import os import mediapipe as mp import tensorflow as tf N_ROWS = 543 N_DIMS = 3 DIM_NAMES = ['x', 'y', 'z'] SEED = 42 NUM_CLASSES = 250 INPUT_SIZE = 32 # Tensorflow layer to process data in TFLite # Data needs to be processed in the model itself, so we cannot use Python class PreprocessLayer(tf.keras.layers.Layer): def __init__(self): super(PreprocessLayer, self).__init__() def pad_edge(self, t, repeats, side): if side == 'LEFT': return tf.concat((tf.repeat(t[:1], repeats=repeats, axis=0), t), axis=0) elif side == 'RIGHT': return tf.concat((t, tf.repeat(t[-1:], repeats=repeats, axis=0)), axis=0) @tf.function( input_signature=(tf.TensorSpec(shape=[None, N_ROWS, N_DIMS], dtype=tf.float32),), ) def call(self, data0): # Number of Frames in Video N_FRAMES0 = tf.shape(data0)[0] # Filter Out Frames With Empty Hand Data frames_hands_nansum = tf.experimental.numpy.nanmean(tf.gather(data0, HAND_IDXS0, axis=1), axis=[1, 2]) non_empty_frames_idxs = tf.where(frames_hands_nansum > 0) non_empty_frames_idxs = tf.squeeze(non_empty_frames_idxs, axis=1) data = tf.gather(data0, non_empty_frames_idxs, axis=0) # Cast Indices in float32 to be compatible with Tensorflow Lite non_empty_frames_idxs = tf.cast(non_empty_frames_idxs, tf.float32) # Number of Frames in Filtered Video N_FRAMES = tf.shape(data)[0] # Gather Relevant Landmark Columns data = tf.gather(data, LANDMARK_IDXS0, axis=1) # Video fits in INPUT_SIZE if N_FRAMES < INPUT_SIZE: # Pad With -1 to indicate padding non_empty_frames_idxs = tf.pad(non_empty_frames_idxs, [[0, INPUT_SIZE - N_FRAMES]], constant_values=-1) # Pad Data With Zeros data = tf.pad(data, [[0, INPUT_SIZE - N_FRAMES], [0, 0], [0, 0]], constant_values=0) # Fill NaN Values With 0 data = tf.where(tf.math.is_nan(data), 0.0, data) return data, non_empty_frames_idxs # Video needs to be downsampled to INPUT_SIZE else: # Repeat if N_FRAMES < INPUT_SIZE ** 2: repeats = tf.math.floordiv(INPUT_SIZE * INPUT_SIZE, N_FRAMES0) data = tf.repeat(data, repeats=repeats, axis=0) non_empty_frames_idxs = tf.repeat(non_empty_frames_idxs, repeats=repeats, axis=0) # Pad To Multiple Of Input Size pool_size = tf.math.floordiv(len(data), INPUT_SIZE) if tf.math.mod(len(data), INPUT_SIZE) > 0: pool_size += 1 if pool_size == 1: pad_size = (pool_size * INPUT_SIZE) - len(data) else: pad_size = (pool_size * INPUT_SIZE) % len(data) # Pad Start/End with Start/End value pad_left = tf.math.floordiv(pad_size, 2) + tf.math.floordiv(INPUT_SIZE, 2) pad_right = tf.math.floordiv(pad_size, 2) + tf.math.floordiv(INPUT_SIZE, 2) if tf.math.mod(pad_size, 2) > 0: pad_right += 1 # Pad By Concatenating Left/Right Edge Values data = self.pad_edge(data, pad_left, 'LEFT') data = self.pad_edge(data, pad_right, 'RIGHT') # Pad Non Empty Frame Indices non_empty_frames_idxs = self.pad_edge(non_empty_frames_idxs, pad_left, 'LEFT') non_empty_frames_idxs = self.pad_edge(non_empty_frames_idxs, pad_right, 'RIGHT') # Reshape to Mean Pool data = tf.reshape(data, [INPUT_SIZE, -1, N_COLS, N_DIMS]) non_empty_frames_idxs = tf.reshape(non_empty_frames_idxs, [INPUT_SIZE, -1]) # Mean Pool data = tf.experimental.numpy.nanmean(data, axis=1) non_empty_frames_idxs = tf.experimental.numpy.nanmean(non_empty_frames_idxs, axis=1) # Fill NaN Values With 0 data = tf.where(tf.math.is_nan(data), 0.0, data) return data, non_empty_frames_idxs # Get the absolute path to the directory containing app.py current_dir = os.path.dirname(os.path.abspath(__file__)) # Define the filename of the TFLite model model_filename = "model.tflite" # Construct the full path to the TFLite model file model_path = os.path.join(current_dir, model_filename) # Load the TFLite model using the interpreter interpreter = tf.lite.Interpreter(model_path=model_path) interpreter.allocate_tensors() # Get input and output details of the TFLite model input_details = interpreter.get_input_details() output_details = interpreter.get_output_details() index_to_class = { "TV": 0, "after": 1, "airplane": 2, "all": 3, "alligator": 4, "animal": 5, "another": 6, "any": 7, "apple": 8, "arm": 9, "aunt": 10, "awake": 11, "backyard": 12, "bad": 13, "balloon": 14, "bath": 15, "because": 16, "bed": 17, "bedroom": 18, "bee": 19, "before": 20, "beside": 21, "better": 22, "bird": 23, "black": 24, "blow": 25, "blue": 26, "boat": 27, "book": 28, "boy": 29, "brother": 30, "brown": 31, "bug": 32, "bye": 33, "callonphone": 34, "can": 35, "car": 36, "carrot": 37, "cat": 38, "cereal": 39, "chair": 40, "cheek": 41, "child": 42, "chin": 43, "chocolate": 44, "clean": 45, "close": 46, "closet": 47, "cloud": 48, "clown": 49, "cow": 50, "cowboy": 51, "cry": 52, "cut": 53, "cute": 54, "dad": 55, "dance": 56, "dirty": 57, "dog": 58, "doll": 59, "donkey": 60, "down": 61, "drawer": 62, "drink": 63, "drop": 64, "dry": 65, "dryer": 66, "duck": 67, "ear": 68, "elephant": 69, "empty": 70, "every": 71, "eye": 72, "face": 73, "fall": 74, "farm": 75, "fast": 76, "feet": 77, "find": 78, "fine": 79, "finger": 80, "finish": 81, "fireman": 82, "first": 83, "fish": 84, "flag": 85, "flower": 86, "food": 87, "for": 88, "frenchfries": 89, "frog": 90, "garbage": 91, "gift": 92, "giraffe": 93, "girl": 94, "give": 95, "glasswindow": 96, "go": 97, "goose": 98, "grandma": 99, "grandpa": 100, "grass": 101, "green": 102, "gum": 103, "hair": 104, "happy": 105, "hat": 106, "hate": 107, "have": 108, "haveto": 109, "head": 110, "hear": 111, "helicopter": 112, "hello": 113, "hen": 114, "hesheit": 115, "hide": 116, "high": 117, "home": 118, "horse": 119, "hot": 120, "hungry": 121, "icecream": 122, "if": 123, "into": 124, "jacket": 125, "jeans": 126, "jump": 127, "kiss": 128, "kitty": 129, "lamp": 130, "later": 131, "like": 132, "lion": 133, "lips": 134, "listen": 135, "look": 136, "loud": 137, "mad": 138, "make": 139, "man": 140, "many": 141, "milk": 142, "minemy": 143, "mitten": 144, "mom": 145, "moon": 146, "morning": 147, "mouse": 148, "mouth": 149, "nap": 150, "napkin": 151, "night": 152, "no": 153, "noisy": 154, "nose": 155, "not": 156, "now": 157, "nuts": 158, "old": 159, "on": 160, "open": 161, "orange": 162, "outside": 163, "owie": 164, "owl": 165, "pajamas": 166, "pen": 167, "pencil": 168, "penny": 169, "person": 170, "pig": 171, "pizza": 172, "please": 173, "police": 174, "pool": 175, "potty": 176, "pretend": 177, "pretty": 178, "puppy": 179, "puzzle": 180, "quiet": 181, "radio": 182, "rain": 183, "read": 184, "red": 185, "refrigerator": 186, "ride": 187, "room": 188, "sad": 189, "same": 190, "say": 191, "scissors": 192, "see": 193, "shhh": 194, "shirt": 195, "shoe": 196, "shower": 197, "sick": 198, "sleep": 199, "sleepy": 200, "smile": 201, "snack": 202, "snow": 203, "stairs": 204, "stay": 205, "sticky": 206, "store": 207, "story": 208, "stuck": 209, "sun": 210, "table": 211, "talk": 212, "taste": 213, "thankyou": 214, "that": 215, "there": 216, "think": 217, "thirsty": 218, "tiger": 219, "time": 220, "tomorrow": 221, "tongue": 222, "tooth": 223, "toothbrush": 224, "touch": 225, "toy": 226, "tree": 227, "uncle": 228, "underwear": 229, "up": 230, "vacuum": 231, "wait": 232, "wake": 233, "water": 234, "wet": 235, "weus": 236, "where": 237, "white": 238, "who": 239, "why": 240, "will": 241, "wolf": 242, "yellow": 243, "yes": 244, "yesterday": 245, "yourself": 246, "yucky": 247, "zebra": 248, "zipper": 249 } inv_index_to_class = {v: k for k, v in index_to_class.items()} mp_holistic = mp.solutions.holistic def mediapipe_detection(image, model): # COLOR CONVERSION BGR 2 RGB image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) image.flags.writeable = False # Image is no longer writeable results = model.process(image) # Make prediction image.flags.writeable = True # Image is now writeable image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) # COLOR COVERSION RGB 2 BGR return image, results def extract_keypoints(results): face = np.array([[res.x, res.y, res.z] for res in results.face_landmarks.landmark]).flatten() lh = np.array([[res.x, res.y, res.z] for res in results.left_hand_landmarks.landmark]).flatten() rh = np.array([[res.x, res.y, res.z] for res in results.right_hand_landmarks.landmark]).flatten() pose = np.array([[res.x, res.y, res.z, res.visibility] for res in results.pose_landmarks.landmark]).flatten() # Pad or truncate the arrays to the expected length (543) face = np.pad(face, (0, max(0, 543 - len(face))), mode='constant') lh = np.pad(lh, (0, max(0, 543 - len(lh))), mode='constant') rh = np.pad(rh, (0, max(0, 543 - len(rh))), mode='constant') pose = np.pad(pose, (0, max(0, 543 - len(pose))), mode='constant') # Concatenate the arrays in the correct order and return the result return np.concatenate([face, lh, rh, pose]) # Make prediction def make_prediction(processed_landmarks): inputs = np.array(processed_landmarks, dtype=np.float32) # Set the input tensor for the TFLite model interpreter.set_tensor(input_details[0]['index'], inputs) # Invoke the TFLite interpreter to perform inference interpreter.invoke() # Get the output tensor of the TFLite model output_data = interpreter.get_tensor(output_details[0]['index']) # Find the index of the predicted class index = np.argmax(output_data) # Map the index to the corresponding class label using the index_to_class dictionary prediction = inv_index_to_class[index] return prediction # ... with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic: import cv2 import numpy as np import gradio as gr import tensorflow as tf # Modify the predict_with_webcam function to take an image as input and return the prediction string def predict_with_webcam(frame): if frame is None: raise ValueError("Frame is None. Make sure your webcam is working properly.") # Make detections using mediapipe image, results = mediapipe_detection(frame, holistic) print(results) if results is not None and results.face_landmarks is not None: landmarks = extract_keypoints(results) if landmarks is not None: # Calculate the number of landmarks per frame landmarks_per_frame = len(landmarks) // (N_ROWS * N_DIMS) # Reshape the landmarks to have shape (None, N_ROWS, N_DIMS) landmarks = landmarks.reshape(-1, landmarks_per_frame, N_DIMS) # Initialize PreprocessLayer preprocess_layer = PreprocessLayer() # Call the PreprocessLayer to preprocess the landmarks processed_landmarks, _ = preprocess_layer.call(landmarks) prediction = make_prediction(processed_landmarks) # Pass the preprocessed landmarks to make_prediction print("Prediction:", prediction) return prediction else: return "Could not detect landmarks or extract keypoints. Make sure your webcam is working properly." else: return "Could not detect face landmarks. Make sure your webcam is working properly." # Define the Gradio interface iface = gr.Interface( fn=predict_with_webcam, # The function to use for prediction inputs="webcam", # Use Gradio's "webcam" input to capture frames from the webcam outputs=gr.outputs.Textbox() # Display the prediction as text ) # Launch the interface iface.launch()