我正在使用一个物理实验系统，使用相机实时跟踪其演变。具体来说，我触发相机以 2.5Hz 的速率检索图像，因此系统的效率对于跟上实验的进度至关重要。
我当前的程序会在专用文件夹
pathimg_round每个图像批次的平均亮度相对于
img_round目前，当我在静态数据上运行该程序时，效果很好。然而，当我尝试在预期的处理实验设置中运行它时，其中图像被主动添加到文件夹中，我得到了绘图中亮度的错误值。
总的来说，我担心我的代码效率不高，我想尽可能优化它。
请找到以下代码：
import os
import cv2
import numpy as np
import pyqtgraph as pg
from scipy.optimize import minimize_scalar
from pyqtgraph.Qt import QtCore, QtGui, QtWidgets
import time
center = (0, 0)
radius = (0)
is_dragging_center = False
is_dragging_radius = False
global avg_brightness_per_img_round
avg_brightness_per_img_round = 0
img_round = 5
run_count = 0
brightness_history = []
std_history = []
func_history = []
global scatter_item
scatter_item = None
def update_display_image():
global resized_image
if resized_image is not None:
display_image = resized_image.copy()
cv2.circle(display_image, center, radius, (0, 255, 0), 2)
cv2.circle(display_image, center, 5, (0, 0, 255), thickness=cv2.FILLED)
class UpdateDisplaySignal(QtCore.QObject):
update_display_signal = QtCore.pyqtSignal()
update_display_signal_obj = UpdateDisplaySignal()
update_display_signal_obj.update_display_signal.connect(update_display_image)
def on_mouse(event, x, y, flags, param):
global center, radius, is_dragging_center, is_dragging_radius
if event == cv2.EVENT_LBUTTONDOWN:
if np.sqrt((x - center[0]) ** 2 + (y - center[1]) ** 2) < 20:
is_dragging_center = True
else:
is_dragging_radius = True
elif event == cv2.EVENT_LBUTTONUP:
is_dragging_center = False
is_dragging_radius = False
elif event == cv2.EVENT_MOUSEMOVE:
if is_dragging_center:
center = (x, y)
elif is_dragging_radius:
radius = int(np.sqrt((x - center[0]) ** 2 + (y - center[1]) ** 2))
app = QtWidgets.QApplication([])
pw = pg.PlotWidget(title='Mean Brightness vs image round')
pw.setLabel('left', 'Mean Brightness')
pw.setLabel('bottom', 'Image round')
scatter = pg.ScatterPlotItem(size=10, pen=pg.mkPen(None), brush=pg.mkBrush(255, 0, 0, 120))
line = pg.PlotDataItem(pen=pg.mkPen(color=(0,0,255), width=2))
pw.addItem(line)
pw.addItem(scatter)
def update_scatter():
global scatter_item
indices, values = zip(*enumerate(brightness_history, start=1))
x = list(indices)
y = list(values)
if scatter_item is None:
scatter_item = pg.ScatterPlotItem(size=10, pen=pg.mkPen(None), brush=pg.mkBrush(255, 0, 0, 120))
pw.addItem(scatter_item)
if isinstance(x, int):
x = [x]
if len(x) > 1:
line.setData(x=x, y=y)
scatter.setData(x=x, y=y, symbol='o', size=10, pen=pg.mkPen(None), brush=pg.mkBrush(255, 0, 0, 120))
for i, (xi, yi) in enumerate(zip(x, y)):
label = pg.TextItem(text=f'{yi:.2f}', anchor=(0, 0))
label.setPos(xi, yi)
pw.addItem(label)
win = QtWidgets.QMainWindow()
win.setCentralWidget(pw)
win.show()
path = r'C:\Users\blehe\Desktop\Betatron\images'
def calc_xray_count(image_path, center, radius):
original_image = cv2.imread(image_path, cv2.IMREAD_ANYDEPTH)
median_filtered_image = cv2.medianBlur(original_image, 5)
mask = np.zeros(original_image.shape, dtype=np.uint8)
cv2.circle(mask, center, radius, 255, thickness=cv2.FILLED)
median_filtered_image += 1 # Avoid not counting black pixels in image
result = cv2.bitwise_and(median_filtered_image, median_filtered_image, mask=mask)
pixel_count = np.count_nonzero(result)
img_brightness_sum = np.sum(result)
img_var = np.var(result)
if (pixel_count > 0):
img_avg_brightness = (img_brightness_sum/pixel_count) -1 # Subtract back to real data
else:
img_avg_brightness = 0
return img_avg_brightness, img_var
#-----------------------------------------------------------------------
image_files = []
for filename in os.listdir(path):
if filename.endswith('.TIF'):
image_files.append(os.path.join(path, filename))
first_image_path = image_files[0]
image = cv2.imread(first_image_path)
scale_percent = 60
width = int(image.shape[1] * scale_percent / 100)
height = int(image.shape[0] * scale_percent / 100)
dim = (width, height)
gray_img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
colored_image = cv2.applyColorMap(gray_img, cv2.COLORMAP_PINK)
resized_image = cv2.resize(colored_image, dim, interpolation=cv2.INTER_AREA)
center = (resized_image.shape[1] // 2, resized_image.shape[0] // 2)
radius = min(resized_image.shape[1] // 3, resized_image.shape[0] // 3)
cv2.namedWindow("Adjust the circle (press 'Enter' to proceed)")
cv2.setMouseCallback("Adjust the circle (press 'Enter' to proceed)", on_mouse)
while True:
display_image = resized_image.copy()
cv2.circle(display_image, center, radius, (0, 255, 0), 2)
cv2.circle(display_image, center, 5, (0, 0, 255), thickness=cv2.FILLED)
cv2.imshow("Adjust the circle (press 'Enter' to proceed)", display_image)
key = cv2.waitKey(1) & 0xFF
if key == 13:
break
cv2.destroyAllWindows()
center = (int(center[0] / scale_percent * 100), int(center[1] / scale_percent * 100))
radius = int(radius / scale_percent * 100)
img_round_brightness_sum = 0
img_round_var_sum = 0
def process_images():
global run_count, img_round_brightness_sum, img_round_var_sum
while run_count < len(os.listdir(path)):
for i, image_path in enumerate(image_files, start=1):
img_avg_brightness, img_var = calc_xray_count(image_path, center, radius)
img_round_brightness_sum += img_avg_brightness
img_round_var_sum += img_var
run_count += 1
if run_count % img_round == 0:
avg_brightness_per_img_round = (img_round_brightness_sum/img_round)
deviation_per_img_round = np.sqrt(img_round_var_sum/img_round)
brightness_history.append(avg_brightness_per_img_round)
std_history.append(deviation_per_img_round)
update_scatter()
img_round_brightness_sum = 0
img_round_var_sum = 0
img_avg_brightness = 0
img_var = 0
QtCore.QCoreApplication.processEvents()
QtCore.QThread.msleep(100)
if __name__ == "__main__":
timer = QtCore.QTimer()
timer.timeout.connect(process_images)
timer.start(100)
app.exec_()
我真的很感激任何帮助。谢谢您。
很难理解您编写代码的方式想要实现的目标，所以请清理它。这是一个示例和一些改进建议，但我很快就放弃了重构整个事情。您可以对此代码进行大量清理和重构，例如将函数分解为更小的单元，然后链接这些函数。使用类来保存处理器的状态，而不是到处依赖全局变量等。
import numpy as np
import matplotlib.pyplot as plt
import cv2
class Processor:
def __init__(self,img):
self.img = img # Self.img can act as your image buffer
self.center = (75,75) # I hard coded values but you can calculate and initialize center here
self.radius = 20
self.is_dragging_center = False # Initialize all of your global variables here
self.is_dragging = False
self.dragging_center = False
def draw_circles(self, img): #Either explicitly pass the image or use self.img
cv2.circle(img, self.center, self.radius, (0, 255, 0), 2)
cv2.circle(img, self.center, 5, (0, 0, 255), thickness=cv2.FILLED)
def resize_img(self,img, scale_percent=60):
width = img.shape[1] * scale_percent // 100
height = img.shape[0] * scale_percent // 100
return cv2.resize(img, (width, height), interpolation=cv2.INTER_AREA)
def transform_color(self, img, cma=cv2.COLORMAP_PINK):
return cv2.applyColorMap(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cmap)
def on_mouse(self, event, x, y, flags, param):
cx,cy = self.center
if event == cv2.EVENT_LBUTTONDOWN:
# <20 should probably not be hard coded?
# if you want performance do you actually care about dragging in a perfectly circular radius?
# For example using "if abs(x-cx) + abs(y-cy) < 20" creates a diamond pattern but it's 3 to 4x faster
# you can also simplify this to ((x - cx) ** 2 + (y - cy) ** 2) < 400
# "is_dragging_radius" looks to be : "not is_dragging_center", do you really need two variables here?
# self.is_dragging = True
# self.dragging_center = ((x - cx) ** 2 + (y - cy) ** 2) < 400
if np.sqrt((x - cx) ** 2 + (y - cy) ** 2) < 20:
self.is_dragging_center = True
else:
self.is_dragging_radius = True
# All of the "elif"s can be simplified to just "if"s
elif event == cv2.EVENT_LBUTTONUP:
# self.is_dragging = False
self.is_dragging_center = False
self.is_dragging_radius = False
# if event == cv2.EVENT_MOUSEMOVE and self.is_dragging:
# if dragging_center:
# center = (x, y)
# else:
# radius = int(np.sqrt((x - center[0]) ** 2 + (y - center[1]) ** 2))
elif event == cv2.EVENT_MOUSEMOVE:
if is_dragging_center:
center = (x, y)
elif is_dragging_radius:
radius = int(np.sqrt((x - center[0]) ** 2 + (y - center[1]) ** 2))
p = Processor(np.zeros((150,150,3), dtype=np.uint8))
p.draw_circles(p.img)
plt.figure()
plt.imshow(p.img)
plt.show()
关于性能问题以及如何在将图像保存到磁盘时处理图像，您可以执行以下操作：
from concurrent.futures import ThreadPoolExecutor as TPE, wait
# below imports are only used in the second example
from collections import deque
from threading import Thread, Event
# below imports are just used for this example, you can ignore them
import time
from random import random, choice
from string import ascii_letters, digits
from threading import Lock
def generator(max_delay): # Acts as the data generator, i.e: a camera in your case
alphabet = list(ascii_letters)+list(digits)
while True:
yield ''.join(choice(alphabet) for i in range(20))
time.sleep(random()*max_delay)
def save(x, lock):
time.sleep(random()*0.5)
with lock:
print(f'saved {x}')
def process(x, lock):
time.sleep(random()*0.8)
with lock:
print(f'Processed {x} to {x[:10].swapcase()}')
def producer(generator, queue, event, lock):
for x in generator:
if event.is_set():
break
queue.append(x)
with lock:
print(f'{"*"*20} Done prodcuing. Shutting down. {"*"*20}')
lock = Lock() # Lock is used to make sure multiple threads don't print at the same time
##############################
##############################
### FIRST EXAMPLE ###
##############################
##############################
# In this example we don't mind if we miss captured frames since we don't have a large enough memory to store
# all of the incoming data and our processing might be too slow to process in real time
g = generator(0.3)
with TPE() as executor:
start = time.time()
while (time.time()-start)<5: # Run experiment for 5 seconds
x = next(g) # Get the next input form "camera"
# Save the image and process it in parallel (you may want to pass a copy to be processed)
futures = [executor.submit(save,x,lock), executor.submit(process,x,lock)]
# Wait for saving and processing to finish before processing the next frame
wait(futures)
print('#'*50)
print('Done processing and saving. Shutting down.')
print('_'*100)
print('_'*100)
print('_'*100)
print('_'*100)
##############################
##############################
### SECOND EXAMPLE ###
##############################
##############################
# This second example deals with a case that your input rate is higher than your processing throughput
# If you can process everything in real time OR if you don't mind missing frames, then you can ignore this part.
# This is only advised if you have enough ram available to hold all the captured data
q = deque()
shutdown_event = Event()
t = Thread(target=producer, args=(generator(0.1), q, shutdown_event, lock))
t.start()
with TPE() as executor:
start = time.time()
while (time.time()-start)<5: # Run experiment for 5 seconds
# If queue is empty, wait for it to be populated (in case that input rate is lower than processing throughput)
while len(q)==0:
pass
x = q.popleft() # Get the next "frame" from the queue
# Save the image and process it in parallel (you may want to pass a copy to be processed)
futures = [executor.submit(save,x,lock), executor.submit(process,x,lock)]
# Wait for saving and processing to finish before processing the next frame
wait(futures)
print('#'*50)
# now that your experiment is done, shutdown the producer
shutdown_event.set()
# Keep processing until the queue is empty
while len(q)!=0:
x = q.popleft() # Get the next input form "camera"
# Save the image and process it in parallel (you may want to pass a copy to be processed)
futures = [executor.submit(save,x,lock), executor.submit(process,x,lock)]
# Wait for saving and processing to finish before processing the next frame
wait(futures)
print('#'*50)
print('Done processing and saving. Shutting down.')
t.join()
请注意，即使实验结束并且您已停止捕获帧，第二个示例也会继续处理。它还输出更多的数据，因为没有丢失任何输入（并且
max_delay