Это простой пример, который получает frame из cv2 и заменяет его на PhotoImage, который отображается на Canvas. Он использует after() для периодического запуска функции update_frame(), поэтому он не блокирует root.mainloop(), который должен выполняться постоянно.
Вам нужно будет запустить код из while True в функции update_frame() без используя while True
import tkinter as tk
from PIL import Image, ImageTk
import cv2
# --- functions ---
def update_frame():
ret, frame = cap.read()
image = Image.fromarray(frame)
photo.paste(image)
#description['text'] = 'new text'
root.after(10, update_frame) # update it again after 10ms
# --- main ---
cap = cv2.VideoCapture(0)
# get first frame
ret, frame = cap.read()
# - GUI -
root = tk.Tk()
image = Image.fromarray(frame)
photo = ImageTk.PhotoImage(image) # it has to be after `tk.Tk()`
canvas = tk.Canvas(root, width=photo.width(), height=photo.height())
canvas.pack(side='left', fill='both', expand=True)
canvas.create_image((0,0), image=photo, anchor='nw')
description = tk.Label(root, text="Place for description")
description.pack(side='right')
# - start -
update_frame() # update it first time
root.mainloop() # start program - this loop runs all time
# - after close -
cap.release()
Кстати: У меня есть пример с кнопками Play, Stop, Save Image: python - примеры / cv2 / tkinter-CV
РЕДАКТИРОВАТЬ: Я могу проверить это, но это может быть что-то вроде этого.
import face_recognition
import cv2
import numpy as np
import tkinter
#from tkinter import * # PEP8: `import *` is not preferred
#import PySimpleGUI as sg #
import xlsxwriter
import os
from PIL import ImageTk, Image
#from datetime import datetime;
import datetime
# --- functions ---
def process_frame():
global process_this_frame
# 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=0.25, fy=0.25)
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_small_frame = small_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)
name = "Unknown"
# use the known face with the smallest distance to the new face
face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
best_match_index = np.argmin(face_distances)
if matches[best_match_index]:
name = known_face_names[best_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 *= 4
right *= 4
bottom *= 4
left *= 4
# Draw a box around the face
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
# Draw a label with a name below the face,
cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
#Writes to spreadsheet and GUI
if name == "Jordan Terzian" and not Printed:
print("Jordan Terzian is Present")
Printed = True
worksheet.write('B6', 'Present')
elif name == "Daniel Pearce" and not Printed:
print("Daniel Pearce is Present")
Printed = True
worksheet.write('B5', 'Present')
elif name == "Ewan Krall" and not Printed:
print("Ewan Krall is Present")
Printed = True
worksheet.write('B4', 'Present')
elif name == "Norman Brosow" and not Printed:
print("Norman Brosow is Present")
Printed = True
worksheet.write('B3', 'Present')
elif name == "Mitchell Benson" and not Printed:
print("Michell Benson is Present")
Printed = True
worskheet.write('B2', 'Present')
description['text'] = name
image = Image.fromarray(frame)
photo.paste(image)
root.after(40, process_frame) # update it again after 40ms - it gives 25 FPS (1000ms/40ms=25)
# --- init ---
#Defines time
now = datetime.datetime.now().time()
#Setup for period segment of spreadsheetname
if now.hour<9:
name = "HomeRoom "
elif now.hour==9 and now.min<=50:
name = "Period1 "
elif now.hour==10 and now.min<=40:
name = "Period2 "
elif now.hour==11 and now.min<=50:
name = "Period3 "
elif now.hour==12 and now.min<=40:
name = "Period4 "
elif now.hour==14 and now.min<=10:
name = "Period5 "
elif now.hour<=15:
name = "Period6 "
else:
name = "Testing "
# to break loop
Printed = False
#Defines todays date #day/month/year-HourAM/PM
todays_date = str(datetime.datetime.now().strftime("%d-%m-%Y %I%p"))
#Sets up spreadsheet
workbook = xlsxwriter.Workbook(name + todays_date +'.xlsx')
worksheet = workbook.add_worksheet()
worksheet.write('A1', 'Name')
worksheet.write('B1', 'Attendance')
worksheet.write('A6', 'Jordan Terzian')
worksheet.write('B6', 'Absent')
worksheet.write('A5', 'Daniel Pearce')
worksheet.write('B5', 'Absent')
worksheet.write('A4', 'Ewan Krall')
worksheet.write('B4', 'Absent')
worksheet.write('A3', 'Norman Brosow')
worksheet.write('B3', 'Absent')
worksheet.write('A2', 'Mitchell Benson')
worksheet.write('B2', 'Absent')
# classmates
#jordan_image = face_recognition.load_image_file("jordan.jpg")
#jordan_face_encoding = face_recognition.face_encodings(jordan_image)[0]
#
#daniel_image = face_recognition.load_image_file("daniel.jpg")
#daniel_face_encoding = face_recognition.face_encodings(daniel_image)[0]
#
#ewan_image = face_recognition.load_image_file("ewan.jpg")
#ewan_face_encoding = face_recognition.face_encodings(ewan_image)[0]
#
#norman_image = face_recognition.load_image_file("norman.jpg")
#norman_face_encoding = face_recognition.face_encodings(norman_image)[0]
#
#mitch_image = face_recognition.load_image_file("mitch.jpg")
#mitch_face_encoding = face_recognition.face_encodings(mitch_image)[0]
# Create arrays of known face encodings and their names
#known_face_encodings = [
# jordan_face_encoding,
# daniel_face_encoding,
# ewan_face_encoding,
# norman_face_encoding,
# mitch_face_encoding,
#]
filenames = [
"jordan.jpg",
"daniel.jpg",
"ewan.jpg",
"norman.jpg",
"mitch.jpg"
]
known_face_encodings = []
for name in filenames:
image = face_recognition.load_image_file(name)
face_encoding = face_recognition.face_encodings(image)[0]
known_face_encodings.append(face_encoding)
known_face_names = [
"Jordan Terzian",
"Daniel Pearce",
"Ewan Krall",
"Norman Brosow",
"Mitchell Benson",
]
# Initialize variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
# --- main ---
# Webcam #0 (the default one)
video_capture = cv2.VideoCapture(0)
# get first frame to get size
ret, frame = cap.read()
# - GUI -
root = tk.Tk()
image = Image.fromarray(frame)
photo = ImageTk.PhotoImage(image) # it has to be after `tk.Tk()`
canvas = tk.Canvas(root, width=photo.width(), height=photo.height())
canvas.pack(side='left', fill='both', expand=True)
canvas.create_image((0,0), image=photo, anchor='nw')
description = tk.Label(root, text="Place for description")
description.pack(side='right')
# - start -
process_frame() # update it first time
root.mainloop() # start program - this loop runs all time
# --- end ---
# Release handle to the webcam, Closes webcam
video_capture.release()
#cv2.destroyAllWindows()
workbook.close()