Test files and initial data gathering completed

analyze.py

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+"""Lightweight image analysis for live capture feedback.
+
+All stats are computed on a small downsampled copy so the demo loop stays
+fast at full 1080p capture.
+"""
+
+from collections import Counter
+
+from PIL import Image
+
+ANALYSIS_SIZE = (320, 180)
+_TOP_COLORS = 5
+
+# Harvesting is detected from the single pixel at this window coordinate.
+# Harvesting shows a light tan (~#f2c285, luma ~201); idle shows dark brown
+# (~#664c33, luma ~81).
+HARVEST_PIXEL = (1710, 1068)
+HARVEST_LUMA_THRESHOLD = 140
+
+# The small numeric readout just before each bar at the bottom of the panel.
+# Regions are (x0, y0, x1, y1) in captured-window pixel coordinates. y0 starts
+# below the panel's top border line (y=1032) which would confuse OCR.
+NUMBER_REGIONS = {
+    "mana": (0, 1033, 62, 1048),
+    "food": (195, 1033, 226, 1048),
+    "health": (355, 1033, 389, 1048),
+    "inventory": (520, 1033, 552, 1048),
+    "action_points": (610, 1033, 714, 1048),
+}
+
+_OCR_SCALE = 3
+
+try:
+    import pytesseract
+except ImportError:
+    pytesseract = None
+
+
+def _luma(pixel):
+    r, g, b = pixel
+    return round(0.299 * r + 0.587 * g + 0.114 * b)
+
+
+def load_image(path):
+    """Load a PNG as RGB; return None if the file is unreadable/corrupt."""
+    try:
+        return Image.open(path).convert("RGB")
+    except OSError:
+        return None
+
+
+def is_harvesting(image):
+    """Return whether the player is harvesting, based on the harvest pixel."""
+    rgb = image.convert("RGB")
+    color = rgb.getpixel(HARVEST_PIXEL)
+    lum = _luma(color)
+    return {
+        "is_harvesting": lum >= HARVEST_LUMA_THRESHOLD,
+        "color": "#%02x%02x%02x" % color,
+        "luma": lum,
+    }
+
+
+def _ocr_number(image, region):
+    """OCR a single number region; return int or None if it can't be read."""
+    if pytesseract is None:
+        return None
+    crop = image.convert("L").crop(region)
+    crop = crop.resize((crop.width * _OCR_SCALE, crop.height * _OCR_SCALE))
+    crop = crop.point(lambda p: 0 if p < 140 else 255)
+    try:
+        text = pytesseract.image_to_string(
+            crop,
+            config="--psm 7 -c tessedit_char_whitelist=0123456789",
+        )
+    except Exception:
+        return None
+    digits = "".join(ch for ch in text if ch.isdigit())
+    return int(digits) if digits else None
+
+
+def read_bar_values(image):
+    """Return the numeric readout before each bar, or None if unreadable."""
+    rgb = image.convert("RGB")
+    return {
+        name: _ocr_number(rgb, region)
+        for name, region in NUMBER_REGIONS.items()
+    }
+
+
+def _pixels(image):
+    px = image.load()
+    w, h = image.size
+    return [px[x, y] for y in range(h) for x in range(w)]
+
+
+def summarize(image):
+    """Return size, mean RGB, and dominant colors of a captured frame."""
+    small = image.convert("RGB").resize(ANALYSIS_SIZE)
+    pixels = _pixels(small)
+    n = len(pixels)
+    total = [0, 0, 0]
+    for r, g, b in pixels:
+        total[0] += r
+        total[1] += g
+        total[2] += b
+    mean = tuple(round(c / n) for c in total)
+    top = Counter(pixels).most_common(_TOP_COLORS)
+    dominant = [
+        {"hex": "#%02x%02x%02x" % color, "fraction": round(count / n, 3)}
+        for color, count in top
+    ]
+    return {"size": (image.width, image.height), "mean_rgb": mean, "dominant": dominant}
+
+
+def difference(a, b):
+    """Sum of absolute channel differences between two same-size RGB images."""
+    if a.size != b.size:
+        return None
+    pa, pb = a.load(), b.load()
+    w, h = a.size
+    diff = 0
+    for y in range(h):
+        for x in range(w):
+            ca, cb = pa[x, y], pb[x, y]
+            diff += abs(ca[0] - cb[0]) + abs(ca[1] - cb[1]) + abs(ca[2] - cb[2])
+    return diff