diff --git a/analyze_people_clothing.py b/analyze_people_clothing.py
new file mode 100644
index 00000000..8ed831b2
--- /dev/null
+++ b/analyze_people_clothing.py
@@ -0,0 +1,338 @@
+#!/usr/bin/env python3
+"""
+Analyze an image to detect people and estimate their clothing colors.
+
+- Detect people using OpenCV's built-in HOG person detector
+- For each detected person, sample a torso region to estimate clothing color
+- If clothing appears grayscale (low saturation), label as 'light' or 'dark'
+- Otherwise, map the dominant hue to a coarse color name (e.g., red, green, blue)
+- Output a simple ASCII table with one row per person
+
+Usage:
+  python analyze_people_clothing.py --image path/to/image.jpg
+
+Optional flags:
+  --save-annotated path/to/output.jpg   Save image with bounding boxes and labels
+  --min-conf 0.0..1.0                   Minimum detection confidence to keep (default 0.0)
+
+Dependencies:
+  - opencv-python
+  - numpy
+"""
+from __future__ import annotations
+
+import argparse
+from dataclasses import dataclass
+from typing import List, Tuple
+
+import cv2
+import numpy as np
+
+
+@dataclass
+class Detection:
+    x: int
+    y: int
+    w: int
+    h: int
+    confidence: float
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Detect people and estimate clothing colors from an image.",
+    )
+    parser.add_argument(
+        "--image", required=True, type=str, help="Path to the input image file",
+    )
+    parser.add_argument(
+        "--save-annotated", type=str, default=None,
+        help="Optional path to save an annotated image with boxes and labels",
+    )
+    parser.add_argument(
+        "--min-conf", type=float, default=0.0,
+        help="Minimum detection confidence (0.0-1.0) for HOG detections to keep",
+    )
+    return parser.parse_args()
+
+
+def load_image(image_path: str) -> np.ndarray:
+    image = cv2.imread(image_path)
+    if image is None:
+        raise FileNotFoundError(f"Could not read image at '{image_path}'")
+    return image
+
+
+def initialize_hog_detector() -> cv2.HOGDescriptor:
+    hog = cv2.HOGDescriptor()
+    hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
+    return hog
+
+
+def detect_people(image_bgr: np.ndarray, min_confidence: float) -> List[Detection]:
+    hog = initialize_hog_detector()
+
+    # HOG works best on reasonably sized images; limit max dimension to ~1200 px for speed
+    height, width = image_bgr.shape[:2]
+    scale = 1.0
+    max_dim = max(height, width)
+    resized_image = image_bgr
+    if max_dim > 1200:
+        scale = 1200.0 / max_dim
+        resized_image = cv2.resize(image_bgr, (int(width * scale), int(height * scale)))
+
+    # Detect people
+    rects, weights = hog.detectMultiScale(
+        resized_image,
+        winStride=(8, 8),
+        padding=(8, 8),
+        scale=1.05,
+    )
+
+    # Map detections back to original scale
+    detections: List[Detection] = []
+    for (x, y, w, h), conf in zip(rects, weights):
+        if scale != 1.0:
+            x = int(x / scale)
+            y = int(y / scale)
+            w = int(w / scale)
+            h = int(h / scale)
+        # OpenCV returns weights as distances; normalize to 0..1 heuristically
+        try:
+            confidence = float(conf)
+        except Exception:
+            confidence = 0.0
+        detections.append(Detection(x=x, y=y, w=w, h=h, confidence=confidence))
+
+    # Filter by confidence threshold if provided
+    if min_confidence > 0.0 and len(detections) > 0:
+        # Convert HOG weights to a relative 0..1 score via min-max scaling
+        raw = np.array([d.confidence for d in detections], dtype=np.float32)
+        min_val, max_val = float(np.min(raw)), float(np.max(raw))
+        denom = (max_val - min_val) if (max_val - min_val) > 1e-6 else 1.0
+        norm = (raw - min_val) / denom
+        for i, d in enumerate(detections):
+            detections[i].confidence = float(norm[i])
+        detections = [d for d in detections if d.confidence >= min_confidence]
+
+    # Apply non-maximum suppression to reduce overlapping boxes
+    detections = non_max_suppression(detections, overlap_threshold=0.65)
+    return detections
+
+
+def non_max_suppression(detections: List[Detection], overlap_threshold: float = 0.65) -> List[Detection]:
+    if len(detections) == 0:
+        return []
+
+    boxes = np.array([[d.x, d.y, d.x + d.w, d.y + d.h] for d in detections], dtype=np.float32)
+    scores = np.array([d.confidence for d in detections], dtype=np.float32)
+
+    pick: List[int] = []
+
+    x1 = boxes[:, 0]
+    y1 = boxes[:, 1]
+    x2 = boxes[:, 2]
+    y2 = boxes[:, 3]
+
+    areas = (x2 - x1 + 1) * (y2 - y1 + 1)
+    idxs = np.argsort(scores)  # low to high; pop from end for high to low
+
+    while len(idxs) > 0:
+        last = idxs[-1]
+        pick.append(int(last))
+
+        suppress = [len(idxs) - 1]
+        for pos in range(0, len(idxs) - 1):
+            i = last
+            j = idxs[pos]
+
+            xx1 = max(x1[i], x1[j])
+            yy1 = max(y1[i], y1[j])
+            xx2 = min(x2[i], x2[j])
+            yy2 = min(y2[i], y2[j])
+
+            w = max(0.0, xx2 - xx1 + 1)
+            h = max(0.0, yy2 - yy1 + 1)
+            overlap = (w * h) / areas[j]
+
+            if overlap > overlap_threshold:
+                suppress.append(pos)
+
+        idxs = np.delete(idxs, suppress)
+
+    return [detections[i] for i in pick]
+
+
+def crop_clothing_region(image_bgr: np.ndarray, det: Detection) -> np.ndarray:
+    height, width = image_bgr.shape[:2]
+
+    x0 = max(0, det.x)
+    y0 = max(0, det.y)
+    x1 = min(width, det.x + det.w)
+    y1 = min(height, det.y + det.h)
+
+    w = max(0, x1 - x0)
+    h = max(0, y1 - y0)
+    if w == 0 or h == 0:
+        return image_bgr[0:0, 0:0]
+
+    # Heuristic torso region: middle 50% vertically, centered horizontally with margin
+    torso_top = y0 + int(0.35 * h)
+    torso_bottom = y0 + int(0.80 * h)
+    torso_left = x0 + int(0.10 * w)
+    torso_right = x0 + int(0.90 * w)
+
+    torso_top = max(0, min(height, torso_top))
+    torso_bottom = max(0, min(height, torso_bottom))
+    torso_left = max(0, min(width, torso_left))
+    torso_right = max(0, min(width, torso_right))
+
+    if torso_bottom <= torso_top or torso_right <= torso_left:
+        return image_bgr[0:0, 0:0]
+
+    roi = image_bgr[torso_top:torso_bottom, torso_left:torso_right]
+    return roi
+
+
+def estimate_clothing_color(roi_bgr: np.ndarray) -> str:
+    if roi_bgr.size == 0:
+        return "unknown"
+
+    # Reduce noise and sampling load
+    roi_small = roi_bgr
+    h_roi, w_roi = roi_bgr.shape[:2]
+    if max(h_roi, w_roi) > 200:
+        scale = 200.0 / max(h_roi, w_roi)
+        roi_small = cv2.resize(roi_bgr, (int(w_roi * scale), int(h_roi * scale)))
+
+    roi_small = cv2.GaussianBlur(roi_small, (5, 5), 0)
+
+    hsv = cv2.cvtColor(roi_small, cv2.COLOR_BGR2HSV)
+    h = hsv[:, :, 0].astype(np.float32)  # 0..179
+    s = hsv[:, :, 1].astype(np.float32)  # 0..255
+    v = hsv[:, :, 2].astype(np.float32)  # 0..255
+
+    s_median = float(np.median(s)) / 255.0
+    v_median = float(np.median(v)) / 255.0
+
+    # If low saturation overall, likely black/white/gray clothing
+    if s_median < 0.22:
+        return "light" if v_median >= 0.6 else "dark"
+
+    # Focus on colorful pixels to estimate dominant hue
+    colorful_mask = s > 50  # filter out dull/gray pixels
+    colorful_hues = h[colorful_mask]
+    colorful_vs = v[colorful_mask]
+
+    if colorful_hues.size == 0:
+        # Fallback to light/dark if saturation is not strong enough overall
+        return "light" if v_median >= 0.6 else "dark"
+
+    # Build hue histogram and find peak
+    hist_bins = 180
+    hist, _ = np.histogram(colorful_hues, bins=hist_bins, range=(0, 180))
+    dominant_h = int(np.argmax(hist))  # 0..179
+
+    # Heuristic for brown vs orange: darker values with orange hue -> brown
+    is_brown = (10 <= dominant_h <= 25) and (float(np.median(colorful_vs)) < 150)
+
+    if is_brown:
+        return "brown"
+
+    return hue_to_color_name(dominant_h)
+
+
+def hue_to_color_name(hue: int) -> str:
+    # Map OpenCV hue (0-179) to coarse color names
+    if hue <= 10 or hue >= 170:
+        return "red"
+    if 11 <= hue <= 25:
+        return "orange"
+    if 26 <= hue <= 35:
+        return "yellow"
+    if 36 <= hue <= 85:
+        return "green"
+    if 86 <= hue <= 95:
+        return "cyan"
+    if 96 <= hue <= 130:
+        return "blue"
+    if 131 <= hue <= 150:
+        return "purple"
+    if 151 <= hue <= 169:
+        return "pink"
+    return "unknown"
+
+
+def format_table(rows: List[Tuple[int, str]]) -> str:
+    headers = ["Person", "Clothing Color"]
+    person_col_width = max(len(headers[0]), max((len(str(idx)) for idx, _ in rows), default=0))
+    color_col_width = max(len(headers[1]), max((len(color) for _, color in rows), default=0))
+
+    def sep(char: str = "-") -> str:
+        return f"+{char * (person_col_width + 2)}+{char * (color_col_width + 2)}+"
+
+    lines = [
+        sep("-"),
+        f"| {headers[0].ljust(person_col_width)} | {headers[1].ljust(color_col_width)} |",
+        sep("="),
+    ]
+    for idx, color in rows:
+        lines.append(f"| {str(idx).ljust(person_col_width)} | {color.ljust(color_col_width)} |")
+    lines.append(sep("-"))
+    return "\n".join(lines)
+
+
+def annotate_image(image_bgr: np.ndarray, detections: List[Detection], labels: List[str]) -> np.ndarray:
+    annotated = image_bgr.copy()
+    for det, label in zip(detections, labels):
+        pt1 = (det.x, det.y)
+        pt2 = (det.x + det.w, det.y + det.h)
+        cv2.rectangle(annotated, pt1, pt2, (0, 255, 0), 2)
+        text = label
+        (text_w, text_h), baseline = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 0.6, 2)
+        x_text = det.x
+        y_text = max(0, det.y - 10)
+        cv2.rectangle(
+            annotated,
+            (x_text, y_text - text_h - baseline),
+            (x_text + text_w + 6, y_text + baseline),
+            (0, 0, 0),
+            thickness=-1,
+        )
+        cv2.putText(annotated, text, (x_text + 3, y_text), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)
+    return annotated
+
+
+def main() -> None:
+    args = parse_args()
+    image = load_image(args.image)
+
+    detections = detect_people(image, min_confidence=args.min_conf)
+
+    if len(detections) == 0:
+        print("No people detected.")
+        return
+
+    clothing_colors: List[str] = []
+    for det in detections:
+        roi = crop_clothing_region(image, det)
+        color_name = estimate_clothing_color(roi)
+        clothing_colors.append(color_name)
+
+    rows = [(i + 1, clothing_colors[i]) for i in range(len(clothing_colors))]
+
+    print(f"People detected: {len(detections)}")
+    print(format_table(rows))
+
+    if args.save_annotated:
+        labels = [f"{i+1}: {c}" for i, c in enumerate(clothing_colors)]
+        annotated = annotate_image(image, detections, labels)
+        ok = cv2.imwrite(args.save_annotated, annotated)
+        if ok:
+            print(f"Annotated image saved to: {args.save_annotated}")
+        else:
+            print(f"Failed to save annotated image to: {args.save_annotated}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 00000000..62891556
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,2 @@
+opencv-python>=4.8.0
+numpy>=1.23.0