I’m an enormous fan of interactive visualizations. As a pc imaginative and prescient engineer, I deal virtually day by day with picture processing associated duties and as a rule I’m iterating on an issue the place I would like visible suggestions to make choices. Let’s consider a quite simple picture processing pipeline with a single step that has some parameters to rework a picture:
How are you aware which parameters to regulate? Does the pipeline even work as anticipated? With out visualizing your output, you would possibly miss out on some key insights and make sub optimum decisions.
Generally merely exhibiting the output picture and/or some calculated metrics will be sufficient to iterate on the parameters. However I’ve discovered myself in lots of conditions the place a instrument can be immensely useful to iterate shortly and interactively on my pipeline. So on this article I’ll present you methods to work with easy built-in interactive parts from OpenCV in addition to methods to construct extra fashionable person interfaces for Pc Imaginative and prescient tasks utilizing customtkinter.
Stipulations
If you wish to comply with alongside, I like to recommend you to arrange your native setting with uv and set up the next packages:
uv add numpy opencv-Python pillow customtkinterAim
Earlier than we dive into the code of the mission, let’s shortly define what we need to construct. The appliance ought to use the webcam feed and permit the person to pick out several types of filters that shall be utilized to the stream. The processed picture needs to be proven in real-time within the window. A tough sketch of a possible UI would look as follows:
OpenCV – GUI
Let’s begin with a easy loop that fetches frames out of your webcam and shows them in an OpenCV window.
import cv2
cap = cv2.VideoCapture(0)
whereas True:
ret, body = cap.learn()
if not ret:
break
cv2.imshow("Video Feed", body)
key = cv2.waitKey(1) & 0xFF
if key == ord('q'):
break
cap.launch()
cv2.destroyAllWindows()Keyboard Enter
The only means so as to add interactivity right here, is by including keyboard inputs. For instance, we are able to cycle via totally different filters with the quantity keys.
...
filter_type = "regular"
whereas True:
...
if filter_type == "grayscale":
body = cv2.cvtColor(body, cv2.COLOR_BGR2GRAY)
elif filter_type == "regular":
cross
...
if key == ord('1'):
filter_type = "regular"
if key == ord('2'):
filter_type = "grayscale"
...Now you may swap between the conventional picture and the grayscale model by urgent the quantity keys 1 and a pair of. Let’s additionally shortly add a caption to the picture so we are able to truly see the identify of the filter we’re making use of.
Now we must be cautious right here: if you happen to check out the form of the body after the filter, you’ll discover that the dimensionality of the body array has modified. Do not forget that OpenCV picture arrays are ordered HWC (top, width, coloration) with coloration as BGR (inexperienced, blue, pink), so the 640×480 picture from my webcam has form (480, 640, 3).
print(filter_type, body.form)
# regular (480, 640, 3)
# grayscale (480, 640)Now as a result of the grayscale operation outputs a single channel picture, the colour dimension is dropped. If we now need to draw on high of this picture, we both have to specify a single channel coloration for the grayscale picture or we convert that picture again to the unique BGR format. The second possibility is a bit cleaner as a result of we are able to unify the annotation of the picture.
if filter_type == "grayscale":
body = cv2.cvtColor(body, cv2.COLOR_BGR2GRAY)
elif filter_type == "regular":
cross
if len(body.form) == 2: # Convert grayscale to BGR
body = cv2.cvtColor(body, cv2.COLOR_GRAY2BGR)Caption
I need to add a black border on the backside of the picture, on high of which the identify of the filter shall be proven. We are able to make use of the copyMakeBorder operate to pad the picture with a border coloration on the backside. Then we are able to add the textual content on high of this border.
# Add a black border on the backside of the body
border_height = 50
border_color = (0, 0, 0)
body = cv2.copyMakeBorder(body, 0, border_height, 0, 0, cv2.BORDER_CONSTANT, worth=border_color)
# Present the filter identify
cv2.putText(
body,
filter_type,
(body.form[1] // 2 - 50, body.form[0] - border_height // 2 + 10),
cv2.FONT_HERSHEY_SIMPLEX,
1,
(255, 255, 255),
2,
cv2.LINE_AA,
)That is how the output ought to look, and you may swap between the conventional and grayscale mode and the frames shall be captioned accordingly.
Sliders
Now as a substitute of utilizing the keyboard as enter methodology, OpenCV gives a fundamental trackbar slider UI ingredient. The trackbar must be initialized at first of the script. We have to reference the identical window as we shall be exhibiting our photographs in later, so I’ll create a variable for the identify of the window. Utilizing this identify, we are able to create the trackbar and let or not it’s a selector for the index within the record of filters.
filter_types = ["normal", "grayscale"]
win_name = "Webcam Stream"
cv2.namedWindow(win_name)
tb_filter = "Filter"
# def createTrackbar(trackbarName: str, windowName: str, worth: int, depend: int, onChange: _typing.Callable[[int], None]) -> None: ...
cv2.createTrackbar(
tb_filter,
win_name,
0,
len(filter_types) - 1,
lambda _: None,
)Discover how we use an empty lambda for the onChange callback, we are going to fetch the worth manually within the loop. Every thing else will keep the identical.
whereas True:
...
# Get the chosen filter kind
filter_id = cv2.getTrackbarPos(tb_filter, win_name)
filter_type = filter_types[filter_id]
...And voilà, we have now a trackbar to pick out our filter.
Now we are able to additionally simply add extra filters simply by extending our record and implementing every processing step.
filter_types = [
"normal",
"grayscale",
"blur",
"threshold",
"canny",
"sobel",
"laplacian",
]
...
if filter_type == "grayscale":
body = cv2.cvtColor(body, cv2.COLOR_BGR2GRAY)
elif filter_type == "blur":
body = cv2.GaussianBlur(body, ksize=(15, 15), sigmaX=0)
elif filter_type == "threshold":
grey = cv2.cvtColor(body, cv2.COLOR_BGR2GRAY)
_, thresholded_frame = cv2.threshold(grey, thresh=127, maxval=255, kind=cv2.THRESH_BINARY)
elif filter_type == "canny":
body = cv2.Canny(body, threshold1=100, threshold2=200)
elif filter_type == "sobel":
body = cv2.Sobel(body, ddepth=cv2.CV_64F, dx=1, dy=0, ksize=5)
elif filter_type == "laplacian":
body = cv2.Laplacian(body, ddepth=cv2.CV_64F)
elif filter_type == "regular":
cross
if body.dtype != np.uint8:
# Scale the body to uint8 if vital
cv2.normalize(body, body, 0, 255, cv2.NORM_MINMAX)
body = body.astype(np.uint8)
Fashionable GUI with CustomTkinter
Now I don’t find out about you however the present person interface doesn’t look very fashionable to me. Don’t get me mistaken, there may be some magnificence within the type of the interface, however I choose cleaner, extra fashionable designs. Plus we’re already on the restrict of what OpenCV gives out of the field by way of UI parts. Yep, no buttons, textual content fields, dropdowns, checkboxes or radio buttons and no customized layouts. So let’s see how we are able to rework the look and person expertise of this fundamental software to a recent and clear one.
So to get began, we first have to create a category for our app. We create two frames: the primary one accommodates our filter choice on the left aspect and the second wraps the picture show. For now, let’s begin with a easy placeholder textual content. Sadly there’s no out of the field opencv element from customtkinter straight, so we might want to shortly construct our personal within the subsequent few steps. However let’s first end the essential UI structure.
import customtkinter
class App(customtkinter.CTk):
def __init__(self) -> None:
tremendous().__init__()
self.title("Webcam Stream")
self.geometry("800x600")
self.filter_var = customtkinter.IntVar(worth=0)
# Body for filters
self.filters_frame = customtkinter.CTkFrame(self)
self.filters_frame.pack(aspect="left", fill="each", develop=False, padx=10, pady=10)
# Body for picture show
self.image_frame = customtkinter.CTkFrame(self)
self.image_frame.pack(aspect="proper", fill="each", develop=True, padx=10, pady=10)
self.image_display = customtkinter.CTkLabel(self.image_frame, textual content="Loading...")
self.image_display.pack(fill="each", develop=True, padx=10, pady=10)
app = App()
app.mainloop()
Filter Radio Buttons
Now that the skeleton is constructed, we are able to begin filling in our elements. For the left aspect, I shall be utilizing the identical record of filter_types to populate a gaggle of radio buttons to pick out the filter.
# Create radio buttons for every filter kind
self.filter_var = customtkinter.IntVar(worth=0)
for filter_id, filter_type in enumerate(filter_types):
rb_filter = customtkinter.CTkRadioButton(
self.filters_frame,
textual content=filter_type.capitalize(),
variable=self.filter_var,
worth=filter_id,
)
rb_filter.pack(padx=10, pady=10)
if filter_id == 0:
rb_filter.choose()
Picture Show Element
Now we are able to get began on the fascinating half, methods to get our OpenCV frames to indicate up within the picture element. As a result of there’s no built-in element, let’s create our personal primarily based on the CTKLabel. This permits us to show a loading textual content whereas the webcam stream is beginning up.
...
class CTkImageDisplay(customtkinter.CTkLabel):
"""
A reusable ctk widget widget to show opencv photographs.
"""
def __init__(
self,
grasp: Any,
) -> None:
self._textvariable = customtkinter.StringVar(grasp, "Loading...")
tremendous().__init__(
grasp,
textvariable=self._textvariable,
picture=None,
)
...
class App(customtkinter.CTk):
def __init__(self) -> None:
...
self.image_display = CTkImageDisplay(self.image_frame)
self.image_display.pack(fill="each", develop=True, padx=10, pady=10) To this point nothing has modified, we merely swapped out the present label with our customized class implementation. In our CTKImageDisplay class we are able to outline an operate to indicate a picture within the element, let’s name it set_frame.
import cv2
import numpy.typing as npt
from PIL import Picture
class CTkImageDisplay(customtkinter.CTkLabel):
...
def set_frame(self, body: npt.NDArray) -> None:
"""
Set the body to be displayed within the widget.
Args:
body: The brand new body to show, in opencv format (BGR).
"""
target_width, target_height = body.form[1], body.form[0]
# Convert the body to PIL Picture format
frame_rgb = cv2.cvtColor(body, cv2.COLOR_BGR2RGB)
frame_pil = Picture.fromarray(frame_rgb, "RGB")
ctk_image = customtkinter.CTkImage(
light_image=frame_pil,
dark_image=frame_pil,
measurement=(target_width, target_height),
)
self.configure(picture=ctk_image, textual content="")
self._textvariable.set("")Let’s digest this. First we have to understand how massive our picture element shall be, we are able to extract that info from the form property of our picture array. To show the picture in tkinter, we’d like a Pillow Picture kind, we can not straight use the OpenCV array. To transform an OpenCV array to Pillow, we first have to convert the colour house from BGR to RGB after which we are able to use the Picture.fromarray operate to create the Pillow Picture object. Subsequent we are able to create a CTKImage, the place we use the identical picture regardless of the theme and set the scale in line with our body. Lastly we are able to use the configure methodology to set the picture in our body. On the finish, we additionally reset the textual content variable to take away the “Loading…” textual content, though it might theoretically be hidden behind the picture.
To shortly take a look at this, we are able to set the primary picture of our webcam within the constructor. (We’ll see in a second why this isn’t such a good suggestion)
class App(customtkinter.CTk):
def __init__(self) -> None:
...
cap = cv2.VideoCapture(0)
_, frame0 = cap.learn()
self.image_display.set_frame(frame0)In the event you run this, you’ll discover that the window takes a bit longer to pop up, however after a brief delay it is best to see a static picture out of your webcam.
NOTE: In the event you don’t have a webcam prepared you can too simply use a neighborhood video file by passing the file path to the
cv2.VideoCaptureconstructor name.
Now this isn’t very thrilling, because the body doesn’t replace but. So let’s see what occurs if we strive to do that naively.
class App(customtkinter.CTk):
def __init__(self) -> None:
...
cap = cv2.VideoCapture(0)
whereas True:
ret, body = cap.learn()
if not ret:
break
self.image_display.set_frame(body)Nearly the identical as earlier than, besides now we run the body loop as we did within the earlier chapter with the OpenCV GUI. In the event you run this, you will notice… precisely nothing. The window by no means reveals up, since we’re creating an infinite loop within the constructor of the app! That is additionally the rationale why this system solely confirmed up after a delay within the earlier instance, the opening of the Webcam stream is a blocking operation, and the occasion loop for the window can not run, so it doesn’t present up but.
So let’s repair this by including a barely higher implementation that permits the gui occasion loop to run whereas we additionally replace the body each every now and then. We are able to use the after methodology of tkinter to schedule a operate name whereas yielding the method in the course of the wait time.
...
self.cap = cv2.VideoCapture(0)
self.after(10, self.update_frame)
def update_frame(self) -> None:
"""
Replace the displayed body.
"""
ret, body = self.cap.learn()
if not ret:
return
self.image_display.set_frame(body)
self.after(10, self.update_frame)
So now we nonetheless arrange the webcam stream within the constructor, so we haven’t solved that drawback but. However a minimum of we are able to see a steady stream of frames in our picture element.
Making use of Filters
Now that the body loop is operating. we are able to re-implement our filters from the start and apply them to our webcam stream. Within the update_frame operate, we are able to examine the present filter variable and apply the corresponding filter operate.
def update_frame(self) -> None:
...
# Get the chosen filter kind
filter_id = self.filter_var.get()
filter_type = filter_types[filter_id]
if filter_type == "grayscale":
body = cv2.cvtColor(body, cv2.COLOR_BGR2GRAY)
elif filter_type == "blur":
body = cv2.GaussianBlur(body, ksize=(15, 15), sigmaX=0)
elif filter_type == "threshold":
grey = cv2.cvtColor(body, cv2.COLOR_BGR2GRAY)
_, body = cv2.threshold(grey, thresh=127, maxval=255, kind=cv2.THRESH_BINARY)
elif filter_type == "canny":
body = cv2.Canny(body, threshold1=100, threshold2=200)
elif filter_type == "sobel":
body = cv2.Sobel(body, ddepth=cv2.CV_64F, dx=1, dy=0, ksize=5)
elif filter_type == "laplacian":
body = cv2.Laplacian(body, ddepth=cv2.CV_64F)
elif filter_type == "regular":
cross
if body.dtype != np.uint8:
# Scale the body to uint8 if vital
cv2.normalize(body, body, 0, 255, cv2.NORM_MINMAX)
body = body.astype(np.uint8)
if len(body.form) == 2: # Convert grayscale to BGR
body = cv2.cvtColor(body, cv2.COLOR_GRAY2BGR)
self.image_display.set_frame(body)
self.after(10, self.update_frame)And now we’re again to the complete performance of the appliance, you may choose any filter on the left aspect and it is going to be utilized in real-time to the webcam feed!
Multithreading and Synchronization
Now though the appliance runs as is, there are some issues with the present means we run our body loop. At present every thing runs in a single thread, the principle GUI thread. For this reason at first, we don’t instantly see the window pop up, our webcam initialization blocks the principle thread. Now think about, if we did some heavier picture processing, perhaps operating the pictures via neural community, you wouldn’t need your person interface to all the time be blocked whereas the community is operating inference. It will result in a really unresponsive person expertise when clicking the UI parts!
A greater method to deal with this in our software is to separate the picture processing from the person interface. Typically that is virtually all the time a good suggestion to separate your GUI logic from any kind of non-trivial processing. So in our case, we are going to run a separate thread that’s answerable for the picture loop. It would learn the frames from the webcam stream and apply the filters.
NOTE: Python threads should not “actual” threads in a way that they don’t have the potential to run on totally different logical cpu cores and therefore won’t actually run in parallel. In Python multithreading the context will swap between the threads, however because of the GIL, the worldwide interpreter lock, a single python course of can solely run one bodily thread. If you would like “actual” parallel processing, you will want to make use of multiprocessing. Since our course of right here shouldn’t be CPU certain however truly I/O certain, multithreading suffices.
class App(customtkinter.CTk):
def __init__(self) -> None:
...
self.webcam_thread = threading.Thread(goal=self.run_webcam_loop, daemon=True)
self.webcam_thread.begin()
def run_webcam_loop(self) -> None:
"""
Run the webcam loop in a separate thread.
"""
self.cap = cv2.VideoCapture(0)
if not self.cap.isOpened():
return
whereas True:
ret, body = self.cap.learn()
if not ret:
break
# Filters
...
self.image_display.set_frame(body)In the event you run this, you’ll now see that our window opens up instantly and we even see our loading textual content whereas the webcam stream is opening up. Nonetheless, as quickly because the stream begins, the frames begin to flicker. Relying on a variety of components, you would possibly expertise totally different visible artifacts or errors at this stage.
Warning: flashing picture
Now why is that this taking place? The issue is that we’re concurrently making an attempt to replace the brand new body whereas the inner refresh loop of the person interface may be utilizing the knowledge of the array to attract it on the display screen. They’re each competing for a similar body array.
It’s typically not a good suggestion to straight replace the UI parts from a special thread, in some frameworks this would possibly even be prevented and can elevate exceptions. In Tkinter, we are able to do it, however we are going to get bizarre outcomes. We want some kind of synchronization between our threads. That’s the place the Queue comes into play.
You’re most likely aware of queues from the grocery retailer or theme parks. The idea of the queue right here could be very comparable: the primary ingredient that goes into the queue additionally leaves first (First In First Out).
On this case, we truly simply desire a queue with a single ingredient, a single slot queue. The queue implementation in Python is thread-safe, that means we are able to put and get objects from the queue from totally different threads. Excellent for our use case, the processing thread will put the picture arrays to the queue and the GUI thread will attempt to get a component, however not block if the queue is empty.
class App(customtkinter.CTk):
def __init__(self) -> None:
...
self.queue = queue.Queue(maxsize=1)
self.webcam_thread = threading.Thread(goal=self.run_webcam_loop, daemon=True)
self.webcam_thread.begin()
self.frame_loop_dt_ms = 16 # ~60 FPS
self.after(self.frame_loop_dt_ms, self._update_frame)
def _update_frame(self) -> None:
"""
Replace the body within the picture show widget.
"""
strive:
body = self.queue.get_nowait()
self.image_display.set_frame(body)
besides queue.Empty:
cross
self.after(self.frame_loop_dt_ms, self._update_frame)
def run_webcam_loop(self) -> None:
...
whereas True:
...
self.queue.put(body)Discover how we transfer the direct name to the set_frame operate from the webcam loop which runs in its personal thread to the _update_frame operate that’s operating on the principle thread, repeatedly scheduled in 16ms intervals.
Right here it’s vital to make use of the get_nowait operate in the principle thread, in any other case if we might use the get operate, we might be blocking there. This name does not block, however raises a queue.Empty exception if there’s no ingredient to fetch so we have now to catch this and ignore it. Within the webcam loop, we are able to use the blocking put operate as a result of it doesn’t matter that we block the run_webcam_loop, there’s nothing else needing to run there.
And now every thing is operating as anticipated, no extra flashing frames!
Conclusion
Combining a UI framework like Tkinter with OpenCV permits us to construct fashionable wanting functions with an interactive graphical person interface. Because of the UI operating in the principle thread, we run the picture processing in a separate thread and synchronize the info between the threads utilizing a single-slot queue. You will discover a cleaned up model of this demo within the repository beneath with a extra modular construction. Let me know if you happen to construct one thing fascinating with this method. Take care!
Checkout the complete supply code within the GitHub repo:
https://github.com/trflorian/ctk-opencv
