cornvision 0.1.0

Count corn kernels and rows from ear images — deep-learning desktop app and Python library

cornvision

Offline corn kernel detection and row counting from ear images.

Installation

pip install -e .

Model weights are downloaded automatically from Hugging Face on first run and cached at ~/.cornvision/model.pth.

Usage

GUI

cornvision

The GUI supports both single and batch inference:

Control	Description
Add Images…	Add one or more images to the queue
Remove / Clear All	Remove selected or all images from the queue
Device	Select inference device (auto, cpu, cuda, mps)
Analyze All	Run inference on all pending images in sequence
Stop	Halt the batch after the current image finishes
Save Annotated…	Save the annotated image for the selected result
Export CSV…	Export all completed results to a CSV file

Queue items are colour-coded: ○ pending, ↻ running, ✓ done, ✗ error.

Python API

from cornvision import CornAnalyzer

analyzer = CornAnalyzer()          # loads model, downloads weights if needed
result = analyzer.analyze("corn.jpg")

print(result.total_kernels)        # int
print(result.total_rows)           # int
print(result.kernels_per_row)      # list[int]
print(result.inference_time_sec)   # float

result.save_viz("annotated.png")   # save annotated image to disk

CornAnalyzer loads the model once and can analyze multiple images:

for path in image_paths:
    result = analyzer.analyze(path)

CornAnalyzer(device="auto")

Parameter	Description
device	"auto" (default) picks CUDA → MPS → CPU automatically

AnalysisResult fields

Field	Type	Description
total_kernels	int	Total detected kernels
total_rows	int	Number of kernel rows
kernels_per_row	list[int]	Kernel count per row
inference_time_sec	float	Wall-clock inference time
average_kernel_width_mm	float	Estimated mean kernel width
average_kernel_height_mm	float	Estimated mean kernel height
viz_image	PIL.Image | None	Annotated image (in-memory)

Dependencies

Package	Purpose
torch / torchvision	Model inference
Pillow	Image I/O and visualization
numpy	Array operations
requests	Weight download

tkinter is used for the GUI and ships with the standard Python installer.

Project layout

src/cornvision/
├── __init__.py       # public API: CornAnalyzer, AnalysisResult
├── _gui.py           # tkinter GUI (entry point)
├── _analyzer.py      # CornAnalyzer — model load + analyze()
├── _inference.py     # end-to-end inference pipeline
├── _clustering.py    # kernel row clustering
├── _image_proc.py    # image pre/post-processing helpers
├── _model.py         # model architecture + weight loading
├── _download.py      # weight download + cache path
├── _config.py        # shared constants
└── _result.py        # AnalysisResult dataclass