diff --git a/CardDetector.py b/CardDetector.py
index 136a8be..a24068d 100644
--- a/CardDetector.py
+++ b/CardDetector.py
@@ -1,124 +1,702 @@
-############## Python-OpenCV Playing Card Detector ###############
-#
-# Author: Evan Juras
-# Date: 9/5/17
-# Description: Python script to detect and identify playing cards
-# from a PiCamera video feed.
-#
-
-# Import necessary packages
-import cv2
-import numpy as np
-import time
-import os
-import Cards
-import VideoStream
-
-
-### ---- INITIALIZATION ---- ###
-# Define constants and initialize variables
-
-## Camera settings
-IM_WIDTH = 1280
-IM_HEIGHT = 720 
-FRAME_RATE = 10
-
-## Initialize calculated frame rate because it's calculated AFTER the first time it's displayed
-frame_rate_calc = 1
-freq = cv2.getTickFrequency()
-
-## Define font to use
-font = cv2.FONT_HERSHEY_SIMPLEX
-
-# Initialize camera object and video feed from the camera. The video stream is set up
-# as a seperate thread that constantly grabs frames from the camera feed. 
-# See VideoStream.py for VideoStream class definition
-## IF USING USB CAMERA INSTEAD OF PICAMERA,
-## CHANGE THE THIRD ARGUMENT FROM 1 TO 2 IN THE FOLLOWING LINE:
-videostream = VideoStream.VideoStream((IM_WIDTH,IM_HEIGHT),FRAME_RATE,1,0).start()
-time.sleep(1) # Give the camera time to warm up
-
-# Load the train rank and suit images
-path = os.path.dirname(os.path.abspath(__file__))
-train_ranks = Cards.load_ranks( path + '/Card_Imgs/')
-train_suits = Cards.load_suits( path + '/Card_Imgs/')
-
-
-### ---- MAIN LOOP ---- ###
-# The main loop repeatedly grabs frames from the video stream
-# and processes them to find and identify playing cards.
-
-cam_quit = 0 # Loop control variable
-
-# Begin capturing frames
-while cam_quit == 0:
-
-    # Grab frame from video stream
-    image = videostream.read()
-
-    # Start timer (for calculating frame rate)
-    t1 = cv2.getTickCount()
-
-    # Pre-process camera image (gray, blur, and threshold it)
-    pre_proc = Cards.preprocess_image(image)
-	
-    # Find and sort the contours of all cards in the image (query cards)
-    cnts_sort, cnt_is_card = Cards.find_cards(pre_proc)
-
-    # If there are no contours, do nothing
-    if len(cnts_sort) != 0:
-
-        # Initialize a new "cards" list to assign the card objects.
-        # k indexes the newly made array of cards.
-        cards = []
-        k = 0
-
-        # For each contour detected:
-        for i in range(len(cnts_sort)):
-            if (cnt_is_card[i] == 1):
-
-                # Create a card object from the contour and append it to the list of cards.
-                # preprocess_card function takes the card contour and contour and
-                # determines the cards properties (corner points, etc). It generates a
-                # flattened 200x300 image of the card, and isolates the card's
-                # suit and rank from the image.
-                cards.append(Cards.preprocess_card(cnts_sort[i],image))
-
-                # Find the best rank and suit match for the card.
-                cards[k].best_rank_match,cards[k].best_suit_match,cards[k].rank_diff,cards[k].suit_diff = Cards.match_card(cards[k],train_ranks,train_suits)
-
-                # Draw center point and match result on the image.
-                image = Cards.draw_results(image, cards[k])
-                k = k + 1
-	    
-        # Draw card contours on image (have to do contours all at once or
-        # they do not show up properly for some reason)
-        if (len(cards) != 0):
-            temp_cnts = []
-            for i in range(len(cards)):
-                temp_cnts.append(cards[i].contour)
-            cv2.drawContours(image,temp_cnts, -1, (255,0,0), 2)
-        
-        
-    # Draw framerate in the corner of the image. Framerate is calculated at the end of the main loop,
-    # so the first time this runs, framerate will be shown as 0.
-    cv2.putText(image,"FPS: "+str(int(frame_rate_calc)),(10,26),font,0.7,(255,0,255),2,cv2.LINE_AA)
-
-    # Finally, display the image with the identified cards!
-    cv2.imshow("Card Detector",image)
-
-    # Calculate framerate
-    t2 = cv2.getTickCount()
-    time1 = (t2-t1)/freq
-    frame_rate_calc = 1/time1
-    
-    # Poll the keyboard. If 'q' is pressed, exit the main loop.
-    key = cv2.waitKey(1) & 0xFF
-    if key == ord("q"):
-        cam_quit = 1
-        
-
-# Close all windows and close the PiCamera video stream.
-cv2.destroyAllWindows()
-videostream.stop()
-
+############## Python-OpenCV Playing Card Detector ###############
+#
+# Author: Evan Juras
+# Date: 9/5/17
+# Description: Python script to detect and identify playing cards
+# from a PiCamera video feed. Updated with blackjack helpers,
+# outcome evaluation, and cleaner overlays.
+#
+
+import argparse
+import os
+import time
+from typing import Dict, List, Optional, Tuple, TypedDict
+
+import cv2
+import numpy as np
+
+import Cards
+import VideoStream
+
+
+### ---- INITIALIZATION ---- ###
+# Define constants and initialize variables
+
+## Camera settings
+IM_WIDTH = 1280
+IM_HEIGHT = 720
+FRAME_RATE = 10
+
+## Define font to use
+font = cv2.FONT_HERSHEY_SIMPLEX
+
+
+class Detection(TypedDict):
+    rank: str
+    rank_diff: int
+    center: Tuple[int, int]
+
+
+class TrackedCardDisplay(TypedDict):
+    rank: str
+    center: Tuple[int, int]
+    is_stable: bool
+
+
+def _parse_cli_args() -> argparse.Namespace:
+    """Parse command-line arguments for detector configuration."""
+    parser = argparse.ArgumentParser(description="Playing card detector camera settings")
+    parser.add_argument(
+        "--camera-type",
+        choices=["auto", "pi", "usb"],
+        default="auto",
+        help="Select camera backend: 'pi' for PiCamera, 'usb' for USB/Continuity Camera, 'auto' tries Pi then USB.",
+    )
+    parser.add_argument(
+        "--camera-index",
+        type=int,
+        default=0,
+        help="USB camera index (ignored for PiCamera). Use when multiple cameras are connected.",
+    )
+    return parser.parse_args()
+
+
+def _create_videostream(args: argparse.Namespace) -> VideoStream.VideoStream:
+    """Create a video stream that matches the requested backend."""
+    if args.camera_type == "pi":
+        return VideoStream.VideoStream((IM_WIDTH, IM_HEIGHT), FRAME_RATE, 1, 0).start()
+    if args.camera_type == "usb":
+        return VideoStream.VideoStream((IM_WIDTH, IM_HEIGHT), FRAME_RATE, 2, args.camera_index).start()
+    try:
+        return VideoStream.VideoStream((IM_WIDTH, IM_HEIGHT), FRAME_RATE, 1, 0).start()
+    except ModuleNotFoundError:
+        print("picamera module not found; defaulting to USB camera.")
+        return VideoStream.VideoStream((IM_WIDTH, IM_HEIGHT), FRAME_RATE, 2, args.camera_index).start()
+
+
+### ---- BLACKJACK HELPERS ---- ###
+
+_FACE_RANKS = {"Ten", "Jack", "Queen", "King"}
+DEALER_ZONE_RATIO = 0.4  # y-position threshold that separates dealer (top) from player (bottom)
+MAX_TRACK_DISTANCE = 90.0  # pixels
+MIN_VOTES_FOR_STABLE = 3
+MAX_MISSED_FRAMES = 40
+RANK_DIFF_ACCEPTANCE = 1400  # lower is stricter; tweak based on noise
+RECOUNT_MISS_THRESHOLD = 15  # frames a card must disappear before recounting
+
+
+def _rank_to_blackjack_value(rank: str) -> Optional[int]:
+    """Map detector rank label to its blackjack numeric value."""
+    if rank == "Unknown":
+        return None
+    if rank == "Ace":
+        return 11
+    if rank in _FACE_RANKS:
+        return 10
+    text_to_value = {
+        "Two": 2,
+        "Three": 3,
+        "Four": 4,
+        "Five": 5,
+        "Six": 6,
+        "Seven": 7,
+        "Eight": 8,
+        "Nine": 9,
+    }
+    return text_to_value.get(rank)
+
+
+def _pair_value(rank: str) -> Optional[int]:
+    """Return pair comparison value for rank names."""
+    if rank == "Unknown":
+        return None
+    if rank == "Ace":
+        return 11
+    if rank in _FACE_RANKS:
+        return 10
+    return _rank_to_blackjack_value(rank)
+
+
+def _hand_total_and_soft(ranks: List[str]) -> Tuple[int, bool]:
+    """Calculate best blackjack total and whether the hand is soft."""
+    total = 0
+    ace_as_eleven = 0
+    for rank in ranks:
+        value = _rank_to_blackjack_value(rank)
+        if value is None:
+            continue
+        if rank == "Ace":
+            ace_as_eleven += 1
+        total += value
+    aces_remaining_as_11 = ace_as_eleven
+    while total > 21 and aces_remaining_as_11 > 0:
+        total -= 10
+        aces_remaining_as_11 -= 1
+    is_soft = aces_remaining_as_11 > 0
+    return total, is_soft
+
+
+def _basic_strategy_action(player_ranks: List[str], dealer_rank: Optional[str]) -> str:
+    """
+    Return basic-strategy move for multi-deck blackjack (dealer stands on soft 17).
+    Possible returns: Hit, Stand, Double, Split, or Waiting for cards.
+    """
+    #small change
+    if len(player_ranks) < 2 or not dealer_rank or dealer_rank == "Unknown":
+        return "Waiting for cards"
+
+    dealer_value = _rank_to_blackjack_value(dealer_rank)
+    if dealer_value is None:
+        return "Waiting for cards"
+
+    two_card_hand = len(player_ranks) == 2
+    pair_values = {_pair_value(rank) for rank in player_ranks}
+    if two_card_hand and len(pair_values) == 1:
+        pair_val = pair_values.pop()
+        if pair_val is not None:
+            if pair_val == 11:  # A,A
+                return "Split"
+            if pair_val == 10:  # Tens
+                return "Stand"
+            if pair_val == 9:
+                return "Split" if dealer_value in {2, 3, 4, 5, 6, 8, 9} else "Stand"
+            if pair_val == 8:
+                return "Split"
+            if pair_val == 7:
+                return "Split" if dealer_value in {2, 3, 4, 5, 6, 7} else "Hit"
+            if pair_val == 6:
+                return "Split" if dealer_value in {2, 3, 4, 5, 6} else "Hit"
+            if pair_val == 4:
+                return "Split" if dealer_value in {5, 6} else "Hit"
+            if pair_val in {2, 3}:
+                return "Split" if dealer_value in {2, 3, 4, 5, 6, 7} else "Hit"
+            if pair_val == 5:
+                if dealer_value in {2, 3, 4, 5, 6, 7, 8, 9}:
+                    return "Double"
+                return "Hit"
+
+    total, is_soft = _hand_total_and_soft(player_ranks)
+
+    if is_soft:
+        if total >= 20:
+            return "Stand"
+        if total == 19:
+            if two_card_hand and dealer_value == 6:
+                return "Double"
+            return "Stand"
+        if total == 18:
+            if two_card_hand and dealer_value in {3, 4, 5, 6}:
+                return "Double"
+            if dealer_value in {2, 7, 8}:
+                return "Stand"
+            return "Hit"
+        if total == 17:
+            if two_card_hand and dealer_value in {3, 4, 5, 6}:
+                return "Double"
+            return "Hit"
+        if total in {15, 16}:
+            if two_card_hand and dealer_value in {4, 5, 6}:
+                return "Double"
+            return "Hit"
+        if total in {13, 14}:
+            if two_card_hand and dealer_value in {5, 6}:
+                return "Double"
+            return "Hit"
+        return "Hit"
+
+    if total >= 17:
+        return "Stand"
+    if total == 16:
+        return "Stand" if dealer_value in {2, 3, 4, 5, 6} else "Hit"
+    if total == 15:
+        return "Stand" if dealer_value in {2, 3, 4, 5, 6} else "Hit"
+    if total == 14:
+        return "Stand" if dealer_value in {2, 3, 4, 5, 6} else "Hit"
+    if total == 13:
+        return "Stand" if dealer_value in {2, 3, 4, 5, 6} else "Hit"
+    if total == 12:
+        return "Stand" if dealer_value in {4, 5, 6} else "Hit"
+    if total == 11:
+        if two_card_hand and dealer_value != 11:
+            return "Double"
+        return "Hit"
+    if total == 10:
+        if two_card_hand and dealer_value in {2, 3, 4, 5, 6, 7, 8, 9}:
+            return "Double"
+        return "Hit"
+    if total == 9:
+        if two_card_hand and dealer_value in {3, 4, 5, 6}:
+            return "Double"
+        return "Hit"
+    return "Hit"
+
+
+def _match_rank_only(qcard: Cards.Query_card, rank_library: List[Cards.Train_ranks]) -> Tuple[str, int]:
+    """Return best rank match and diff, ignoring suits."""
+    best_rank_name = "Unknown"
+    best_rank_diff = Cards.RANK_DIFF_MAX
+    if len(qcard.rank_img) != 0:
+        for train_rank in rank_library:
+            diff_img = cv2.absdiff(qcard.rank_img, train_rank.img)
+            rank_diff = int(np.sum(diff_img) / 255)
+            if rank_diff < best_rank_diff:
+                best_rank_diff = rank_diff
+                best_rank_name = train_rank.name
+    return best_rank_name, best_rank_diff
+
+
+def _draw_rank_result(image: np.ndarray, center: Tuple[int, int], label: str) -> None:
+    """Overlay rank label and center point without suits."""
+    x_center, y_center = center
+    cv2.circle(image, (x_center, y_center), 5, (255, 0, 0), -1)
+    cv2.putText(image, label, (x_center - 50, y_center), font, 1, (0, 0, 0), 3, cv2.LINE_AA)
+    cv2.putText(image, label, (x_center - 50, y_center), font, 1, (0, 255, 255), 2, cv2.LINE_AA)
+
+
+def _format_card_string(rank: str) -> str:
+    """Produce a short description of a card for on-screen text."""
+    if rank == "Unknown":
+        return "Unknown"
+    return rank[:3].upper()
+
+
+def _detect_cards(
+    image: np.ndarray,
+    rank_library: List[Cards.Train_ranks],
+    frame_height: int,
+) -> Tuple[List[Detection], List[Detection]]:
+    """Locate cards in the frame and return dealer/player detections."""
+    pre_proc = Cards.preprocess_image(image)
+    cnts_sort, cnt_is_card = Cards.find_cards(pre_proc)
+
+    player_detections: List[Detection] = []
+    dealer_detections: List[Detection] = []
+    cards_in_frame: List[Cards.Query_card] = []
+
+    if cnts_sort:
+        for contour, is_card in zip(cnts_sort, cnt_is_card):
+            if not is_card:
+                continue
+            qcard = Cards.preprocess_card(contour, image)
+            best_rank, rank_diff = _match_rank_only(qcard, rank_library)
+            qcard.best_rank_match = best_rank
+            qcard.rank_diff = rank_diff
+            qcard.best_suit_match = "Unknown"
+            qcard.suit_diff = 0
+
+            detection: Detection = {
+                "rank": best_rank,
+                "rank_diff": rank_diff,
+                "center": (int(qcard.center[0]), int(qcard.center[1])),
+            }
+            if detection["center"][1] <= frame_height * DEALER_ZONE_RATIO:
+                dealer_detections.append(detection)
+            else:
+                player_detections.append(detection)
+            cards_in_frame.append(qcard)
+
+    if cards_in_frame:
+        cv2.drawContours(image, [card.contour for card in cards_in_frame], -1, (255, 0, 0), 2)
+
+    return player_detections, dealer_detections
+
+
+def _stable_known_ranks(cards: List[TrackedCardDisplay]) -> List[str]:
+    """Return ranks for cards that have stabilized and are recognized."""
+    return [card["rank"] for card in cards if card["is_stable"] and card["rank"] != "Unknown"]
+
+
+def _card_summary(cards: List[TrackedCardDisplay]) -> str:
+    """Create a compact text summary of tracked cards."""
+    text = " ".join(_format_card_string(card["rank"]) for card in cards)
+    return text if text else "None"
+
+
+def _blend_rect(
+    image: np.ndarray,
+    top_left: Tuple[int, int],
+    bottom_right: Tuple[int, int],
+    color: Tuple[int, int, int],
+    alpha: float = 0.55,
+) -> None:
+    """Draw a tinted rectangle without losing scene context."""
+    overlay = image.copy()
+    cv2.rectangle(overlay, top_left, bottom_right, color, -1)
+    cv2.addWeighted(overlay, alpha, image, 1 - alpha, 0, image)
+
+
+def _draw_label_value(
+    image: np.ndarray,
+    label: str,
+    value: str,
+    origin: Tuple[int, int],
+    label_color: Tuple[int, int, int],
+    value_color: Tuple[int, int, int],
+    value_scale: float = 0.8,
+) -> None:
+    """Render a two-line label/value pair with consistent spacing."""
+    x, y = origin
+    cv2.putText(image, label, (x, y), font, 0.55, label_color, 1, cv2.LINE_AA)
+    cv2.putText(image, value, (x, y + 28), font, value_scale, value_color, 2, cv2.LINE_AA)
+
+
+def _determine_blackjack_outcome(
+    player_ranks: List[str],
+    dealer_ranks: List[str],
+    player_total: Optional[int],
+    dealer_total: Optional[int],
+) -> Tuple[str, Tuple[int, int]]:
+    """Return a human-readable outcome string and display color."""
+    if player_total is None or dealer_total is None:
+        return "Waiting for cards", (0, 200, 255)
+
+    player_blackjack = len(player_ranks) == 2 and player_total == 21
+    dealer_blackjack = len(dealer_ranks) == 2 and dealer_total == 21
+
+    if player_total > 21 and dealer_total > 21:
+        return "Both bust", (0, 180, 255)
+    if player_total > 21:
+        return "Dealer wins (player busts)", (0, 0, 255)
+    if dealer_total > 21:
+        return "Player wins (dealer busts)", (0, 220, 0)
+    if player_blackjack and dealer_blackjack:
+        return "Push (blackjack)", (0, 200, 255)
+    if player_blackjack:
+        return "Player wins (blackjack)", (0, 220, 0)
+    if dealer_blackjack:
+        return "Dealer wins (blackjack)", (0, 0, 255)
+    if player_total > dealer_total:
+        return "Player wins", (0, 220, 0)
+    if player_total < dealer_total:
+        return "Dealer wins", (0, 0, 255)
+    return "Push", (0, 200, 255)
+
+
+def _render_overlay(
+    image: np.ndarray,
+    frame_rate_calc: float,
+    player_cards: List[TrackedCardDisplay],
+    dealer_cards: List[TrackedCardDisplay],
+    player_total: Optional[int],
+    player_soft: bool,
+    dealer_upcard: Optional[str],
+    recommendation: str,
+    outcome_message: str,
+    outcome_color: Tuple[int, int, int],
+) -> None:
+    """Render the informational overlay on the frame."""
+    player_cards_text = _card_summary(player_cards)
+    dealer_cards_text = _card_summary(dealer_cards)
+    total_label = "?"
+    if player_total is not None:
+        total_label = f"{player_total} ({'soft' if player_soft else 'hard'})"
+    dealer_label = dealer_upcard if dealer_upcard else "?"
+
+    if recommendation == "Waiting for cards":
+        action_color = (0, 200, 255)
+    elif recommendation in {"Stand", "Double", "Split"}:
+        action_color = (0, 220, 0)
+    else:
+        action_color = (0, 0, 255)
+
+    height, width = image.shape[:2]
+    banner_height = 70
+    panel_left = 20
+    panel_width = min(int(width * 0.42), 430)
+    panel_top = banner_height + 20
+    panel_height = 210
+    stats_top = panel_top + panel_height + 16
+    stats_height = 150
+    panel_right = panel_left + panel_width
+
+    _blend_rect(image, (0, 0), (width, banner_height), outcome_color, alpha=0.6)
+    _blend_rect(image, (panel_left, panel_top), (panel_right, panel_top + panel_height), (30, 30, 30), alpha=0.7)
+    _blend_rect(image, (panel_left, stats_top), (panel_right, stats_top + stats_height), (22, 22, 22), alpha=0.7)
+
+    banner_text = outcome_message.upper()
+    cv2.putText(image, banner_text, (26, 45), font, 1.1, (255, 255, 255), 3, cv2.LINE_AA)
+    fps_text = f"{frame_rate_calc:.1f} FPS"
+    cv2.putText(image, fps_text, (width - 180, 42), font, 0.65, (255, 255, 255), 2, cv2.LINE_AA)
+
+    text_left = panel_left + 22
+    dealer_origin_y = panel_top + 42
+    _draw_label_value(
+        image,
+        "DEALER",
+        dealer_cards_text,
+        (text_left, dealer_origin_y),
+        (210, 230, 255),
+        (255, 255, 255),
+    )
+    cv2.putText(
+        image,
+        f"Upcard: {dealer_label}",
+        (text_left, dealer_origin_y + 62),
+        font,
+        0.6,
+        (210, 210, 210),
+        1,
+        cv2.LINE_AA,
+    )
+
+    player_origin_y = dealer_origin_y + 95
+    _draw_label_value(
+        image,
+        "PLAYER",
+        player_cards_text,
+        (text_left, player_origin_y),
+        (185, 255, 200),
+        (255, 255, 255),
+    )
+    cv2.putText(
+        image,
+        f"Total: {total_label}",
+        (text_left, player_origin_y + 62),
+        font,
+        0.65,
+        (220, 220, 220),
+        2,
+        cv2.LINE_AA,
+    )
+
+    stats_origin_y = stats_top + 38
+    _draw_label_value(
+        image,
+        "PLAY",
+        recommendation.upper(),
+        (text_left, stats_origin_y),
+        (210, 230, 255),
+        action_color,
+        value_scale=0.9,
+    )
+
+    counts_y = stats_origin_y + 55
+    cv2.putText(
+        image,
+        f"Player cards {len(player_cards)}",
+        (text_left, counts_y),
+        font,
+        0.6,
+        (210, 210, 210),
+        2,
+        cv2.LINE_AA,
+    )
+    cv2.putText(
+        image,
+        f"Dealer cards {len(dealer_cards)}",
+        (text_left + 200, counts_y),
+        font,
+        0.6,
+        (210, 210, 210),
+        2,
+        cv2.LINE_AA,
+    )
+
+    counts_y += 30
+    cv2.putText(
+        image,
+        "Press Q to quit",
+        (text_left, counts_y),
+        font,
+        0.6,
+        (180, 220, 255),
+        2,
+        cv2.LINE_AA,
+    )
+    cv2.putText(
+        image,
+        "Press R to reset",
+        (text_left + 200, counts_y),
+        font,
+        0.6,
+        (180, 220, 255),
+        2,
+        cv2.LINE_AA,
+    )
+
+
+class CardTracker:
+    """Tracks card positions across frames to reduce recognition flicker."""
+
+    def __init__(self) -> None:
+        self.tracks: List[Dict[str, object]] = []
+
+    def update(self, detections: List[Detection]) -> Tuple[List[TrackedCardDisplay], List[str]]:
+        # Age existing tracks
+        for track in self.tracks:
+            track["missed"] += 1
+            if "counted" not in track:
+                track["counted"] = False
+            if "last_counted_rank" not in track:
+                track["last_counted_rank"] = None
+            if track["missed"] >= RECOUNT_MISS_THRESHOLD and track.get("stable_rank") not in (None, "Unknown"):
+                track["stable_rank"] = "Unknown"
+                track["counted"] = False
+                track["last_counted_rank"] = None
+                track["candidate_rank"] = None
+                track["candidate_count"] = 0
+
+        newly_counted: List[str] = []
+
+        for det in detections:
+            center_arr = np.array(det["center"], dtype=np.float32)
+            rank = det["rank"]
+            rank_diff = det["rank_diff"]
+
+            best_track = None
+            best_distance = MAX_TRACK_DISTANCE
+            for track in self.tracks:
+                distance = np.linalg.norm(center_arr - track["center"])
+                if distance < best_distance:
+                    best_distance = distance
+                    best_track = track
+
+            if best_track is not None and best_distance <= MAX_TRACK_DISTANCE:
+                prev_missed = best_track["missed"]
+                best_track["center"] = 0.7 * best_track["center"] + 0.3 * center_arr
+                best_track["missed"] = 0
+                if rank != "Unknown" and rank_diff < RANK_DIFF_ACCEPTANCE:
+                    if rank == best_track.get("candidate_rank"):
+                        best_track["candidate_count"] += 1
+                    else:
+                        best_track["candidate_rank"] = rank
+                        best_track["candidate_count"] = 1
+                    if best_track["candidate_count"] >= MIN_VOTES_FOR_STABLE:
+                        if best_track.get("stable_rank") != rank:
+                            best_track["stable_rank"] = rank
+                            if prev_missed >= RECOUNT_MISS_THRESHOLD:
+                                best_track["counted"] = False
+                                best_track["last_counted_rank"] = None
+                            elif best_track.get("last_counted_rank") == rank:
+                                best_track["counted"] = True
+                            else:
+                                best_track["counted"] = True
+                else:
+                    best_track["candidate_rank"] = None
+                    best_track["candidate_count"] = 0
+                if best_track.get("stable_rank") not in (None, "Unknown") and not best_track["counted"]:
+                    newly_counted.append(best_track["stable_rank"])
+                    best_track["counted"] = True
+                    best_track["last_counted_rank"] = best_track["stable_rank"]
+            else:
+                new_track = {
+                    "center": center_arr,
+                    "stable_rank": "Unknown",
+                    "candidate_rank": None,
+                    "candidate_count": 0,
+                    "missed": 0,
+                    "counted": False,
+                    "last_counted_rank": None,
+                }
+                if rank != "Unknown" and rank_diff < RANK_DIFF_ACCEPTANCE:
+                    new_track["candidate_rank"] = rank
+                    new_track["candidate_count"] = 1
+                    if new_track["candidate_count"] >= MIN_VOTES_FOR_STABLE:
+                        new_track["stable_rank"] = rank
+                        newly_counted.append(rank)
+                        new_track["counted"] = True
+                        new_track["last_counted_rank"] = rank
+                self.tracks.append(new_track)
+
+        self.tracks = [track for track in self.tracks if track["missed"] <= MAX_MISSED_FRAMES]
+
+        return self.get_cards(), newly_counted
+
+    def get_cards(self) -> List[TrackedCardDisplay]:
+        cards: List[TrackedCardDisplay] = []
+        for track in self.tracks:
+            stable_rank = track.get("stable_rank", "Unknown")
+            candidate_rank = track.get("candidate_rank")
+            display_rank = stable_rank if stable_rank != "Unknown" else (candidate_rank or "Unknown")
+            center_tuple = (int(track["center"][0]), int(track["center"][1]))
+            cards.append(
+                {
+                    "rank": display_rank,
+                    "center": center_tuple,
+                    "is_stable": stable_rank != "Unknown",
+                }
+            )
+        return sorted(cards, key=lambda card: card["center"][0])
+
+
+def main() -> None:
+    args = _parse_cli_args()
+    videostream = _create_videostream(args)
+    time.sleep(1)  # Give the camera time to warm up
+
+    path = os.path.dirname(os.path.abspath(__file__))
+    train_ranks = Cards.load_ranks(os.path.join(path, "Card_Imgs") + "/")
+
+    player_tracker = CardTracker()
+    dealer_tracker = CardTracker()
+
+    frame_rate_calc = 1.0
+    freq = cv2.getTickFrequency()
+
+    try:
+        while True:
+            image = videostream.read()
+            frame_height = image.shape[0]
+
+            t1 = cv2.getTickCount()
+
+            player_detections, dealer_detections = _detect_cards(image, train_ranks, frame_height)
+
+            dealer_cards_display, _ = dealer_tracker.update(dealer_detections)
+            player_cards_display, _ = player_tracker.update(player_detections)
+
+            for tracked_card in dealer_cards_display + player_cards_display:
+                if tracked_card["rank"] != "Unknown":
+                    _draw_rank_result(image, tracked_card["center"], tracked_card["rank"])
+
+            player_ranks = _stable_known_ranks(player_cards_display)
+            dealer_ranks = _stable_known_ranks(dealer_cards_display)
+
+            player_total: Optional[int] = None
+            player_soft = False
+            if player_ranks:
+                player_total, player_soft = _hand_total_and_soft(player_ranks)
+            dealer_total: Optional[int] = None
+            if dealer_ranks:
+                dealer_total, _ = _hand_total_and_soft(dealer_ranks)
+            dealer_upcard = dealer_ranks[0] if dealer_ranks else None
+            recommendation = _basic_strategy_action(player_ranks, dealer_upcard)
+
+            outcome_message, outcome_color = _determine_blackjack_outcome(
+                player_ranks, dealer_ranks, player_total, dealer_total
+            )
+
+            _render_overlay(
+                image,
+                frame_rate_calc,
+                player_cards_display,
+                dealer_cards_display,
+                player_total,
+                player_soft,
+                dealer_upcard,
+                recommendation,
+                outcome_message,
+                outcome_color,
+            )
+
+            cv2.imshow("Card Detector", image)
+
+            t2 = cv2.getTickCount()
+            time1 = (t2 - t1) / freq
+            if time1 != 0:
+                frame_rate_calc = 1 / time1
+
+            key = cv2.waitKey(1) & 0xFF
+            if key == ord("q"):
+                break
+            if key == ord("r"):
+                player_tracker = CardTracker()
+                dealer_tracker = CardTracker()
+
+    finally:
+        cv2.destroyAllWindows()
+        videostream.stop()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Cards.py b/Cards.py
index 68c0cdb..298d9a7 100644
--- a/Cards.py
+++ b/Cards.py
@@ -132,7 +132,12 @@ def find_cards(thresh_image):
     from largest to smallest."""
 
     # Find contours and sort their indices by contour size
-    dummy,cnts,hier = cv2.findContours(thresh_image,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
+    contours = cv2.findContours(thresh_image,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
+    if len(contours) == 3:
+        dummy,cnts,hier = contours
+    else:  # OpenCV 4 compatibility
+        cnts,hier = contours
+        dummy = None
     index_sort = sorted(range(len(cnts)), key=lambda i : cv2.contourArea(cnts[i]),reverse=True)
 
     # If there are no contours, do nothing
@@ -212,7 +217,12 @@ def preprocess_card(contour, image):
     Qsuit = query_thresh[186:336, 0:128]
 
     # Find rank contour and bounding rectangle, isolate and find largest contour
-    dummy, Qrank_cnts, hier = cv2.findContours(Qrank, cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
+    contours = cv2.findContours(Qrank, cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
+    if len(contours) == 3:
+        dummy, Qrank_cnts, hier = contours
+    else:
+        Qrank_cnts, hier = contours
+        dummy = None
     Qrank_cnts = sorted(Qrank_cnts, key=cv2.contourArea,reverse=True)
 
     # Find bounding rectangle for largest contour, use it to resize query rank
@@ -224,7 +234,12 @@ def preprocess_card(contour, image):
         qCard.rank_img = Qrank_sized
 
     # Find suit contour and bounding rectangle, isolate and find largest contour
-    dummy, Qsuit_cnts, hier = cv2.findContours(Qsuit, cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
+    contours = cv2.findContours(Qsuit, cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
+    if len(contours) == 3:
+        dummy, Qsuit_cnts, hier = contours
+    else:
+        Qsuit_cnts, hier = contours
+        dummy = None
     Qsuit_cnts = sorted(Qsuit_cnts, key=cv2.contourArea,reverse=True)
     
     # Find bounding rectangle for largest contour, use it to resize query suit
diff --git a/Rank_Suit_Isolator.py b/Rank_Suit_Isolator.py
index bc6bbca..466ae22 100644
--- a/Rank_Suit_Isolator.py
+++ b/Rank_Suit_Isolator.py
@@ -3,6 +3,7 @@
 ### Runs through A - K ranks and then the 4 suits.
 
 # Import necessary packages
+import argparse
 import cv2
 import numpy as np
 import time
@@ -20,23 +21,39 @@
 SUIT_WIDTH = 70
 SUIT_HEIGHT = 100
 
-# If using a USB Camera instead of a PiCamera, change PiOrUSB to 2
-PiOrUSB = 1
-
-if PiOrUSB == 1:
-    # Import packages from picamera library
-    from picamera.array import PiRGBArray
-    from picamera import PiCamera
-
-    # Initialize PiCamera and grab reference to the raw capture
-    camera = PiCamera()
-    camera.resolution = (IM_WIDTH,IM_HEIGHT)
-    camera.framerate = 10
-    rawCapture = PiRGBArray(camera, size=(IM_WIDTH,IM_HEIGHT))
-
-if PiOrUSB == 2:
-    # Initialize USB camera
-    cap = cv2.VideoCapture(0)
+parser = argparse.ArgumentParser(description="Rank/Suit isolator camera settings")
+parser.add_argument("--camera-type", choices=["auto","pi","usb"], default="auto",
+                    help="Select camera backend: 'pi' for PiCamera, 'usb' for USB/Continuity Camera, 'auto' tries Pi then USB.")
+parser.add_argument("--camera-index", type=int, default=0,
+                    help="USB camera index (ignored for PiCamera). Use when multiple cameras are connected.")
+args = parser.parse_args()
+
+camera = None
+rawCapture = None
+cap = None
+camera_type_used = None
+
+if args.camera_type in ("pi", "auto"):
+    try:
+        from picamera.array import PiRGBArray
+        from picamera import PiCamera
+        camera = PiCamera()
+        camera.resolution = (IM_WIDTH,IM_HEIGHT)
+        camera.framerate = 10
+        rawCapture = PiRGBArray(camera, size=(IM_WIDTH,IM_HEIGHT))
+        camera_type_used = "pi"
+    except ModuleNotFoundError:
+        if args.camera_type == "pi":
+            raise
+        print("picamera module not found; defaulting to USB camera.")
+
+if camera_type_used != "pi":
+    cap = cv2.VideoCapture(args.camera_index)
+    if not cap.isOpened():
+        raise RuntimeError(f"Unable to open USB camera at index {args.camera_index}.")
+    cap.set(cv2.CAP_PROP_FRAME_WIDTH, IM_WIDTH)
+    cap.set(cv2.CAP_PROP_FRAME_HEIGHT, IM_HEIGHT)
+    camera_type_used = "usb"
 
 # Use counter variable to switch from isolating Rank to isolating Suit
 i = 1
@@ -51,7 +68,7 @@
     
     
 
-    if PiOrUSB == 1: # PiCamera
+    if camera_type_used == "pi": # PiCamera
         rawCapture.truncate(0)
         # Press 'p' to take a picture
         for frame in camera.capture_continuous(rawCapture, format="bgr",use_video_port=True):
@@ -64,7 +81,7 @@
 
             rawCapture.truncate(0)
 
-    if PiOrUSB == 2: # USB camera
+    if camera_type_used == "usb": # USB camera
         # Press 'p' to take a picture
         while(True):
 
@@ -81,7 +98,12 @@
     retval, thresh = cv2.threshold(blur,100,255,cv2.THRESH_BINARY)
 
     # Find contours and sort them by size
-    dummy,cnts,hier = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
+    contours = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
+    if len(contours) == 3:
+        dummy,cnts,hier = contours
+    else:
+        cnts,hier = contours
+        dummy = None
     cnts = sorted(cnts, key=cv2.contourArea,reverse=True)
 
     # Assume largest contour is the card. If there are no contours, print an error
@@ -114,7 +136,12 @@
     # Isolate suit or rank
     if i <= 13: # Isolate rank
         rank = corner_thresh[20:185, 0:128] # Grabs portion of image that shows rank
-        dummy, rank_cnts, hier = cv2.findContours(rank, cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
+        contours = cv2.findContours(rank, cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
+        if len(contours) == 3:
+            dummy, rank_cnts, hier = contours
+        else:
+            rank_cnts, hier = contours
+            dummy = None
         rank_cnts = sorted(rank_cnts, key=cv2.contourArea,reverse=True)
         x,y,w,h = cv2.boundingRect(rank_cnts[0])
         rank_roi = rank[y:y+h, x:x+w]
@@ -123,7 +150,12 @@
 
     if i > 13: # Isolate suit
         suit = corner_thresh[186:336, 0:128] # Grabs portion of image that shows suit
-        dummy, suit_cnts, hier = cv2.findContours(suit, cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
+        contours = cv2.findContours(suit, cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
+        if len(contours) == 3:
+            dummy, suit_cnts, hier = contours
+        else:
+            suit_cnts, hier = contours
+            dummy = None
         suit_cnts = sorted(suit_cnts, key=cv2.contourArea,reverse=True)
         x,y,w,h = cv2.boundingRect(suit_cnts[0])
         suit_roi = suit[y:y+h, x:x+w]
@@ -141,4 +173,7 @@
     i = i + 1
 
 cv2.destroyAllWindows()
-camera.close()
+if camera_type_used == "pi":
+    camera.close()
+elif camera_type_used == "usb" and cap is not None:
+    cap.release()
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