Update app.py

app.py

@@ -1,196 +1,312 @@
 import cv2
 import base64
 import numpy as np
-from flask import Flask, render_template, request, jsonify, send_from_directory
-import time
+import io
+from flask import Flask, render_template, Response, request, jsonify
+from flask_socketio import SocketIO, emit
+from PIL import Image
+from time import time as unix_time
+import os
 import mediapipe as mp
+from mediapipe.tasks import python
+from mediapipe.tasks.python import vision
+import time
+import argparse
 from mediapipe.framework.formats import landmark_pb2
 from mediapipe import solutions
 from tflite_support.task import vision as vision2
-from tflite_support.task import core, processor
+from tflite_support.task import core
+from tflite_support.task import processor
 from numpy.linalg import norm
 
-# Flask app setup
-app = Flask(__name__)
-
-# Global variables for letter detection results
+#Image Annotation Utils
+char_list=[]
+global letter_result
 letter_result = 0
-result_to_show = 0
-cresult_to_show = 0
-letterscore = 0
-no_hand_flag = 1
+global old_letter_result
+old_letter_result = 0
+MARGIN = 10  # pixels
+FONT_SIZE = 1
+FONT_THICKNESS = 1
+HANDEDNESS_TEXT_COLOR = (88, 205, 54) # vibrant green
+global test_x
+global test_y
+global result_to_show
+result_to_show=0
+global cresult_to_show
+cresult_to_show=0
+text_x = 0
+text_y = 0
+cwhich=0
+lastwidth = 400
+letterscore=0
+frame_time=0
+same_letter_time=0
+no_hand_flag=1
+# UTILS
+
+
+def brightness(img):
+    if len(img.shape) == 3:
+        # Colored RGB or BGR (*Do Not* use HSV images with this function)
+        # create brightness with euclidean norm
+        return np.average(norm(img, axis=2)) / np.sqrt(3)
+    else:
+        # Grayscale
+        return np.average(img)
+
+
+def draw_landmarks_on_image(rgb_image, detection_result):
+  hand_landmarks_list = detection_result.hand_landmarks
+  handedness_list = detection_result.handedness
+  annotated_image = np.copy(rgb_image)
+  crop = []
+  image_height, image_width, image_heightgray=annotated_image.shape
+
+
+  # Loop through the detected hands to visualize.
+  for idx in range(len(hand_landmarks_list)):
+    hand_landmarks = hand_landmarks_list[idx]
+    handedness = handedness_list[idx]
+
+    # Draw the hand landmarks.
+    hand_landmarks_proto = landmark_pb2.NormalizedLandmarkList()
+    hand_landmarks_proto.landmark.extend([
+      landmark_pb2.NormalizedLandmark(x=landmark.x, y=landmark.y, z=landmark.z) for landmark in hand_landmarks
+    ])
+    solutions.drawing_utils.draw_landmarks(
+      annotated_image,
+      hand_landmarks_proto,
+      solutions.hands.HAND_CONNECTIONS,
+      solutions.drawing_styles.get_default_hand_landmarks_style(),
+      solutions.drawing_styles.get_default_hand_connections_style())
+
+    # Get bounding box
+
+    height, width, _ = annotated_image.shape
+    
+    x_coordinates = [landmark.x for landmark in hand_landmarks]
+    y_coordinates = [landmark.y for landmark in hand_landmarks]
+    
+    min_x = int(min(x_coordinates) * width)  # Left
+    min_y = int(min(y_coordinates) * height)    # Top
+    max_x = int(max(x_coordinates) * width)  # Right
+    max_y = int(max(y_coordinates) * height) # Bottom
+
+    #Get dimensions of bounding box
+    sect_height = max_y-(min_y)
+    sect_width = max_x-(min_x)
+
+    #Get center of bounding box
+    center_x=(min_x+max_x)/2
+    center_y=(min_y+max_y)/2
+
+    sect_diameter=50
+    #Define dominant axis for aspect ratio
+
+    if(sect_height>sect_width):
+        sect_diameter = sect_height
+
+    if(sect_height<sect_width):
+        sect_diameter = sect_width
 
-# Initialize MediaPipe hand landmark detection
+    sect_diameter=sect_diameter+50 # Pad diameter
+    sect_radius=int(sect_diameter/2) # Find radius
+    
+    #Crop Image
+    crop_top=int(center_y-sect_radius) #Top boundry
+    crop_bottom=int(center_y+sect_radius) #Bottom boundry
+    crop_left=int(center_x-sect_radius) #Left boundry
+    crop_right=int(center_x+sect_radius) #Right boundry
+
+    #Account for out of canvas
+    if(crop_top<0): #Bounding box too high
+        crop_top=0
+
+    if(crop_left<0): #Bounding box too far left
+        crop_left=0
+
+    if(crop_right>image_width): #Bounding box too far right
+        crop_right=image_width
+
+    if(crop_bottom>image_height): #Bounding box too low
+        crop_bottom=image_height
+
+    # Trace bounding box
+    annotated_image = cv2.rectangle(annotated_image, (crop_left, crop_top), (crop_right, crop_bottom), (255,0,0), 6)
+
+    global text_x
+    global text_y
+
+    # For text, currently not used
+    text_x=crop_left
+    text_y=crop_top
+    
+    # Get cropped image
+    crop = annotated_image[crop_top:crop_bottom, crop_left:crop_right] 
+
+    # Scale cropped image
+    h, w = crop.shape[0:2]
+    neww = 150
+    newh = int(neww*(h/w))
+    crop = cv2.resize(crop, (neww, newh))
+    
+    #annotated_image[0:0+crop.shape[0], 0:0+crop.shape[1]] = crop # Used for superimposition
+
+    #annotated_image=crop # Used for replacement
+    
+  return [annotated_image, crop]
+
+#-------------------------------------------------------------
+
+# Letter List
+letter_list=["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","#"]
+
+# Initialise MediaPipe hand landmark detction
+RESULT = None
 BaseOptions = mp.tasks.BaseOptions
 HandLandmarker = mp.tasks.vision.HandLandmarker
 HandLandmarkerOptions = mp.tasks.vision.HandLandmarkerOptions
 HandLandmarkerResult = mp.tasks.vision.HandLandmarkerResult
 VisionRunningMode = mp.tasks.vision.RunningMode
 
-# Load your TFLite models (adjust paths if needed)
-cbase_options = core.BaseOptions(file_name="./exported/model.tflite")  # New model
-ccbase_options = core.BaseOptions(file_name="./exported/word.tflite")  # Old model or word model
+cbase_options = core.BaseOptions(file_name="./better_exported/model.tflite") # New tflite
+ccbase_options = core.BaseOptions(file_name="./exported/model.tflite") # Old tflite
+
+# Initialise ASL tflite model
 
 cclassification_options = processor.ClassificationOptions(max_results=1)
 coptions = vision2.ImageClassifierOptions(base_options=cbase_options, classification_options=cclassification_options)
 ccoptions = vision2.ImageClassifierOptions(base_options=ccbase_options, classification_options=cclassification_options)
-
 cclassifier = vision2.ImageClassifier.create_from_options(coptions)
 ccclassifier = vision2.ImageClassifier.create_from_options(ccoptions)
 
-# Callback to store MediaPipe detection results asynchronously
-RESULT = None
+
 
 def print_result(result: HandLandmarkerResult, output_image: mp.Image, timestamp_ms: int):
+
     global RESULT
-    RESULT = result
+    RESULT=result
+
 
 options = HandLandmarkerOptions(
     base_options=BaseOptions(model_asset_path='hand_landmarker.task'),
     running_mode=VisionRunningMode.LIVE_STREAM,
     result_callback=print_result)
 
-detector = mp.tasks.vision.HandLandmarker.create_from_options(options)
 
-# Utility functions for image processing
-def data_uri_to_image(data_uri):
-    header, encoded = data_uri.split(',', 1)
-    decoded_data = base64.b64decode(encoded)
-    nparr = np.frombuffer(decoded_data, np.uint8)
-    image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
-    return image 
+detector = vision.HandLandmarker.create_from_options(options)
+video_frames=[]
+
+
+
+app = Flask(__name__)
 
-def image_to_data_uri(image):
-    _, buffer = cv2.imencode('.jpg', image)
-    image_bytes = buffer.tobytes()
-    base64_encoded = base64.b64encode(image_bytes).decode('utf-8')
-    return f"data:image/jpeg;base64,{base64_encoded}"
 
-def draw_landmarks_on_image(rgb_image, detection_result):
-    hand_landmarks_list = detection_result.hand_landmarks
-    annotated_image = np.copy(rgb_image)
-    image_height, image_width, _ = annotated_image.shape
-
-    for idx in range(len(hand_landmarks_list)):
-        hand_landmarks = hand_landmarks_list[idx]
-        hand_landmarks_proto = landmark_pb2.NormalizedLandmarkList()
-        hand_landmarks_proto.landmark.extend([
-            landmark_pb2.NormalizedLandmark(x=landmark.x, y=landmark.y, z=landmark.z) for landmark in hand_landmarks
-        ])
-        solutions.drawing_utils.draw_landmarks(
-            annotated_image,
-            hand_landmarks_proto,
-            solutions.hands.HAND_CONNECTIONS,
-            solutions.drawing_styles.get_default_hand_landmarks_style(),
-            solutions.drawing_styles.get_default_hand_connections_style()
-        )
-    return annotated_image
-
-# Letter list - modify if needed
-letter_list = [chr(i) for i in range(65, 91)] + ['#']  # A-Z + #
-
-# Isẹ̀kiri dictionary (Example mapping, update with real words)
-isekiri_dict = {
-    'A': 'Àṣẹ',
-    'B': 'Bí',
-    'C': 'Ṣe',
-    'D': 'Dá',
-    'E': 'Ẹ̀',
-    'F': 'Fẹ́',
-    'G': 'Gba',
-    'H': 'Hàn',
-    'I': 'Ìyà',
-    'J': 'Jẹ',
-    'K': 'Kọ',
-    'L': 'Lá',
-    'M': 'Má',
-    'N': 'Ná',
-    'O': 'Ọ̀',
-    'P': 'Pẹ̀',
-    'Q': 'Kù',  # approximate since Q rarely used
-    'R': 'Rà',
-    'S': 'Ṣá',
-    'T': 'Tẹ',
-    'U': 'Ú',
-    'V': 'Vẹ',
-    'W': 'Wá',
-    'X': 'Ẹ́s',
-    'Y': 'Yá',
-    'Z': 'Zà',
-    '#': '#'
-}
-
-# Routes for web UI and models
 @app.route('/')
 def index():
     return render_template('index.html')
 
-@app.route('/exported/<path:filename>')
-def send_model(filename):
-    return send_from_directory('exported', filename)
 
-# Video frame processing API (ASL detection)
 @app.route('/api/data', methods=['POST'])
 def handle_video_frame():
-    global letter_result, result_to_show, cresult_to_show, letterscore, no_hand_flag
-
-    frame_data_uri = request.json.get('key')
-    if not frame_data_uri:
-        return jsonify({'error': 'No frame data received'}), 400
+    frame = request.json.get('key')
+    #print(request.json)
+    response_frame = data_uri_to_image(frame)
+    decimg = response_frame
 
-    frame = data_uri_to_image(frame_data_uri)
-    mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=frame)
+#--------------------------------------------
+    mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=decimg) # Create MediaPipe image
+    #print(mp.Timestamp.from_seconds(time.time()).value)
+    
+    detection_result = detector.detect_async(mp_image, mp.Timestamp.from_seconds(time.time()).value) # detct
+    
+    # Try-Catch block, because detection is not done during model initialisation
+    global no_hand_flag, frame_time, same_letter_time, letter_result, old_letter_result, char_list, letterscore
 
     try:
-        detection_result = detector.detect_async(mp_image, mp.Timestamp.from_seconds(time.time()).value)
+        result_images = draw_landmarks_on_image(mp_image.numpy_view(), RESULT) # Array of annotated and cropped images
+        annotated_image = result_images[0] 
+        cropped_image = result_images[1]
+
+        #Standardise and fit shape by resizing
+
+        h, w = annotated_image.shape[0:2]
+        neww = 500
+        newh = int(neww*(h/w))
+        resized_image = cv2.resize(annotated_image, (neww, newh))
+        final_image=resized_image
 
-        global RESULT
-        if RESULT is None:
-            return jsonify({'result': '_', 'frame': frame_data_uri})
+        if(RESULT.handedness != []): # To chack if there is any result at all and then feed tflite model
+            no_hand_flag=0
 
-        annotated_image = draw_landmarks_on_image(frame, RESULT)
-        
-        if RESULT.handedness:
-            no_hand_flag = 0
-            # If right hand detected, classify using models
             if RESULT.handedness[0][0].display_name == 'Right':
-                tf_image = vision2.TensorImage.create_from_array(frame)
-                classification_result = cclassifier.classify(tf_image)
-                cclassification_result = ccclassifier.classify(tf_image)
+                tf_image = vision2.TensorImage.create_from_array(cropped_image)
+                classification_result = cclassifier.classify(tf_image) # New
+                cclassification_result = ccclassifier.classify(tf_image) # Old
                 
-                result_to_show = classification_result.classifications[0].categories[0].category_name
-                cresult_to_show = cclassification_result.classifications[0].categories[0].category_name
-                
-                # Simple decision logic between old and new models
+                result_to_show = classification_result.classifications[0].categories[0].category_name # New
+                cresult_to_show = cclassification_result.classifications[0].categories[0].category_name # Old
+
                 if cclassification_result.classifications[0].categories[0].score > classification_result.classifications[0].categories[0].score:
-                    letter_result = cresult_to_show
+                    letter_result = cresult_to_show # To implement further UX with Text to Speech
+                    cwhich="Old"
+                    if result_to_show == "P" and cresult_to_show !="P":
+                        cwhich="New"
+                        letter_result = result_to_show
+                    
+                    
                 else:
-                    letter_result = result_to_show
-                letterscore = max(
-                    classification_result.classifications[0].categories[0].score,
-                    cclassification_result.classifications[0].categories[0].score
-                )
+                    letter_result = result_to_show # To implement further UX with Text to Speech
+                    cwhich="New"
+                    if cresult_to_show == "M" and cresult_to_show !="M":
+                        cwhich="Old"
+                    
+                    if result_to_show != "R" and cresult_to_show =="R":
+                        cwhich="Old"
+                        letter_result = cresult_to_show
+
+                    if result_to_show != "T" and cresult_to_show =="T":
+                        cwhich="Old"
+                        letter_result = cresult_to_show
+                if cwhich=="Old" :
+                    letterscore = cclassification_result.classifications[0].categories[0].score
+
+                if cwhich=="New" :
+                    letterscore = classification_result.classifications[0].categories[0].score
             else:
-                letter_result = '_'
-        else:
-            letter_result = '_'
-
-    except Exception as e:
-        print("Detection error:", e)
-        letter_result = '_'
-        annotated_image = frame
-
-    frame_out = image_to_data_uri(annotated_image)
-    return jsonify({"result": letter_result, "frame": frame_out})
-
-# Isẹ̀kiri translation API
-@app.route('/api/translate', methods=['POST'])
-def translate_to_isekiri():
-    data = request.json
-    text = data.get('text', '')
-    # Translate each letter to Isẹ̀kiri word or keep as is if unknown
-    translated = ' '.join(isekiri_dict.get(ch.upper(), ch) for ch in text if ch.strip())
-    return jsonify({'isekiri': translated})
-
-if __name__ == '__main__':
-    app.run(host='0.0.0.0', port=7860)
+                tf_image = vision2.TensorImage.create_from_array(cropped_image)
+                classification_result = cclassifier.classify(tf_image) # New
+                result_to_show = classification_result.classifications[0].categories[0].category_name # New
+
+                if result_to_show != "B":
+                    letter_result='_'
+                else:
+                    letter_result='>'
+    except Exception as e: 
+        # Ha! The catch err{throw err} scenario, it was actually quite useful in debugging though
+        print(e)
+    frame_data = image_to_data_uri(final_image)
+    #print(frame_data)
+    
+    return jsonify({"result": letter_result, "frame": frame_data}), 200
+
+def data_uri_to_image(data_uri):
+    header, encoded = data_uri.split(',', 1)
+    decoded_data = base64.b64decode(encoded)
+    nparr = np.frombuffer(decoded_data, np.uint8)
+    image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+    return image 
+
+def image_to_data_uri(image):
+    # Encode the image as a JPEG
+    _, buffer = cv2.imencode('.jpg', image)
+    # Convert the buffer to bytes
+    image_bytes = buffer.tobytes()
+    # Encode the bytes to Base64
+    base64_encoded = base64.b64encode(image_bytes).decode('utf-8')
+    # Create the Data URI
+    data_uri = f"data:image/jpeg;base64,{base64_encoded}"
+    return data_uri
+if (__name__ == '__main__'):
+    app.run( host='0.0.0.0', port=7860)