sign_detection_module.py

import cv2
import numpy as np
import os
from matplotlib import pyplot as plt
import time
import mediapipe as mp


mpHolistic = mp.solutions.holistic
mp_holistic = mpHolistic # Holistic model
mp_drawing =  mp.solutions.drawing_utils


# Drawing utilities
def mediapipe_detection(image, model):
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # COLOR CONVERSION BGR 2 RGB
    results = model.process(image)               
    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) # COLOR COVERSION RGB 2 BGR
    return image, results
def draw_landmarks(image, results):
    mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS) # Draw pose connections
    mp_drawing.draw_landmarks(image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS) # Draw left hand connections
    mp_drawing.draw_landmarks(image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS) # Draw righ
  
def draw_styled_landmarks(image, results):
    # Draw face connections
   
    # Draw pose connections
    mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS,
                             mp_drawing.DrawingSpec(), 
                             mp_drawing.DrawingSpec()
                             ) 
    # Draw left hand connections
    mp_drawing.draw_landmarks(image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS, 
                             mp_drawing.DrawingSpec(), 
                             mp_drawing.DrawingSpec()
                             ) 
    # Draw right hand connections  
    mp_drawing.draw_landmarks(image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS, 
                             mp_drawing.DrawingSpec(), 
                             mp_drawing.DrawingSpec()
                             ) 
    
def extract_keypoints(results):

    pose = np.array([[res.x, res.y, res.z, res.visibility] for res in results.pose_landmarks.landmark]).flatten() if results.pose_landmarks else np.zeros(33*4)
    face = np.array([[res.x, res.y, res.z] for res in results.face_landmarks.landmark]).flatten() if results.face_landmarks else np.zeros(468*3)
    lh = np.array([[res.x, res.y, res.z] for res in results.left_hand_landmarks.landmark]).flatten() if results.left_hand_landmarks else np.zeros(21*3)
    rh = np.array([[res.x, res.y, res.z] for res in results.right_hand_landmarks.landmark]).flatten() if results.right_hand_landmarks else np.zeros(21*3)
    return np.concatenate([pose, face, lh, rh])


#module for collecting data




# Path for exported data, numpy arrays
DATA_PATH = os.path.join('MP_Data') 

# Actions that we try to detect
actions = np.array(['thanks', 'iloveyou','milk','father','mother', 'fine'])

# Thirty videos worth of data
no_sequences = 30

# Videos are going to be 30 frames in length
sequence_length = 30

# Folder start
start_folder = 30

for action in actions:
    dir_path = os.path.join(DATA_PATH, action)
    if not os.path.exists(dir_path):
        os.makedirs(dir_path)
    dirmax = np.max([int(folder) for folder in os.listdir(dir_path)]) if os.listdir(dir_path) else 0
    for sequence in range(1, no_sequences + 1):
        new_dir_path = os.path.join(dir_path, str(dirmax + sequence))
        try:
            os.makedirs(new_dir_path)
        except FileExistsError:
            print(f"Directory {new_dir_path} already exists.")
        except Exception as e:
            print(f"Error creating directory {new_dir_path}: {e}")


            
cap = cv2.VideoCapture(0)
# Set mediapipe model 
with mp_holistic.Holistic(min_detection_confidence=0, min_tracking_confidence=0) as holistic:

    # Loop through actions
    for action in actions:
        # Loop through sequences aka videos
        for sequence in range(start_folder, (start_folder+sequence_length)):
            # Loop through video length aka sequence length
            for frame_num in range(sequence_length):

               
                ret, frame = cap.read()

                # Make detections
                image, results = mediapipe_detection(frame, holistic)

                # Draw landmarks
                draw_styled_landmarks(image, results)
                
                # NEW Apply wait logic
                if frame_num == 0: 
                    cv2.putText(image, 'STARTING COLLECTION', (120,200), 
                               cv2.FONT_HERSHEY_SIMPLEX, 1, (0,255, 0), 4, cv2.LINE_AA)
                    cv2.putText(image, 'Collecting frames for {} Video Number {}'.format(action, sequence), (15,12), 
                               cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1, cv2.LINE_AA)
                    # Show to screen
                    cv2.imshow('OpenCV Feed', image)
                    cv2.waitKey(500)
                else: 
                    cv2.putText(image, 'Collecting frames for {} Video Number {}'.format(action, sequence), (15,12), 
                               cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1, cv2.LINE_AA)
                    # Show to screen
                    cv2.imshow('OpenCV Feed', image)
                
                # NEW Export keypoints
                keypoints = extract_keypoints(results)
                npy_path = os.path.join(DATA_PATH, action, str(sequence), str(frame_num))
                np.save(npy_path, keypoints)

                # Break gracefully
                if cv2.waitKey(10) & 0xFF == ord('q'):
                    break
                    
    cap.release()
    cv2.destroyAllWindows()
result_test = extract_keypoints(results)
print(result_test)
np.save('0.npy', result_test)
loaded_array = np.load('0.npy')

cap.release()
cv2.destroyAllWindows()