Nik Afiq
a04ef5d0c5
- Changed references from './scratchpad' to '.scratchpad/' in graph.json and plan.md for consistency. - Expanded Phase 6 follow-up section in plan.md to clarify changes made to the pat detection logic: - Updated `find_cafe_sparkle()` to utilize multiple template scales for improved detection. - Modified `_pat_room` to allow polling for maximum clicks instead of breaking on the first miss. - Added mouse movement after each pat to prevent cursor occlusion of sparkles. - Verified that room entry and modal state checks function correctly, but end-to-end pat success remains untested due to lack of available interactions.
51 lines
1.9 KiB
Python
51 lines
1.9 KiB
Python
"""Image/color matching helpers (OpenCV-based). Ported from scripts/detect_and_click.py."""
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import os
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import cv2
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import numpy as np
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from ba_auto import config, driver
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SPARKLE_SHOT_PATH = os.path.join(config.SCRATCHPAD_DIR, "cafe_live.png")
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SPARKLE_CLICK_OFFSET = (75, 47)
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SPARKLE_THRESHOLD = 0.97
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# The cafe camera's zoom level isn't reset before farming, and the sparkle
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# icon's on-screen size scales with it (see screenshots/cafe/sparkle/
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# 01_sparke_zoomed_centered.png vs 03_sparkle_zoomed_out.png) -- a single
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# fixed-scale template match misses whenever the camera isn't at the exact
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# zoom the template was captured at. Try a spread of scales instead.
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SPARKLE_SCALES = (0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2)
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def _masked_template(template):
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b, g, r = cv2.split(template.astype(np.int16))
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yellow_white = ((r > 180) & (g > 140) & (r - b > 60)) | ((r > 200) & (g > 200) & (b > 200))
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mask_plane = (yellow_white.astype(np.uint8)) * 255
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return cv2.merge([mask_plane, mask_plane, mask_plane])
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def find_cafe_sparkle():
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driver.screenshot(SPARKLE_SHOT_PATH)
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template_full = cv2.imread(config.CAFE_SPARKLE_TEMPLATE)
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img = cv2.imread(SPARKLE_SHOT_PATH)
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th0, tw0 = template_full.shape[:2]
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best = None
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for scale in SPARKLE_SCALES:
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tw, th = max(1, round(tw0 * scale)), max(1, round(th0 * scale))
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template = cv2.resize(template_full, (tw, th))
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mask = _masked_template(template)
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result = cv2.matchTemplate(img, template, cv2.TM_CCORR_NORMED, mask=mask)
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_, max_val, _, max_loc = cv2.minMaxLoc(result)
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if max_val >= SPARKLE_THRESHOLD and (best is None or max_val > best[0]):
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best = (max_val, max_loc[0], max_loc[1], tw, th, scale)
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if best is None:
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return None
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score, x, y, tw, th, scale = best
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ox, oy = SPARKLE_CLICK_OFFSET
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return (x + tw // 2 + round(ox * scale), y + th // 2 + round(oy * scale), score)
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