Update pixelateTG.py

pixelateTG.py

@@ -1,8 +1,9 @@
 import os
-from dotenv import load_dotenv  # Import the load_dotenv function from python-dotenv
+from dotenv import load_dotenv
 import cv2
 import random
 import imageio
+import numpy as np
 from telegram import Update, InlineKeyboardButton, InlineKeyboardMarkup
 from telegram.ext import Updater, CallbackContext, CommandHandler, CallbackQueryHandler, MessageHandler, Filters
 from concurrent.futures import ThreadPoolExecutor, wait
@@ -12,59 +13,66 @@
 # Load environment variables from .env file
 load_dotenv()
 
-TOKEN = os.getenv('TELEGRAM_BOT_TOKEN')  # Get the Telegram bot token from the environment variable
+TOKEN = os.getenv('TELEGRAM_BOT_TOKEN')
 MAX_THREADS = 15
 PIXELATION_FACTOR = 0.04
-RESIZE_FACTOR = 1.5  # Common resize factor
+RESIZE_FACTOR = 1.5
 executor = ThreadPoolExecutor(max_workers=MAX_THREADS)
 
+# Global MTCNN detector
+mtcnn_detector = MTCNN()
+
+# Cache for overlay files
+overlay_cache = {}
+
 def start(update: Update, context: CallbackContext) -> None:
     update.message.reply_text('Send me a picture or a GIF, and I will pixelate faces in it!')
 
 def detect_heads(image):
-    mtcnn = MTCNN()
-    faces = mtcnn.detect_faces(image)
-    head_boxes = [(face['box'][0], face['box'][1], int(RESIZE_FACTOR * face['box'][2]), int(RESIZE_FACTOR * face['box'][3])) for face in faces]
-    return head_boxes
+    global mtcnn_detector
+    faces = mtcnn_detector.detect_faces(image)
+    return [(int(face['box'][0]), int(face['box'][1]), int(face['box'][2]), int(face['box'][3])) for face in faces]
+
+def get_overlay_files(overlay_type):
+    if overlay_type not in overlay_cache:
+        overlay_cache[overlay_type] = [name for name in os.listdir() if name.startswith(f'{overlay_type}_')]
+    return overlay_cache[overlay_type]
 
 def overlay(photo_path, user_id, overlay_type, resize_factor, bot):
-    image = cv2.imread(photo_path)
+    image_data = np.fromfile(photo_path, np.uint8)
+    image = cv2.imdecode(image_data, cv2.IMREAD_UNCHANGED)
+
     heads = detect_heads(image)
-
+    overlay_files = get_overlay_files(overlay_type)
+
     for (x, y, w, h) in heads:
-        overlay_files = [name for name in os.listdir() if name.startswith(f'{overlay_type}_')]
         if not overlay_files:
             continue
         random_overlay = random.choice(overlay_files)
-        overlay_image = cv2.imread(random_overlay, cv2.IMREAD_UNCHANGED)
+
+        overlay_data = np.fromfile(random_overlay, np.uint8)
+        overlay_image = cv2.imdecode(overlay_data, cv2.IMREAD_UNCHANGED)
+
         original_aspect_ratio = overlay_image.shape[1] / overlay_image.shape[0]
-
-        # Calculate new dimensions for the overlay
         new_width = int(resize_factor * w)
         new_height = int(new_width / original_aspect_ratio)
 
-        # Ensure the overlay is centered on the face
         center_x = x + w // 2
         center_y = y + h // 2
 
-        # Overlay position adjusted for better centering
         overlay_x = int(center_x - 0.5 * resize_factor * w) - int(0.1 * resize_factor * w)
         overlay_y = int(center_y - 0.5 * resize_factor * h) - int(0.1 * resize_factor * w)
 
-        # Clamp values to ensure they are within the image boundaries
         overlay_x = max(0, overlay_x)
         overlay_y = max(0, overlay_y)
 
-        # Resize the overlay image
         overlay_image_resized = cv2.resize(overlay_image, (new_width, new_height), interpolation=cv2.INTER_AREA)
 
-        # Calculate the regions of interest (ROI)
         roi_start_x = overlay_x
         roi_start_y = overlay_y
         roi_end_x = min(image.shape[1], overlay_x + new_width)
         roi_end_y = min(image.shape[0], overlay_y + new_height)
 
-        # Blend the overlay onto the image
         try:
             overlay_part = overlay_image_resized[:roi_end_y - roi_start_y, :roi_end_x - roi_start_x]
             alpha_mask = overlay_part[:, :, 3] / 255.0
@@ -81,7 +89,6 @@ def overlay(photo_path, user_id, overlay_type, resize_factor, bot):
     cv2.imwrite(processed_path, image, [int(cv2.IMWRITE_JPEG_QUALITY), 95])
     return processed_path
 
-
 # Overlay functions
 def liotta_overlay(photo_path, user_id, bot):
     return overlay(photo_path, user_id, 'liotta', RESIZE_FACTOR, bot)
@@ -102,8 +109,12 @@ def clowns_overlay(photo_path, user_id, bot):
     return overlay(photo_path, user_id, 'clown', RESIZE_FACTOR, bot)
 
 def process_gif(gif_path, session_id, user_id, bot):
-    frames = imageio.mimread(gif_path)
-    processed_frames = [process_image(frame, user_id, session_id, bot) for frame in frames]
+    reader = imageio.get_reader(gif_path)
+    processed_frames = []
+    for frame in reader:
+        processed_frame = process_image(frame, user_id, session_id, bot)
+        processed_frames.append(processed_frame)
+
     processed_gif_path = f"processed/{user_id}_{session_id}.gif"
     imageio.mimsave(processed_gif_path, processed_frames)
     return processed_gif_path
@@ -135,7 +146,6 @@ def pixelate_faces(update: Update, context: CallbackContext) -> None:
              InlineKeyboardButton("🏆 Chad", callback_data=f'chad_overlay_{session_id}')]
         ]
 
-        # Check if it's a private chat, if yes, include the "⚔️ Pixel" button
         if update.message.chat.type == 'private':
             keyboard.append([InlineKeyboardButton("⚔️ Pixel", callback_data=f'pixelate_{session_id}')])
 
@@ -160,7 +170,6 @@ def pixelate_faces(update: Update, context: CallbackContext) -> None:
     else:
         update.message.reply_text('Please send either a photo or a GIF.')
 
-
 def pixelate_command(update: Update, context: CallbackContext) -> None:
     if update.message.reply_to_message and update.message.reply_to_message.photo:
         session_id = str(uuid4())
@@ -201,22 +210,16 @@ def process_image(photo_path, user_id, session_id, bot):
     faces = detect_heads(image)
 
     for (x, y, w, h) in faces:
-        # Define the region of interest (ROI)
         roi = image[y:y+h, x:x+w]
-
-        # Apply pixelation to the ROI
-        pixelation_size = max(1, int(PIXELATION_FACTOR * min(w, h)))  # Ensure pixelation size is at least 1
+        pixelation_size = max(1, int(PIXELATION_FACTOR * min(w, h)))
         pixelated_roi = cv2.resize(roi, (pixelation_size, pixelation_size), interpolation=cv2.INTER_NEAREST)
         pixelated_roi = cv2.resize(pixelated_roi, (w, h), interpolation=cv2.INTER_NEAREST)
-
-        # Replace the original face region with the pixelated ROI
         image[y:y+h, x:x+w] = pixelated_roi
 
     processed_path = f"processed/{user_id}_{session_id}_pixelated.jpg"
     cv2.imwrite(processed_path, image, [int(cv2.IMWRITE_JPEG_QUALITY), 95])
     return processed_path
 
-
 def button_callback(update: Update, context: CallbackContext) -> None:
     query = update.callback_query
     query.answer()
@@ -259,7 +262,6 @@ def button_callback(update: Update, context: CallbackContext) -> None:
 
 def main() -> None:
     updater = Updater(TOKEN)
-
     dispatcher = updater.dispatcher
 
     dispatcher.add_handler(CommandHandler("start", start))