rotsl/fungal-colony-pipeline

input.py                                pipeline.py / app.py (Space)
┌──────────────────┐                    ┌──────────────────────────────────────┐
│  Gradio GUI      │                    │  Read image_metadata.csv             │
│  - Scan folder   │  image_metadata.   │  Load smp.Unet (thread-local)       │
│  - Tag metadata  │──── csv/json ─────▶│  For each image:                     │
│  - Export CSV    │                    │    1. OpenCV HoughCircles → dish     │
│  - Export ICS    │                    │    2. U-Net → colony mask            │
└──────────────────┘                    │    3. Crack detection (adaptive)     │
                                        │    4. Hyphae (Frangi + Meijering)    │
                                        │    5. Morphometrics (mm/mm²)         │
                                        │  6 overlay panels per image          │
                                        │  Growth charts (≥2 images)           │
                                        │  Output: analysis_full.csv/json      │
                                        └──────────────────────────────────────┘

# 1. Clone
git clone https://huggingface.co/rotsl/fungal-colony-pipeline
cd fungal-colony-pipeline

# 2. Virtual environment
python3 -m venv .venv
source .venv/bin/activate

# 3. Install
pip install -r requirements.txt

# 4. (Optional) Pre-download model
python -c "
from huggingface_hub import hf_hub_download
p = hf_hub_download('rotsl/grayleafspot-segmentation', 'grayleafspot.pt')
print(f'Downloaded to: {p}')
"

python input.py

IMG_DIR=./data python pipeline.py

pip install gradio_client

from gradio_client import Client, handle_file

client = Client("rotsl/fungal-colony-input")

# Step 1: Upload images
result = client.predict(
    files=[
        handle_file("plate_d01.jpg"),
        handle_file("plate_d03.jpg"),
        handle_file("plate_d05.jpg"),
    ],
    api_name="/on_upload",
)
# result = (gallery_items, status_markdown)

# Step 2: Run the full analysis pipeline
analysis = client.predict(
    en="MagExp01",                # experiment name
    ed="2025-04-01",              # experiment start date
    un="YourName",                # user name
    pc=1,                         # plates count
    api_name="/on_run",
)
# analysis is a tuple:
#   [0] status message (str)
#   [1] overlay gallery — list of dicts with 'image' paths (6 panels per input image)
#   [2] growth chart gallery — list of dicts with 'image' paths
#   [3] results dataframe (dict with 'headers' and 'data')
#   [4] path to analysis_full.zip

status_msg    = analysis[0]
overlays      = analysis[1]   # list of {image: filepath, caption: str}
charts        = analysis[2]   # list of {image: filepath, caption: str}
results_table = analysis[3]   # {"headers": [...], "data": [[...], ...]}
zip_path      = analysis[4]   # local path to downloaded analysis_full.zip

print(status_msg)
print(f"Overlays: {len(overlays)} panels")
print(f"Charts:   {len(charts)}")
print(f"Results:  {len(results_table['data'])} rows × {len(results_table['headers'])} cols")
print(f"Download: {zip_path}")

meta = client.predict(
    en="MagExp01",
    ed="2025-04-01",
    un="YourName",
    pc=1,
    api_name="/on_export",
)
# meta[0] = status message
# meta[1] = metadata dataframe
# meta[2] = path to image_metadata.zip

# Upload images and run pipeline via REST API
# (Gradio uses a session-based API — the Python client is recommended)

curl -X POST https://rotsl-fungal-colony-input.hf.space/gradio_api/call/on_upload \
  -H "Content-Type: application/json" \
  -d '{
    "data": [
      [{"path": "https://your-server.com/plate_d01.jpg"}]
    ]
  }'

"""Process a folder of petri dish images via the HF Space API."""
from pathlib import Path
from gradio_client import Client, handle_file

IMAGE_DIR = Path("./my_experiment")
EXPERIMENT = "MagExp01"
START_DATE = "2025-04-01"

client = Client("rotsl/fungal-colony-input")

# Collect all images
images = sorted(
    p for p in IMAGE_DIR.rglob("*")
    if p.suffix.lower() in {".jpg", ".jpeg", ".png", ".tif", ".bmp", ".webp"}
)
print(f"Found {len(images)} images")

# Upload
client.predict(
    files=[handle_file(str(p)) for p in images],
    api_name="/on_upload",
)

# Run pipeline
status, overlays, charts, table, zip_path = client.predict(
    en=EXPERIMENT,
    ed=START_DATE,
    un="BatchUser",
    pc=1,
    api_name="/on_run",
)

print(status)
print(f"Results zip: {zip_path}")

# Access results as a DataFrame
import pandas as pd
df = pd.DataFrame(table["data"], columns=table["headers"])
print(df[["image_path", "area_mm2", "diameter_mm", "crack_coverage_pct"]].to_string())

library(readr)
library(dplyr)
library(ggplot2)

df <- read_csv("analysis_full.csv")

# Growth curve
df %>%
  filter(is.na(error) | error == "") %>%
  ggplot(aes(x = days_since_start, y = area_mm2, color = experiment_name)) +
  geom_line() + geom_point() +
  labs(x = "Days", y = "Colony Area (mm²)", title = "Magnaporthe Growth") +
  theme_minimal()

# Morphology summary
df %>%
  filter(is.na(error) | error == "") %>%
  group_by(experiment_name) %>%
  summarise(
    n = n(),
    mean_area = mean(area_mm2, na.rm = TRUE),
    mean_roughness = mean(edge_roughness, na.rm = TRUE),
    mean_crack_pct = mean(crack_coverage_pct, na.rm = TRUE),
    total_hyphae = sum(hyph_hybrid_mm, na.rm = TRUE)
  )

# RGR
df %>%
  filter(!is.na(rgr_per_day) & rgr_per_day != "") %>%
  mutate(rgr_per_day = as.numeric(rgr_per_day)) %>%
  ggplot(aes(x = days_since_start, y = rgr_per_day)) +
  geom_col(fill = "steelblue") +
  facet_wrap(~ experiment_name) +
  labs(x = "Days", y = "RGR (day⁻¹)") +
  theme_minimal()

library(jsonlite)
df <- fromJSON("analysis_full.json")
meta <- read_csv("image_metadata.csv")

your_image_folder/
├── subdir_a/
│   ├── mag01_20250401_01.jpg
│   └── mag01_20250402_01.jpg
├── image_metadata.csv          ← input.py
├── image_metadata.json         ← input.py
├── reminders.ics               ← input.py (if reminders)
├── analysis_full.csv           ← pipeline.py
└── analysis_full.json          ← pipeline.py

@misc{rohan_r_2026,
	author       = { rohan r },
	title        = { fungal-colony-pipeline (Revision e51373b) },
	year         = 2026,
	url          = { https://huggingface.co/rotsl/fungal-colony-pipeline },
	doi          = { 10.57967/hf/8570 },
	publisher    = { Hugging Face }
}