1.shape[1], frame_re1.shape[0], frame_re1.shape[1], frame_re1.shape[0]])
x1, y1, x2, y2 = box.astype('int')
cv2.rectangle(frame_re1, (x1, y1), (x2, y2), (0, 0, 255), 2)
cv2.putText(frame_re1, class_name, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
cv2.imshow('zone 1',frame_re1) # зона 1
#ret2,frame2 = cap.read()
#flip2 = cv2.flip(frame2, 1)
for i in range(detections2.shape[2]):
confidence = detections2[0, 0, i, 2]
if confidence > 0.5:
class_id = int(detections2[0, 0, i, 1])
class_name = classNames[class_id]
box = detections2[0, 0, i, 3:7] * np.array([frame_re2.shape[1], frame_re2.shape[0], frame_re2.shape[1], frame_re2.shape[0]])
x1, y1, x2, y2 = box.astype('int')
cv2.rectangle(frame_re2, (x1, y1), (x2, y2), (0, 0, 255), 2)
cv2.putText(frame_re2, class_name, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
cv2.imshow('zone 2',frame_re2) # зона 2
#ret3,frame3 = cap.read()
#flip3 = cv2.flip(frame3, 1)
for i in range(detections3.shape[2]):
confidence = detections3[0, 0, i, 2]
if confidence > 0.5:
class_id = int(detections3[0, 0, i, 1])
class_name = classNames[class_id]
box = detections3[0, 0, i, 3:7] * np.array([frame_re3.shape[1], frame_re3.shape[0], frame_re3.shape[1], frame_re3.shape[0]])
x1, y1, x2, y2 = box.astype('int')
cv2.rectangle(frame_re3, (x1, y1), (x2, y2), (0, 0, 255), 2)
cv2.putText(frame_re3, class_name, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
cv2.imshow('zone 3',frame_re3) # зона 3
#ret4,frame4 = cap.read()
#flip4 = cv2.flip(frame4, 1)
for i in range(detections4.shape[2]):
confidence = detections4[0, 0, i, 2]
if confidence > 0.5:
class_id = int(detections4[0, 0, i, 1])
class_name = classNames[class_id]
box = detections4[0, 0, i, 3:7] * np.array([frame_re4.shape[1], frame_re4.shape[0], frame_re4.shape[1], frame_re4.shape[0]])
x1, y1, x2, y2 = box.astype('int')
cv2.rectangle(frame_re4, (x1, y1), (x2, y2), (0, 0, 255), 2)
cv2.putText(frame_re4, class_name, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
cv2.imshow('zone 4',frame_re4) # зона 4
#re5,fram5 = cap.read()
#flip5 = cv2.flip(frame, 1)
for i in range(detections5.shape[2]):
confidence = detections5[0, 0, i, 2]
if confidence > 0.5:
class_id = int(detections5[0, 0, i, 1])
class_name = classNames[class_id]
box = detections5[0, 0, i, 3:7] * np.array([frame_re5.shape[1], frame_re5.shape[0], frame_re5.shape[1], frame_re5.shape[0]])
x1, y1, x2, y2 = box.astype('int')
cv2.rectangle(frame_re5, (x1, y1), (x2, y2), (0, 0, 255), 2)
cv2.putText(frame_re5, class_name, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
cv2.imshow('zone 5',frame_re5) # зона 5
#ret6,frame6 = cap.read()
#flip6 = cv2.flip(frame6, 1)
for i in range(detections6.shape[2]):
confidence = detections6[0, 0, i, 2]
if confidence > 0.5:
class_id = int(detections6[0, 0, i, 1])
class_name = classNames[class_id]
box = detections6[0, 0, i, 3:7] * np.array([frame_re6.shape[1], frame_re6.shape[0], frame_re6.shape[1], frame_re6.shape[0]])
x1, y1, x2, y2 = box.astype('int')
cv2.rectangle(frame_re6, (x1, y1), (x2, y2), (0, 0, 255), 2)
cv2.putText(frame_re6, class_name, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
cv2.imshow('zone 6',frame_re6) # зона 6
#ret7,frame7 = cap.read()
#flip7 = cv2.flip(frame3, 1)
for i in range(detections7.shape[2]):
confidence = detections7[0, 0, i, 2]
if confidence > 0.5:
class_id = int(detections7[0, 0, i, 1])
class_name = classNames[class_id]
box = detections7[0, 0, i, 3:7] * np.array([frame_re7.shape[1], frame_re7.shape[0], frame_re7.shape[1], frame_re7.shape[0]])
x1, y1, x2, y2 = box.astype('int')
cv2.rectangle(frame_re7, (x1, y1), (x2, y2), (0, 0, 255), 2)
cv2.putText(frame_re7, class_name, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
cv2.imshow('zone 7',frame_re7) # зона 7
#ret8,frame8 = cap.read()
#flip8 = cv2.flip(frame7, 1)
for i in range(detections8.shape[2]):
confidence = detections8[0, 0, i, 2]
if confidence > 0.5:
class_id = int(detections8[0, 0, i, 1])
class_name = classNames[class_id]
box = detections8[0, 0, i, 3:7] * np.array([frame_re8.shape[1], frame_re8.shape[0], frame_re8.shape[1], frame_re8.shape[0]])
x1, y1, x2, y2 = box.astype('int')
cv2.rectangle(frame_re8, (x1, y1), (x2, y2), (0, 0, 255), 2)
cv2.putText(frame_re8, class_name, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
cv2.imshow('zone 8',frame_re8) # зона 8
# print(class_name)
# При нажатии клавиши q код прекращает свою работу
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Прекращаем работу вспомогательных систем
cap.release()
cv2.destroyAllWindows()
Есть вот такой код, и я бы хотел запустить его на видюхе, так как на процессоре он вообще не фурычит. Почитал инет и там пишут, что opencv нельзя запускать на видеокарте, но некоторые пишут, что можно, но не знаю как. Может кто подскажет как мне быть?
