Macroni interpreter
Project description
Macroni
DSL for GUI automation with OCR, template matching, and screen interaction. Randomness is baked into all operations, such as mouse movement and playback.
Installation
1. Install Python Dependencies
pip install macroni
Note that pytorch for CPU is installed by default. If your NVIDIA GPU supports CUDA, you will have to manually reinstall pytorch for GPU.
2. System Permissions
macOS: System Preferences → Security & Privacy → Accessibility
Grant permission to Terminal/Python to control your computer
3. VSCode Extension
For syntax highlighting, please add the extension to vscode: Macroni Language Support
4. Usage
Basic Command
Interactive
macroni
Execute file
macroni --file script.macroni
Note: the first time running will take time because pytorch is a massive library.
Debug Mode
Enable interactive debugging with breakpoints:
# Enable debug mode
macroni --file script.macroni --debug
# Set breakpoints at specific lines
macroni --file script.macroni --debug --breakpoints 10 --breakpoints 25
# Or use short flags
macroni -f script.macroni -d -b 10 -b 25
Debug Commands:
n(next) - Execute next linec(continue) - Continue to next breakpointp <var>(print) - Print variable valueq(quit) - Exit debuggereval <expression>- Evaluate expression
OCR Text Search
Find and click text on screen:
# Capture region once, reuse forever (cached)
region = @capture_region("login_area", false);
# Find text in region
results = @ocr_find_text(region, 0.8, "Login", 1.0);
if @len(results) > 0 {
text, conf, bbox = results[0];
x1, y1 = bbox[0];
@mouse_move(x1, y1, 500, true);
@left_click();
}
Note: This operation is fairly slow without CUDA. If using a CPU, keeping the region as small as possible will improve performance greatly.
OCR Functions:
@capture_region(key, overwrite)- Interactive region capture with caching- Hover top-left → Enter → bottom-right → Enter
- Returns
(x1, y1, x2, y2)tuple
@ocr_find_text(region, min_conf, filter, upscale)- Find text via OCRregion: From@capture_region()ornullfor full screenmin_conf: 0.0-1.0 confidence thresholdfilter: Text substring to search for (case-insensitive)upscale: 1.0 = no scaling, 0.5 = faster, 2.0 = tiny text- Returns
[(text, conf, [[x1,y1],[x2,y2],[x3,y3],[x4,y4]]), ...]
Template Matching
@set_template_dir("./templates");
x, y = @find_template("login_button");
if x != null {
@mouse_move(x, y, 1000, true);
@left_click();
}
Templates folder structure:
templates/
└── login_button/
├── ex1.png
└── ex2.png
Language Basics
# Variables & types
x = 10;
name = "test";
coords = (100, 200);
items = [1, 2, 3];
# Booleans
enabled = true; # true = 1
disabled = false; # false = 0
# Destructuring
x, y = (100, 200);
text, conf, bbox = results[0];
# Control flow
if x > 10 {
@print("yes");
} else {
@print("no");
}
while x < 100 {
x = x + 1;
}
# Functions
fn click_button(x, y) {
@mouse_move(x, y, 500, true);
@left_click();
}
Key Functions
Mouse/Keyboard:
@mouse_move(x, y, speed, human_like)@left_click()@press_and_release(delay_ms, ...keys)
Screen:
@get_coordinates(label, use_cache)- Interactive coordinate capture@get_pixel_at(x, y)- Returns(r, g, b)@check_pixel_color(x, y, radius, r, g, b, tolerance)
Timing:
@wait(ms)or@wait(ms, random_range)@time()
Recording:
@record(name, start_btn, stop_btn)- Record mouse/keyboard@playback(name, stop_btn)- Replay recording
Lists:
@len(list),@append(list, item),@pop(list, index),@shuffle(list)
Human-Like Randomness
Macroni incorporates randomness to avoid detection and mimic natural user behavior:
Mouse Movement:
@mouse_move(x, y, 1000, true); # human_like=true enables randomness
- Uses smoothstep for natural acceleration/deceleration
- Adds big random arcs (bulge peaks randomly between 25-75% of path)
- Mixes in sin wave wobble with random phase drift
- Each movement takes a unique path, even to the same destination
Wait Times:
@wait(1000, (100, 300)); # Base delay + random 100-300ms
- Optional random range parameter adds variability to timing
- Prevents predictable patterns in automation
Recording Playback:
- Mouse coordinates are compressed during recording (50ms buckets)
- Playback connects positions using smoothstep with randomized arcs and sin waves
- Each playback generates a different mouse path, never the exact same trajectory
- Maintains original timing while applying human-like movement between recorded points
Cache Files
Auto-created in working directory:
regions_cache.json- OCR regionscoordinates_cache.json- Captured coordinatespixel_colors_cache.json- Captured colorsrecordings_cache.json- Recorded macros
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