[英]How to maintain a higher FPS while enlarging an image using cv2.resize()
我正在運行一個簡單的代碼來測量我的實時流的FPS(使用網絡攝像頭)。 當我將圖像調整為較大的幀時,FPS會降低。 有什么方法可以同時放大框架(通過調整大小功能)來維持FPS。 還是不可避免的權衡?
這是使用face_recognition庫進行人臉識別的代碼。 當我將尺寸調整為較大尺寸時,FPS(每秒幀數)變慢。 有什么方法可以維持較高的FPS,同時也可以使用cv2.resize()
放大圖像嗎?
import face_recognition
import cv2
video_capture = cv2.VideoCapture(0)
#video_capture.set(cv2.CAP_PROP_FPS, 30)
# Load a sample picture and learn how to recognize it.
obama_image = face_recognition.load_image_file("osama LinkedIN.jpg")
obama_face_encoding = face_recognition.face_encodings(obama_image)[0]
# Load a second sample picture and learn how to recognize it.
imran_shafqat_image = face_recognition.load_image_file("haris intern3.jpg")
imran_shafqat_face_encoding = face_recognition.face_encodings(imran_shafqat_image)[0]
# Create arrays of known face encodings and their names
known_face_encodings = [
obama_face_encoding,
imran_shafqat_face_encoding,
# obama_face_encoding2
# biden_face_encoding
]
known_face_names = [
"Osama Naeem",
"Imran Shafqat"
# "random guy2"
]
# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
fxx = 1.5
fyy = 1.5
while True:
# Grab a single frame of video
ret, frame = video_capture.read()
# Resize frame of video to 1/4 size for faster face recognition processing
small_frame = cv2.resize(frame, (0, 0), fx=fxx, fy=fyy)
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_small_frame = small_frame[:, :, ::-1]
#rgb_small_frame = frame[:, :, ::-1]
# Only process every other frame of video to save time
if process_this_frame:
# Find all the faces and face encodings in the current frame of video
face_locations = face_recognition.face_locations(rgb_small_frame)
face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
face_names = []
for face_encoding in face_encodings:
# See if the face is a match for the known face(s)
matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
print ("match = ", matches)
name = "Unknown"
# If a match was found in known_face_encodings, just use the first one.
if True in matches:
first_match_index = matches.index(True)
name = known_face_names[first_match_index]
face_names.append(name)
process_this_frame = not process_this_frame
# Display the results
for (top, right, bottom, left), name in zip(face_locations, face_names):
# Scale back up face locations since the frame we detected in was scaled to 1/4 size
top *= (1/fxx)
right *= (1/fxx)
bottom *= (1/fyy)
left *= (1/fyy)
# Draw a box around the face
cv2.rectangle(frame, (round(left), round(top)), (round(right), round(bottom)), (0, 0, 255), 2)
# Draw a label with a name below the face
#cv2.rectangle(frame, (round(left) - 35, round(bottom) - 40), (round(right), round(bottom)), (0, 0, 255), cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame, name, (round(left) + 6, round(bottom) - 6), font, 0.5, (255, 255, 255), 1)
# Display the resulting image
cv2.imshow('Video', frame)
# Hit 'q' on the keyboard to quit!
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()
代碼工作正常,但是當我將FPS放大到更大的尺寸時,我想以相同的速率保持FPS。
使用cv2.resize()
放大圖像時,會創建一個較大的圖像,這會增加每幀的處理時間。 本質上,您的程序必須做其他工作才能處理更多像素。 但是,可能允許您提高FPS的解決方案是使用多線程 。 通過這種方法,您可以通過減少I / O延遲來提高FPS,而不是減少處理每個調整大小的幀所需的時間。 這個想法是在您在主線程中進行處理時,將閱讀框分成自己的獨立線程。 這是一個小部件,顯示了如何將閱讀框和處理分成單獨的線程。
from threading import Thread
import cv2, time
class VideoStreamWidget(object):
def __init__(self, src=0):
self.capture = cv2.VideoCapture(src)
# Start the thread to read frames from the video stream
self.thread = Thread(target=self.update, args=())
self.thread.daemon = True
self.thread.start()
def update(self):
# Read the next frame from the stream in a different thread
while True:
if self.capture.isOpened():
(self.status, self.frame) = self.capture.read()
time.sleep(.01)
def show_frame(self):
# Display frames in main program
cv2.imshow('frame', self.frame)
key = cv2.waitKey(1)
if key == ord('q'):
self.capture.release()
cv2.destroyAllWindows()
exit(1)
if __name__ == '__main__':
video_stream_widget = VideoStreamWidget()
while True:
try:
video_stream_widget.show_frame()
except AttributeError:
pass
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