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Annotate screenshots, diagrams, and images with callout rectangles, arrows, labels, and color-coded highlights using PIL. Includes rules for animated GIF annotations with timing and pacing.
Basierend auf der SOC-Berufsklassifikation
Expert on the history, origin, and correct use of the em dash. Use when writing or reviewing code, comments, or data files to avoid em and en dashes, defaulting to never using them and replacing any found with a hyphen (-). Includes strong knowledge of punctuation marks and the proper usage of punctuation characters when writing comments.
Use when an outage, production incident, or significant service degradation has occurred and the team needs to write a structured blameless post-mortem. Triggers on phrases like "write a post-mortem", "incident review", "what went wrong", "outage report", "root cause analysis", or "RCA". Covers timeline reconstruction, contributing factor analysis, impact quantification, and action item generation with owners.
Create, set up, or update the personal context portfolio: structured markdown files describing who you are, how you work, your teams, and your tool/ADO configuration. Runs the interview workflow for first-time setup and targeted edits for updates. Trigger this skill when the user asks to: set up their context, create or update their context portfolio, "create my IQ", "set up my IQ", edit their profile, add/remove a stakeholder, update ADO config, change team info, update pillars, or set up any plugin configuration. Trigger when another skill fails to find context (missing files or TODO markers) and needs context populated. Also trigger when the user mentions a context change in passing (e.g., "my manager changed", "we added someone to the team") to offer a context file update. Do NOT trigger for read-only questions like "who's on my team?" or "what's my ADO config?". Those are answered directly from the context files referenced in the loaded custom instructions; no skill is needed.
Adopt repository-level harness engineering for coding agents. Use when a user wants to prevent repeated AI coding-agent mistakes by turning failures into durable instructions, drift checks, regression tests, failure memory, and adoption reports tailored to the target repository.
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Build GitHub Copilot workflows with Xquik X API SDKs, REST endpoints, MCP tools, signed webhooks, tweet search, user lookup, follower exports, media actions, and agent automation.
| name | image-annotations |
| description | Annotate screenshots, diagrams, and images with callout rectangles, arrows, labels, and color-coded highlights using PIL. Includes rules for animated GIF annotations with timing and pacing. |
Add visual callouts to any image — screenshots, diagrams, architecture docs, demo frames — using PIL/Pillow. Highlights what changed or what to look at, so reviewers don't have to guess.
Use this skill when you need to:
pip install Pillow -q
#E63946) — only for "bad" / "removed" things (e.g., circling a bug being fixed)#FF9F1C) — for neutral highlights ("look here", "new feature", etc.)C:/Windows/Fonts/Inkfree.ttf) for a handwritten look on WindowsImageFont.load_default()stroke_width=1 with stroke_fill=<same color as fill> — gives body without being too thickdraw.rounded_rectangle([x1, y1, x2, y2], radius=14, outline=color, width=5)from PIL import Image, ImageDraw, ImageFont
# Setup
font = ImageFont.truetype('C:/Windows/Fonts/Inkfree.ttf', 36) # or load_default()
color = '#FF9F1C' # orange for highlights
stroke = 5
pad = 18
img = Image.open('screenshot.png')
draw = ImageDraw.Draw(img)
# Rounded rect with padding
draw.rounded_rectangle(
[x1 - pad, y1 - pad, x2 + pad, y2 + pad],
radius=14, outline=color, width=stroke
)
# Leader line (same thickness as rect)
draw.line([x2 + pad, cy, x2 + pad + 40, cy - 30], fill=color, width=stroke)
# Label — same-color stroke for body, NO white stroke
draw.text(
(x2 + pad + 45, cy - 60), 'label text',
fill=color, font=font, stroke_width=1, stroke_fill=color
)
img.save('annotated.png')
annotate.pyFor images with multiple elements to annotate, use the annotate.py module below. Save it next to your script and import from it. It handles automatic label placement without overlapping.
from annotate import annotate_image
result = annotate_image(
'screenshot.png',
[
{'elem': (560, 275, 635, 390), 'label': 'button', 'draw_box': True},
{'elem': (105, 453, 236, 470), 'label': 'status text'},
],
debug=True,
)
result.save('annotated.png')
elem: (x1, y1, x2, y2) tight bounding box — must be exact pixel coordinateslabel: text label (supports \n for multi-line)draw_box: if True, draws a rounded rectangle around the element. If False (default), draws a V-arrowhead pointing at the elementdebug: shows targeting rectangles and candidate heatmap for placement validationAlways use grid_image() before annotating an unfamiliar image. Scaled-down previews display images smaller than actual pixel dimensions — the error compounds as you move away from (0,0).
from annotate import grid_image
grid = grid_image('screenshot.png', step=100)
grid.save('grid.png')
Then verify with small crops:
from PIL import Image
img = Image.open('screenshot.png')
crop = img.crop((x1 - 20, y1 - 20, x2 + 20, y2 + 20))
crop.save('verify.png')
abs(avg_brightness - 147) - std * 0.3 - dist * 0.02| Color | Meaning |
|---|---|
| Cyan | Target element box (elem + padding) |
| Gray | Exclusion zone (MIN_ARROW buffer) |
| Red→Green | Candidate heatmap (red=bad, green=good) |
| Magenta | Chosen label position |
| Orange | Final rendered annotation |
draw_box=True: rounded rectangle + straight line to label, no arrowheaddraw_box=False: V-shaped arrowhead with rounded line capsannotate.py — full moduleSave this as annotate.py and import from it:
"""
Algorithmic screenshot annotation with automatic label placement.
pip install Pillow numpy
Optional for diff_images: pip install scipy
"""
import math
import numpy as np
from PIL import Image, ImageDraw, ImageFont
# --- Defaults ---
DEFAULT_FONT = 'C:/Windows/Fonts/Inkfree.ttf'
DEFAULT_FONT_SIZE = 32
DEFAULT_COLOR = '#FF9F1C'
DEFAULT_STROKE = 5
MIN_ARROW = 25
MAX_ARROW = 120
TEXT_PAD = 6
BREATH = 18
CROSSING_PENALTY = 50
PROXIMITY_MARGIN = 40
PROXIMITY_PENALTY = 50
def _rect_intersects(a, b):
return a[0] < b[2] and a[2] > b[0] and a[1] < b[3] and a[3] > b[1]
def _segments_intersect(p1, p2, p3, p4):
def cross(o, a, b):
return (a[0] - o[0]) * (b[1] - o[1]) - (a[1] - o[1]) * (b[0] - o[0])
d1, d2 = cross(p3, p4, p1), cross(p3, p4, p2)
d3, d4 = cross(p1, p2, p3), cross(p1, p2, p4)
return ((d1 > 0 and d2 < 0) or (d1 < 0 and d2 > 0)) and \
((d3 > 0 and d4 < 0) or (d3 < 0 and d4 > 0))
def _line_rect_exit(cx, cy, tx, ty, rect):
x1, y1, x2, y2 = rect
dx, dy = tx - cx, ty - cy
tmin, tmax = 0.0, 1.0
for lo, hi, p, d in [(x1, x2, cx, dx), (y1, y2, cy, dy)]:
if abs(d) < 1e-9:
continue
t0, t1 = (lo - p) / d, (hi - p) / d
if t0 > t1:
t0, t1 = t1, t0
tmin, tmax = max(tmin, t0), min(tmax, t1)
return (cx + dx * tmax, cy + dy * tmax)
def _rect_gap(a, b):
dx = max(a[0] - b[2], b[0] - a[2], 0)
dy = max(a[1] - b[3], b[1] - a[3], 0)
if dx == 0 and dy == 0:
return 0
return math.sqrt(dx**2 + dy**2)
def _find_candidates(pixels, W, H, cyan, pw, ph, font):
cx, cy = (cyan[0] + cyan[2]) / 2, (cyan[1] + cyan[3]) / 2
excl_zone = (cyan[0] - MIN_ARROW, cyan[1] - MIN_ARROW,
cyan[2] + MIN_ARROW, cyan[3] + MIN_ARROW)
sx1 = max(0, cyan[0] - MAX_ARROW - pw)
sy1 = max(0, cyan[1] - MAX_ARROW - ph)
sx2 = min(W - pw, cyan[2] + MAX_ARROW)
sy2 = min(H - ph, cyan[3] + MAX_ARROW)
step_x = max(8, min(pw // 2, MAX_ARROW // 3))
step_y = max(8, min(ph // 2, MAX_ARROW // 3))
cands = []
for px in range(sx1, sx2, step_x):
for py in range(sy1, sy2, step_y):
pink = (px, py, px + pw, py + ph)
if _rect_intersects(pink, excl_zone):
continue
gl, gr = cyan[0] - pink[2], pink[0] - cyan[2]
gt, gb = cyan[1] - pink[3], pink[1] - cyan[3]
hd, vd = max(gl, gr, 0), max(gt, gb, 0)
ed = math.sqrt(hd**2 + vd**2) if (hd > 0 and vd > 0) else max(hd, vd)
if ed > MAX_ARROW:
continue
region = pixels[py:py + ph, px:px + pw, :3].astype(float)
score = abs(np.mean(region) - 147) - np.std(region) * 0.3
dist = math.sqrt((px + pw/2 - cx)**2 + (py + ph/2 - cy)**2)
score -= dist * 0.02
cands.append(((px, py), score))
return cands
def _resolve_placements(annots, font):
placed = []
all_elem_zones = []
for ann in annots:
all_elem_zones.append(ann['cyan'])
if ann.get('draw_box', False):
c = ann['cyan']
all_elem_zones.append((c[0]-BREATH, c[1]-BREATH, c[2]+BREATH, c[3]+BREATH))
for ann in sorted(annots, key=lambda a: -a['best_score']):
pw, ph = ann['pw'], ann['ph']
cyan = ann['cyan']
cx, cy = ann['cyan_center']
draw_box = ann.get('draw_box', False)
best_pos, best_score = None, -999
valid = []
for (px, py), score in ann['candidates']:
pink = (px, py, px + pw, py + ph)
ok = True
for ez in all_elem_zones:
if ez == cyan:
continue
if ann.get('draw_box', False):
own_viz = (cyan[0]-BREATH, cyan[1]-BREATH, cyan[2]+BREATH, cyan[3]+BREATH)
if ez == own_viz:
continue
if _rect_intersects(pink, ez):
ok = False; break
if not ok:
continue
for p_pink, p_excl, p_viz, _ in placed:
if _rect_intersects(pink, p_pink) or _rect_intersects(pink, p_excl):
ok = False; break
if p_viz and _rect_intersects(pink, p_viz):
ok = False; break
if not ok:
continue
for p_pink, p_excl, p_viz, _ in placed:
for rect in [p_pink, p_excl, p_viz]:
if rect is None:
continue
gap = _rect_gap(pink, rect)
if gap < PROXIMITY_MARGIN:
score -= PROXIMITY_PENALTY * (1 - gap / PROXIMITY_MARGIN)
for ez in all_elem_zones:
if ez == cyan:
continue
gap = _rect_gap(pink, ez)
if gap < PROXIMITY_MARGIN:
score -= PROXIMITY_PENALTY * (1 - gap / PROXIMITY_MARGIN)
tcx, tcy = px + pw/2, py + ph/2
cand_start = _line_rect_exit(tcx, tcy, cx, cy, pink)
if draw_box:
viz = (cyan[0]-BREATH, cyan[1]-BREATH, cyan[2]+BREATH, cyan[3]+BREATH)
cand_end = _line_rect_exit(cx, cy, tcx, tcy, viz)
else:
cand_end = _line_rect_exit(cx, cy, tcx, tcy, cyan)
for _, _, _, pa in placed:
if pa and _segments_intersect(cand_start, cand_end, pa[0], pa[1]):
score -= CROSSING_PENALTY; break
valid.append(((px, py), score))
if score > best_score:
best_score, best_pos = score, (px, py)
ann['valid_candidates'] = valid
if best_pos is None:
ann['pink'] = ann['tpos'] = ann['astart'] = ann['aend'] = ann['viz'] = None
continue
px, py = best_pos
pink = (px, py, px + pw, py + ph)
ann['pink'] = pink
ann['tpos'] = (px + TEXT_PAD, py + TEXT_PAD)
tcx, tcy = px + pw/2, py + ph/2
ann['astart'] = _line_rect_exit(tcx, tcy, cx, cy, pink)
if draw_box:
viz = (cyan[0]-BREATH, cyan[1]-BREATH, cyan[2]+BREATH, cyan[3]+BREATH)
ann['viz'] = viz
ann['aend'] = _line_rect_exit(cx, cy, tcx, tcy, viz)
else:
ann['viz'] = None
ann['aend'] = _line_rect_exit(cx, cy, tcx, tcy, cyan)
placed.append((pink, ann['excl_zone'], ann['viz'], (ann['astart'], ann['aend'])))
def _draw_debug(img, annots, color):
overlay = Image.new('RGBA', img.size, (0, 0, 0, 0))
od = ImageDraw.Draw(overlay)
for ann in annots:
cands = ann.get('valid_candidates', ann['candidates'])
if not cands:
continue
pw, ph = ann['pw'], ann['ph']
scores = [s for _, s in cands]
smin, smax = min(scores), max(scores)
rng = smax - smin if smax > smin else 1
for (px, py), score in cands:
t = (score - smin) / rng
if t < 0.5:
r_c, g_c, b_c = 220, int(180 * (t * 2)), 0
else:
r_c, g_c, b_c = int(220 * (1 - (t-0.5)*2)), 200, 0
alpha_fill = int(40 + 70 * t)
alpha_out = int(80 + 120 * t)
od.rectangle((px, py, px + pw, py + ph),
fill=(r_c, g_c, b_c, alpha_fill), outline=(r_c, g_c, b_c, alpha_out), width=1)
for ann in annots:
ez = ann['excl_zone']
od.rectangle(ez, fill=(120, 120, 120, 50), outline=(160, 160, 160, 160), width=1)
od.rectangle(ann['cyan'], fill=(0, 255, 255, 30), outline=(0, 255, 255, 180), width=2)
if ann.get('pink'):
od.rectangle(ann['pink'], fill=(255, 0, 255, 50),
outline=(255, 0, 255, 180), width=2)
return Image.alpha_composite(img, overlay)
def _draw_annotations(img, annots, font, color, stroke_width):
draw = ImageDraw.Draw(img)
for ann in annots:
if ann.get('viz'):
draw.rounded_rectangle(ann['viz'], radius=12, outline=color, width=stroke_width)
tpos = ann.get('tpos')
astart, aend = ann.get('astart'), ann.get('aend')
if not (tpos and astart and aend):
continue
sx, sy = int(astart[0]), int(astart[1])
ex, ey = int(aend[0]), int(aend[1])
draw.line([(sx, sy), (ex, ey)], fill=color, width=4, joint='curve')
r = 2
draw.ellipse([(sx-r, sy-r), (sx+r, sy+r)], fill=color)
draw.ellipse([(ex-r, ey-r), (ex+r, ey+r)], fill=color)
if not ann.get('draw_box', False):
angle = math.atan2(ey - sy, ex - sx)
al, spread = 18, 0.45
ax = ex - al * math.cos(angle - spread)
ay = ey - al * math.sin(angle - spread)
bx = ex - al * math.cos(angle + spread)
by = ey - al * math.sin(angle + spread)
draw.line([(int(ax), int(ay)), (ex, ey)], fill=color, width=4)
draw.line([(int(bx), int(by)), (ex, ey)], fill=color, width=4)
for px_, py_ in [(int(ax), int(ay)), (int(bx), int(by))]:
draw.ellipse([(px_-r, py_-r), (px_+r, py_+r)], fill=color)
draw.text(tpos, ann['label'], fill=color, font=font,
stroke_width=1, stroke_fill=color)
return img
def annotate_image(image_path, annotations, *,
debug=False,
font_path=DEFAULT_FONT,
font_size=DEFAULT_FONT_SIZE,
color=DEFAULT_COLOR,
stroke_width=DEFAULT_STROKE):
"""
Annotate a screenshot with automatic label placement.
Args:
image_path: path to the input image
annotations: list of dicts with keys:
- elem: (x1, y1, x2, y2) tight bounding box of element
- label: text label string
- draw_box: (optional, default False) draw rounded rect around element
debug: if True, draw developer rectangles (cyan/pink/gray/heatmap)
font_path: path to TTF font file
font_size: font size in pixels
color: hex color for annotations (default orange #FF9F1C)
stroke_width: width of orange highlight box outline
Returns:
PIL.Image with annotations drawn
"""
font = ImageFont.truetype(font_path, font_size)
img = Image.open(image_path).convert('RGBA')
pixels = np.array(img)
W, H = img.size
annots = []
for i, spec in enumerate(annotations):
eb = spec['elem']
em_pad = min(20, max(10, (eb[2] - eb[0]) // 10))
cyan = (eb[0] - em_pad, eb[1] - em_pad, eb[2] + em_pad, eb[3] + em_pad)
lines = spec['label'].split('\n')
tw = max(font.getbbox(line)[2] - font.getbbox(line)[0] for line in lines)
line_h = font.getbbox('Ay')[3] - font.getbbox('Ay')[0]
th = line_h * len(lines) + 4 * (len(lines) - 1)
pw, ph = tw + 2 * TEXT_PAD, th + 2 * TEXT_PAD
cands = _find_candidates(pixels, W, H, cyan, pw, ph, font)
annots.append({
'id': i,
'label': spec['label'],
'draw_box': spec.get('draw_box', False),
'cyan': cyan,
'cyan_center': ((cyan[0]+cyan[2])/2, (cyan[1]+cyan[3])/2),
'excl_zone': (cyan[0]-MIN_ARROW, cyan[1]-MIN_ARROW,
cyan[2]+MIN_ARROW, cyan[3]+MIN_ARROW),
'pw': pw, 'ph': ph,
'candidates': cands,
'best_score': max((s for _, s in cands), default=-999),
})
_resolve_placements(annots, font)
annots.sort(key=lambda a: a['id'])
if debug:
img = _draw_debug(img, annots, color)
img = _draw_annotations(img, annots, font, color, stroke_width)
return img
def diff_images(before_path, after_path, *, threshold=30, min_pixels=300,
dilate=5, debug=False):
"""Find changed regions between two screenshots and return cluster boxes.
Returns (clusters, debug_img_or_None):
clusters: list of (x1, y1, x2, y2, pixel_count) sorted largest-first
debug_img: if debug=True, PIL Image with heatmap overlay and cluster boxes
"""
from scipy import ndimage
img_a = Image.open(before_path).convert('RGB')
img_b = Image.open(after_path).convert('RGB')
if img_a.size != img_b.size:
raise ValueError(f"Image sizes differ: {img_a.size} vs {img_b.size}")
arr_a = np.array(img_a, dtype=np.float32)
arr_b = np.array(img_b, dtype=np.float32)
W, H = img_a.size
diff = np.abs(arr_b - arr_a).max(axis=2)
mask = diff > threshold
dilated = ndimage.binary_dilation(mask, iterations=dilate)
labeled, n_clusters = ndimage.label(dilated)
clusters = []
for i in range(1, n_clusters + 1):
ys, xs = np.where(labeled == i)
if len(ys) < min_pixels:
continue
clusters.append((int(xs.min()), int(ys.min()),
int(xs.max()), int(ys.max()), len(ys)))
clusters.sort(key=lambda c: -c[4])
debug_img = None
if debug:
overlay = img_b.copy().convert('RGBA')
norm = np.clip(diff / 255.0, 0, 1)
show_mask = diff > 10
r = np.clip((norm * 2) * 255, 0, 255).astype(np.uint8)
g = np.clip((1 - np.abs(norm - 0.5) * 2) * 200, 0, 200).astype(np.uint8)
b = np.clip((1 - norm) * 255, 0, 255).astype(np.uint8)
a = np.where(show_mask, np.clip(norm * 200 + 40, 40, 220).astype(np.uint8), 0)
heat = Image.fromarray(np.stack([r, g, b, a], axis=2), 'RGBA')
overlay = Image.alpha_composite(overlay, heat)
draw = ImageDraw.Draw(overlay)
try:
font = ImageFont.truetype('C:/Windows/Fonts/consola.ttf', 18)
except OSError:
font = ImageFont.load_default()
for idx, (x1, y1, x2, y2, px_count) in enumerate(clusters):
draw.rectangle([x1, y1, x2, y2], outline=(0, 255, 255, 200), width=3)
label = f"#{idx+1} {px_count:,}px"
bbox = font.getbbox(label)
tw, th = bbox[2] - bbox[0], bbox[3] - bbox[1]
lx, ly = x1, max(0, y1 - th - 8)
draw.rectangle([lx, ly, lx + tw + 8, ly + th + 4], fill=(0, 0, 0, 180))
draw.text((lx + 4, ly + 2), label, fill=(0, 255, 255, 255), font=font)
debug_img = overlay
return clusters, debug_img
def grid_image(image_path, step=100):
"""Draw a coordinate grid on an image for precise element location."""
img = Image.open(image_path).convert('RGBA')
draw = ImageDraw.Draw(img)
W, H = img.size
try:
font = ImageFont.truetype('C:/Windows/Fonts/consola.ttf', 14)
except OSError:
font = ImageFont.load_default()
for x in range(0, W, step):
draw.line([(x, 0), (x, H)], fill=(255, 0, 0, 120), width=1)
draw.text((x + 2, 2), str(x), fill=(255, 0, 0, 200), font=font)
for y in range(0, H, step):
draw.line([(0, y), (W, y)], fill=(255, 0, 0, 120), width=1)
draw.text((2, y + 2), str(y), fill=(255, 0, 0, 200), font=font)
return img
Find what changed between two screenshots programmatically. Use as a safety net for subtle changes — when the difference is obvious, annotate directly instead.
from annotate import diff_images
clusters, debug_img = diff_images(
'before.png', 'after.png',
threshold=30, # pixel difference floor (0-255)
min_pixels=300, # ignore tiny noise clusters
dilate=5, # merge nearby changed pixels
debug=True, # render heatmap overlay
)
# clusters = [(x1, y1, x2, y2, pixel_count), ...] sorted largest-first
if debug_img:
debug_img.save('diff-debug.png')
# Feed clusters into annotate_image:
annotations = [
{'elem': (x1, y1, x2, y2), 'label': f'Change #{i+1}', 'draw_box': True}
for i, (x1, y1, x2, y2, _) in enumerate(clusters[:3])
]
Debug heatmap colors: Blue = small difference, Yellow = medium, Red = large, Cyan boxes = cluster bounding boxes.
When to use: subtle opacity changes, dashed lines, minor color shifts, anti-aliasing differences. When NOT to use: any change you can see by eye — annotate directly for better labels.
Different from static images — animations have timing, transitions, and competing visual motion.
# 2-frame pop-in at 10fps
FADE_ALPHAS = [0.50, 1.00]
for frame_idx in range(total_frames):
if annotation_just_changed and local_idx < len(FADE_ALPHAS):
alpha = FADE_ALPHAS[local_idx]
else:
alpha = 1.0
# Apply alpha to annotation elements:
# - pill background: fill=(r, g, b, int(base_alpha * alpha))
# - text: fill=(*color, int(255 * alpha))
# - rect outline: outline=(*color, int(255 * alpha))
width, line width, and visual text weight should feel consistent (~5px)<img width="300"> in markdown, never resize with PIL (creates artifacts)Image.open(path).size first — HiDPI screenshots are larger than they appear (150% scaling = 1.5x CSS pixel dimensions)