Good organization is the key to a successful project I am happy you enjoyed the video! 😄🙌

Thank you! So glad you enjoy the content! 😃🙌

Could you tell me the installation process

Can u pls help me out? Please

Thank you for your support! 😃🙌💪

Glad the content is helpful! 😃🙌

You are welcome!! 😃

Thank you! Glad you enjoyed it! 😃💪

You are welcome! Glad it was helpful! 😃

Please send your github link please

I got lot of error bro please please please please

Thank you, Arif! I am happy you enjoyed it. 😃🙌

bro can you send me the file for your project

@@ijaspr5486 like the whole file?

Could you share your GitHub link of your project?

@@e2mnaturals442 yes like github code or i give you my social media id

can you share your code? I'm having somre errors, while I try do identify the letters. Also, in your code, could you do with signs with both hands and with movements? @e2mnaturals442

can you help me. when I trained only one gesture nothing else, but the system detects untrained gestures as the trained gesture why? any idea

can you please share the changed code

i think guys to solve this problem we had to tell the collect data script to save just frames where he could detect our hands else we will store bad models that will ends with this getting errors like "inhomogeneous shapes" , i actually try to solved this problem by not moving my hand when collecting data and making my model else you can try this code to check your images that are stored This script will only print the paths of the images that are deleted due to no hands being detected. It won't display any image windows. ##########################################" import os import cv2 import mediapipe as mp def process_and_show(image_path, mp_drawing): mp_hands = mp.solutions.hands hands = mp_hands.Hands() # Read the image image = cv2.imread(image_path) image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # Detect hands and landmarks results = hands.process(image_rgb) if not results.multi_hand_landmarks: print(f"Deleted image: {image_path}") # Delete the image with no hands detected os.remove(image_path) # Path to your data folder containing subfolders data_folder = "data" mp_drawing = mp.solutions.drawing_utils mp_drawing_styles = mp.solutions.drawing_styles # Iterate through subfolders for folder_name in os.listdir(data_folder): folder_path = os.path.join(data_folder, folder_name) if os.path.isdir(folder_path): print(f"Checking images in folder: {folder_name}") # Iterate through images in the folder for filename in os.listdir(folder_path): if filename.endswith(".jpg") or filename.endswith(".png"): image_path = os.path.join(folder_path, filename) process_and_show(image_path, mp_drawing)

I understood the problem but can't really put it in the program. could you explain it please?

You are welcome! 😃 Glad you enjoyed it!! 🙂🙌

Glad to hear it is helpful! 😃🙌

size.width>0 && size.height>0 in function 'cv::imshow' error sir

I will try to. 🙌

Hey Hayat, I am glad you found it helpful! 😄 Do you mean making a video about how to be strategic when starting a project and choose the most promising approach? Sure, I can do a video about problem solving strategies! 😃🙌

Do you have file word report ?

🤞😀 Good luck with your project, Martin! 🙌

@@ComputerVisionEngineer Sir i have an error "ValueError: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. ", what can be the problem, im trying to classfy all the alphabet letters, your help will be highly appreciated.

Thanks a lot!! How did you notice that this was the issue?

thanks a lot bro!!!

It worked ! Thanks

Do you mean using Yolo for object detection instead of mediapipe + Scikit learn? It can be done. You just need to train it. I did it with mediapipe + Scikit learn only for simplicity, and I think it also results in a more robust classifier. 🙌

You are welcome! I will try to make a video about it. 🙌

Hi, did you get it?

Sure. Do you think you can take an approach as I do in the video?

No particular reason why I used a Random Forest, I think pretty much any other classifier would have a similar performance in this case.

@@ComputerVisionEngineer Thanks felipe!!

Hey, thank you! I am using mediapipe as a hand detector and landmark detector and a Random Forest classifier as sign classifier. 🙌

Hey, limitation in terms of possible symbols? I would say any static symbol made with only one hand.

Hey George! Yeah, I sent that change in a new commit. It makes the solution more robust, you could think about it as a way of 'normalization'. This makes the classifier learn better than the (x, y) position of each landmark is not that important, the distance of each landmark to each other landmark is what matters most! 😃💪

@@ComputerVisionEngineer Thanks a lot for the answer! I thought it has something to do with the mediapipe library and is a must, but it actually makes sense to be some kind of normalization. Thanks for you time!

Hey Sourabh, it sounds like a complex and very cool project! I would start by saving all the symbols you detect, its confidence score, and the duration of time you detect them so you can analyze this info later on. This is going to help you to understand the problem a little better and also it is going to help you to define rules in order to achieve your goal. 😃💪

Hi Sourabh were you able to make it if yes could you please share some update or code for the same

You would need to make some edits in order to use it with both hands but I guess it would work, yes. Regarding the performance, yeah you could train it and improve it so it can be used at a production level. 🙌

thanks @@ComputerVisionEngineer 😁i'll try and see if it works out

Hey @@ComputerVisionEngineer , its not working efficiently for the british sign lang , maybe because it uses both hands , do you have any suggestions on how i can build up my project , it'll be a huge help , thanks

am also thinking the same. The images seem no to be there

Hey, you can create the model yourself following the steps I describe in the video. 😃🙌

The thing about Apple is that MacOS often puts a file called ".DS_Store" in the directory which stores some information. In your code where you iterate over folders, compare the name with ".DS_Store" and simply skip it

did you fix this error?

Yes, you could try with video classification. 🙌

​@@ComputerVisionEngineer how to insert video type in pycharm?

I hope you can help us..thank you

Is there a front - end that can connect in pycharm?

I am looking at the Indian sign language alphabet and I see some characters are done with 2 hands and others with 1 hand. In order to do something based on landmarks as we did on this video you would have to train 2 classifiers, one of them taking as input the landmarks of one hand only (as we did on the video) and the other classifier taking as input the landmarks of both hands. Then some logic to apply one classifier or the other one depending on how many hands appear on the frame. Or, you can just follow a different approach and train an image classifier taking the crop of the hand/s. 💪🙌

Hi ! Have you completed working on this project? Did it worked ?

😃 That is soooo cool! I am happy to help you guys. 😊🙌

Have you manage to Train the model with all alphabet letters

Try using the method 'predict_proba' instead of 'predict'. You wil get a probability vector for all the classes. Taking the largest number will give you the confidence value you are looking for. 💪💪

@@ComputerVisionEngineer Thanks a lot you are amazing !!! 😃

@@prathamupadhyay1265 bhai if u dont mind kya app apke code ki zip file mujhe share kar skte hai, coz im getting many errors and i have tried many steps but kuch ho nahi raha hai. PLZ!!!!!!

plz bhai

@@yashanchule9641 GitHub link is there..or have you tried that too?!

You may try to contact me in our discord.

Hey, yeah I think that would work for a video for example, where you have already computed the object detection and classification for that video beforehand. If you are detecting objects live, in a webcam, you would need an object detector and an image classifier. I don't think you could load the annotations from a txt file in yolo fomat because you are capturing the data live. 💪💪

@@ComputerVisionEngineer Thank you for your response! I may have made my last comment a bit confusing. Basically, I am trying to adapt this code to eliminate the media pipe elements. I already have a dataset of still images and corresponding text files holding the bounding box coordinates. I am using scikit to train still. But I keep getting errors when i run the training script. Your help would be much appreciated

@daisybristow7036 oh I see, yes sure you could use something like yolov8 and replace both mediapipe and Scikit learn. Once you collected the data, you can just train yolov8 following the steps in my video on how to train yolov8 on a custom dataset. Then, for inference, take a look at my video on object detection + tracking. 🙌

@@ComputerVisionEngineer This would be an optimal solution, however I am required to use scikit to train not yolo. Would it be ok if I could explain it further beyond the comment section of this video? 😵‍💫

@@daisybristow7036 you could use both Scikit learn and yolov8, so you use Scikit learn to validate the class you got with yolov8, that would be a super robust solution! 😃 I may create a discord or something similar later on, for now it is only the comments section of my videos.

Thank you! I haven't tried to do it an edge device, I don't have any suggestions. 🙌

hello I am having the same problem. Could be share your solution if you find one. Thank you!

i think guys to solve this problem we had to tell the collect data script to save just frames where he could detect our hands else we will store bad models that will ends with this getting errors like "inhomogeneous shapes" , i actually try to solved this problem by not moving my hand when collecting data and making my model else you can try this code to check your images that are stored This script will only print the paths of the images that are deleted due to no hands being detected. It won't display any image windows. ##########################################" import os import cv2 import mediapipe as mp def process_and_show(image_path, mp_drawing): mp_hands = mp.solutions.hands hands = mp_hands.Hands() # Read the image image = cv2.imread(image_path) image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # Detect hands and landmarks results = hands.process(image_rgb) if not results.multi_hand_landmarks: print(f"Deleted image: {image_path}") # Delete the image with no hands detected os.remove(image_path) # Path to your data folder containing subfolders data_folder = "data" mp_drawing = mp.solutions.drawing_utils mp_drawing_styles = mp.solutions.drawing_styles # Iterate through subfolders for folder_name in os.listdir(data_folder): folder_path = os.path.join(data_folder, folder_name) if os.path.isdir(folder_path): print(f"Checking images in folder: {folder_name}") # Iterate through images in the folder for filename in os.listdir(folder_path): if filename.endswith(".jpg") or filename.endswith(".png"): image_path = os.path.join(folder_path, filename) process_and_show(image_path, mp_drawing)

Yes, by the way we are doing it in this tutorial, it should work if you change the background. 🙌

@@ComputerVisionEngineer i think guys to solve this problem we had to tell the collect data script to save just frames where he could detect our hands else we will store bad models that will ends with this getting errors like "inhomogeneous shapes" , i actually try to solved this problem by not moving my hand when collecting data and making my model else you can try this code to check your images that are stored This script will only print the paths of the images that are deleted due to no hands being detected. It won't display any image windows. ##########################################" import os import cv2 import mediapipe as mp def process_and_show(image_path, mp_drawing): mp_hands = mp.solutions.hands hands = mp_hands.Hands() # Read the image image = cv2.imread(image_path) image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # Detect hands and landmarks results = hands.process(image_rgb) if not results.multi_hand_landmarks: print(f"Deleted image: {image_path}") # Delete the image with no hands detected os.remove(image_path) # Path to your data folder containing subfolders data_folder = "data" mp_drawing = mp.solutions.drawing_utils mp_drawing_styles = mp.solutions.drawing_styles # Iterate through subfolders for folder_name in os.listdir(data_folder): folder_path = os.path.join(data_folder, folder_name) if os.path.isdir(folder_path): print(f"Checking images in folder: {folder_name}") # Iterate through images in the folder for filename in os.listdir(folder_path): if filename.endswith(".jpg") or filename.endswith(".png"): image_path = os.path.join(folder_path, filename) process_and_show(image_path, mp_drawing)

Hey, what file / line triggers this error?

Hey, I didn't use any research paper for this project. 🙌

@@ComputerVisionEngineer alright then...but do you have any idea which one would be similar or near to this?

@@054_vishwadhimar4 hi, did you get the research paper?

@@aakritityagi7203 no I did not actually... thankfully my mentor did not force me to find one and accepted multiple youtube.videos as references

If you're getting this, that means you're showing something else that isn't in the data. Only show what you've captured. Or else simply increase number of classes and take different pictures from different angles.

i think guys to solve this problem we had to tell the collect data script to save just frames where he could detect our hands else we will store bad models that will ends with this getting errors like "inhomogeneous shapes" , i actually try to solved this problem by not moving my hand when collecting data and making my model else you can try this code to check your images that are stored This script will only print the paths of the images that are deleted due to no hands being detected. It won't display any image windows. ##########################################" import os import cv2 import mediapipe as mp def process_and_show(image_path, mp_drawing): mp_hands = mp.solutions.hands hands = mp_hands.Hands() # Read the image image = cv2.imread(image_path) image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # Detect hands and landmarks results = hands.process(image_rgb) if not results.multi_hand_landmarks: print(f"Deleted image: {image_path}") # Delete the image with no hands detected os.remove(image_path) # Path to your data folder containing subfolders data_folder = "data" mp_drawing = mp.solutions.drawing_utils mp_drawing_styles = mp.solutions.drawing_styles # Iterate through subfolders for folder_name in os.listdir(data_folder): folder_path = os.path.join(data_folder, folder_name) if os.path.isdir(folder_path): print(f"Checking images in folder: {folder_name}") # Iterate through images in the folder for filename in os.listdir(folder_path): if filename.endswith(".jpg") or filename.endswith(".png"): image_path = os.path.join(folder_path, filename) process_and_show(image_path, mp_drawing)

Do not give 2 hands at the same on your camera

were you able to find a solution to your problem?

hi were you able to solve this? i used import matplotlib.pyplot as plt import seaborn as sns # class names class_names = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z'] # Plot the confusion matrix plt.figure(figsize=(15, 15)) sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=class_names, yticklabels=class_names) plt.title('Confusion Matrix') plt.xlabel('Predicted') plt.ylabel('True') plt.show()

Did you solve this error??

@@lal6797 if you have solution please inform me

Once the model is trained, anyone can sign the alphabet and it will show what sign they are doing.

did you figure it out?

i think guys to solve this problem we had to tell the collect data script to save just frames where he could detect our hands else we will store bad models that will ends with this getting errors like "inhomogeneous shapes" , i actually try to solved this problem by not moving my hand when collecting data and making my model else you can try this code to check your images that are stored This script will only print the paths of the images that are deleted due to no hands being detected. It won't display any image windows. ##########################################" import os import cv2 import mediapipe as mp def process_and_show(image_path, mp_drawing): mp_hands = mp.solutions.hands hands = mp_hands.Hands() # Read the image image = cv2.imread(image_path) image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # Detect hands and landmarks results = hands.process(image_rgb) if not results.multi_hand_landmarks: print(f"Deleted image: {image_path}") # Delete the image with no hands detected os.remove(image_path) # Path to your data folder containing subfolders data_folder = "data" mp_drawing = mp.solutions.drawing_utils mp_drawing_styles = mp.solutions.drawing_styles # Iterate through subfolders for folder_name in os.listdir(data_folder): folder_path = os.path.join(data_folder, folder_name) if os.path.isdir(folder_path): print(f"Checking images in folder: {folder_name}") # Iterate through images in the folder for filename in os.listdir(folder_path): if filename.endswith(".jpg") or filename.endswith(".png"): image_path = os.path.join(folder_path, filename) process_and_show(image_path, mp_drawing)

@michaenrangelgiraldo5428 Okay so, I'm assuming that you are getting this error when predicting for that I just put an if condition like: if (len(data_aux) != 84) And with in that if condition I predict the values. I myself don't know whats causing this error but my assumption is it has something to do with the both left and right hand landmarks (42+42=84). Nevertheless, this solves this issue hope it will help you too.

Can you try this code: desired_length = 4200 # Pad data_aux with zeros to achieve the desired length while len(data_aux) < desired_length: data_aux.extend([0.0, 0.0]) # Truncate data_aux if it exceeds the desired length data_aux = data_aux[:desired_length]

i think guys to solve this problem we had to tell the collect data script to save just frames where he could detect our hands else we will store bad models that will ends with this getting errors like "inhomogeneous shapes" , i actually try to solved this problem by not moving my hand when collecting data and making my model else you can try this code to check your images that are stored This script will only print the paths of the images that are deleted due to no hands being detected. It won't display any image windows. ##########################################" import os import cv2 import mediapipe as mp def process_and_show(image_path, mp_drawing): mp_hands = mp.solutions.hands hands = mp_hands.Hands() # Read the image image = cv2.imread(image_path) image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # Detect hands and landmarks results = hands.process(image_rgb) if not results.multi_hand_landmarks: print(f"Deleted image: {image_path}") # Delete the image with no hands detected os.remove(image_path) # Path to your data folder containing subfolders data_folder = "data" mp_drawing = mp.solutions.drawing_utils mp_drawing_styles = mp.solutions.drawing_styles # Iterate through subfolders for folder_name in os.listdir(data_folder): folder_path = os.path.join(data_folder, folder_name) if os.path.isdir(folder_path): print(f"Checking images in folder: {folder_name}") # Iterate through images in the folder for filename in os.listdir(folder_path): if filename.endswith(".jpg") or filename.endswith(".png"): image_path = os.path.join(folder_path, filename) process_and_show(image_path, mp_drawing)

same here, did you fix it?

Do you mean the accuracy of the hand detection?

@@ComputerVisionEngineer yes

@@ocelottes it is mediapipe hand detection, if you want to test it's accuracy you would need to take another hand detector to compare mediapipe detections against

Python 3.7 if not mistaken

Sir kindly help me with this error. I am working on this project as my final year project and I have to extend it as my major project work.

i think guys to solve this problem we had to tell the collect data script to save just frames where he could detect our hands else we will store bad models that will ends with this getting errors like "inhomogeneous shapes" , i actually try to solved this problem by not moving my hand when collecting data and making my model else you can try this code to check your images that are stored This script will only print the paths of the images that are deleted due to no hands being detected. It won't display any image windows. ##########################################" import os import cv2 import mediapipe as mp def process_and_show(image_path, mp_drawing): mp_hands = mp.solutions.hands hands = mp_hands.Hands() # Read the image image = cv2.imread(image_path) image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # Detect hands and landmarks results = hands.process(image_rgb) if not results.multi_hand_landmarks: print(f"Deleted image: {image_path}") # Delete the image with no hands detected os.remove(image_path) # Path to your data folder containing subfolders data_folder = "data" mp_drawing = mp.solutions.drawing_utils mp_drawing_styles = mp.solutions.drawing_styles # Iterate through subfolders for folder_name in os.listdir(data_folder): folder_path = os.path.join(data_folder, folder_name) if os.path.isdir(folder_path): print(f"Checking images in folder: {folder_name}") # Iterate through images in the folder for filename in os.listdir(folder_path): if filename.endswith(".jpg") or filename.endswith(".png"): image_path = os.path.join(folder_path, filename) process_and_show(image_path, mp_drawing)