File size: 4,425 Bytes
fe69e84 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 | #!/usr/bin/env python3
"""
download_dataset.py — fetch the public datasets that make up the CropGuard corpus.
What it does:
* Lists every source dataset with its licence and link.
* Attempts the Kaggle-hosted downloads automatically IF the Kaggle CLI is
configured (pip install kaggle, and ~/.kaggle/kaggle.json in place).
* Prints the Mendeley / GitHub links for the sets that must be downloaded by
hand (Mendeley serves a JS redirect, so scripted download is unreliable).
It does NOT organise images into class folders — run prepare_dataset.py for that.
Usage:
python download_dataset.py # show the plan + try Kaggle downloads
python download_dataset.py --list # just print the registry, download nothing
python download_dataset.py --out ./raw_downloads
"""
import argparse, os, shutil, subprocess, sys, textwrap
# (name, kind, identifier, licence note)
REGISTRY = [
("CCMT (Cashew/Cassava/Maize/Tomato — collected in Ghana) [PRIMARY]",
"mendeley", "https://data.mendeley.com/datasets/bwh3zbpkpv/1",
"Mendeley, typically CC BY 4.0 — verify on page"),
("PlantVillage (tomato, pepper, maize cross-checks)",
"kaggle-dataset", "abdallahalidev/plantvillage-dataset",
"Free for research — verify the mirror"),
("Cassava Leaf Disease (Makerere) — source for cassava_cbsd",
"kaggle-competition", "cassava-leaf-disease-classification",
"Kaggle competition rules (research/educational)"),
("MangoLeafBD (mango)",
"mendeley", "https://data.mendeley.com/datasets/hxsnvwty3r/1",
"CC BY 4.0 — verify"),
("Rice Leaf Disease Image Samples (Sethy)",
"mendeley", "https://data.mendeley.com/datasets/fwcj7stb8r/1",
"CC BY 4.0 — verify"),
("Groundnut Leaf Dataset (Sasmal)",
"mendeley", "https://data.mendeley.com/datasets/x6x5jkk873/2",
"CC BY 4.0 — verify"),
("Cocoa Diseases YOLOv4 (black pod) — Kaggle",
"kaggle-dataset", "serranosebas/enfermedades-cacao-yolov4",
"Verify on page"),
("KaraAgroAI Cocoa (CSSVD/healthy/anthracnose)",
"manual", "arXiv:2405.04535 — see the paper for the dataset repository link",
"Research/educational — verify"),
("BananaLSD (plantain/banana — sigatoka, healthy)",
"manual", "Data in Brief S2352340923006959 (Kaggle mirrors exist; search 'BananaLSD')",
"CC BY 4.0 — verify"),
]
NOTE_LOCAL = textwrap.dedent("""
Crops that need LOCALLY COLLECTED images (weak/no public dataset):
cowpea (all classes), yam (all), okra (all), garden egg (all),
plantain_bbtv, plantain_panama, cocoa_capsid, pepper_anthracnose.
Photograph these in the field — extension officers / research stations can help.
""")
def kaggle_available():
return shutil.which("kaggle") is not None
def try_kaggle(kind, ident, out):
if not kaggle_available():
print(" ! Kaggle CLI not found — skipping (pip install kaggle, add ~/.kaggle/kaggle.json)")
return
os.makedirs(out, exist_ok=True)
if kind == "kaggle-competition":
cmd = ["kaggle", "competitions", "download", "-c", ident, "-p", out]
else:
cmd = ["kaggle", "datasets", "download", "-d", ident, "-p", out]
print(" >", " ".join(cmd))
try:
subprocess.run(cmd, check=True)
print(" ✓ downloaded (unzip it inside the raw folder)")
except subprocess.CalledProcessError as e:
print(f" ! Kaggle download failed ({e}). For competitions you must accept the rules on the website first.")
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--out", default="./raw_downloads")
ap.add_argument("--list", action="store_true", help="print the registry only")
args = ap.parse_args()
print("=" * 72)
print("CropGuard GH — dataset sources")
print("=" * 72)
for i, (name, kind, ident, lic) in enumerate(REGISTRY, 1):
print(f"\n[{i}] {name}")
print(f" kind: {kind}")
print(f" source: {ident}")
print(f" licence: {lic}")
if args.list:
continue
if kind.startswith("kaggle"):
try_kaggle(kind, ident, args.out)
else:
print(" → download by hand from the link above into:", args.out)
print(NOTE_LOCAL)
print("Next: unzip everything into the raw folder, then run prepare_dataset.py")
if __name__ == "__main__":
main()
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