#!/usr/bin/env python3 """ prepare_dataset.py — turn the downloaded raw datasets into the train/val/test folder tree that backend/train.py expects. Pipeline: 1. Read MAPPING below (raw subfolder -> CropGuard class key). 2. Copy every matched image into _flat// . 3. Split each class 70/15/15 into /train|val|test// . 4. Print a per-class count so you can see which classes are thin. IMPORTANT — you must edit the SOURCE PATHS in MAPPING to match where you actually unzipped each dataset. Mirror folder names differ slightly, so adjust the left-hand paths (relative to --raw) until they point at real folders. Usage: python prepare_dataset.py --raw ./raw_downloads --out ./data python prepare_dataset.py --raw ./raw_downloads --out ./data --check # report only, copy nothing """ import argparse, os, random, shutil from collections import defaultdict random.seed(42) IMG_EXT = (".jpg", ".jpeg", ".png", ".bmp", ".webp") # ---------------------------------------------------------------------------- # MAPPING: { class_key : [ list of raw subfolders (relative to --raw) ] } # Multiple source folders can feed one class; edit paths to match your unzip. # Folders that don't exist are simply skipped (with a warning), so it's safe to # fill in only the datasets you have downloaded so far. # ---------------------------------------------------------------------------- MAPPING = { # ---- Maize (CCMT + PlantVillage) ---- "maize_healthy": ["CCMT/Maize/healthy", "plantvillage/Corn_(maize)___healthy"], "maize_gls": ["CCMT/Maize/leaf spot", "plantvillage/Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot"], "maize_nclb": ["CCMT/Maize/leaf blight", "plantvillage/Corn_(maize)___Northern_Leaf_Blight"], "maize_rust": ["plantvillage/Corn_(maize)___Common_rust_"], "maize_msv": ["CCMT/Maize/streak virus"], "maize_faw": ["CCMT/Maize/fall armyworm"], # ---- Cassava (Cassava-Kaggle + CCMT) ---- "cassava_healthy":["cassava/healthy", "CCMT/Cassava/healthy"], "cassava_cmd": ["cassava/cmd", "CCMT/Cassava/mosaic"], "cassava_cbsd": ["cassava/cbsd"], "cassava_cbb": ["cassava/cbb", "CCMT/Cassava/bacterial blight"], # ---- Tomato (CCMT + PlantVillage) ---- "tomato_healthy": ["CCMT/Tomato/healthy", "plantvillage/Tomato___healthy"], "tomato_early": ["plantvillage/Tomato___Early_blight"], "tomato_late": ["plantvillage/Tomato___Late_blight", "CCMT/Tomato/leaf blight"], "tomato_wilt": ["CCMT/Tomato/verticillium wilt"], "tomato_septoria":["plantvillage/Tomato___Septoria_leaf_spot", "CCMT/Tomato/septoria"], "tomato_tylcv": ["plantvillage/Tomato___Tomato_Yellow_Leaf_Curl_Virus", "CCMT/Tomato/leaf curl"], # ---- Cocoa (KaraAgroAI + Kaggle cocoa) ---- "cocoa_healthy": ["cocoa/healthy"], "cocoa_blackpod": ["cocoa/black_pod"], "cocoa_cssvd": ["cocoa/cssvd"], "cocoa_capsid": ["LOCAL/cocoa_capsid"], # collect locally # ---- Cashew (CCMT) ---- "cashew_healthy": ["CCMT/Cashew/healthy"], "cashew_anthracnose":["CCMT/Cashew/anthracnose"], "cashew_gumosis": ["CCMT/Cashew/gummosis"], "cashew_leafminer": ["CCMT/Cashew/leaf miner"], # ---- Plantain (BananaLSD + local) ---- "plantain_healthy": ["bananalsd/healthy"], "plantain_sigatoka": ["bananalsd/sigatoka"], "plantain_bbtv": ["LOCAL/plantain_bbtv"], # collect locally "plantain_panama": ["LOCAL/plantain_panama"], # collect locally # ---- Yam (local) ---- "yam_healthy": ["LOCAL/yam_healthy"], "yam_anthracnose":["LOCAL/yam_anthracnose"], "yam_mosaic": ["LOCAL/yam_mosaic"], # ---- Pepper (PlantVillage + chili anthracnose/local) ---- "pepper_healthy": ["plantvillage/Pepper,_bell___healthy"], "pepper_bacterialspot":["plantvillage/Pepper,_bell___Bacterial_spot"], "pepper_anthracnose": ["LOCAL/pepper_anthracnose"], # ---- Cowpea (local) ---- "cowpea_healthy": ["LOCAL/cowpea_healthy"], "cowpea_blight": ["LOCAL/cowpea_blight"], "cowpea_mosaic": ["LOCAL/cowpea_mosaic"], "cowpea_cercospora":["LOCAL/cowpea_cercospora"], # ---- Groundnut (Sasmal) ---- "groundnut_healthy": ["groundnut/HEALTHY"], "groundnut_leafspot":["groundnut/LEAF SPOT (EARLY AND LATE)"], "groundnut_rosette": ["groundnut/ROSETTE"], "groundnut_rust": ["groundnut/RUST"], # ---- Rice (Sethy + healthy source/local) ---- "rice_healthy": ["LOCAL/rice_healthy"], "rice_blast": ["rice/Blast"], "rice_blb": ["rice/Bacterialblight"], "rice_brownspot": ["rice/Brownspot"], # ---- Okra (local) ---- "okra_healthy": ["LOCAL/okra_healthy"], "okra_yvmv": ["LOCAL/okra_yvmv"], "okra_leafspot":["LOCAL/okra_leafspot"], # ---- Garden egg (local) ---- "gardenegg_healthy": ["LOCAL/gardenegg_healthy"], "gardenegg_wilt": ["LOCAL/gardenegg_wilt"], "gardenegg_leafspot":["LOCAL/gardenegg_leafspot"], # ---- Mango (MangoLeafBD) ---- "mango_healthy": ["mangoleafbd/Healthy"], "mango_anthracnose": ["mangoleafbd/Anthracnose"], "mango_bacterialspot":["mangoleafbd/Bacterial Canker"], } def list_images(folder): if not os.path.isdir(folder): return [] out = [] for root, _, files in os.walk(folder): for f in files: if f.lower().endswith(IMG_EXT): out.append(os.path.join(root, f)) return out def main(): ap = argparse.ArgumentParser() ap.add_argument("--raw", default="./raw_downloads") ap.add_argument("--out", default="./data") ap.add_argument("--check", action="store_true", help="report counts only, copy nothing") ap.add_argument("--val", type=float, default=0.15) ap.add_argument("--test", type=float, default=0.15) args = ap.parse_args() flat = args.out + "_flat" counts = {} missing = [] for cls, sources in MAPPING.items(): imgs = [] for s in sources: p = os.path.join(args.raw, s) found = list_images(p) if not found and not s.startswith("LOCAL/"): missing.append((cls, s)) imgs += found counts[cls] = len(imgs) if args.check: continue # copy into flat// dst = os.path.join(flat, cls) os.makedirs(dst, exist_ok=True) for i, src in enumerate(imgs): ext = os.path.splitext(src)[1].lower() shutil.copy2(src, os.path.join(dst, f"{cls}_{i:05d}{ext}")) # ---- split flat -> train/val/test ---- if not args.check: for cls in MAPPING: files = list_images(os.path.join(flat, cls)) random.shuffle(files) n = len(files) n_test = int(n * args.test) n_val = int(n * args.val) buckets = { "test": files[:n_test], "val": files[n_test:n_test + n_val], "train": files[n_test + n_val:], } for split, fs in buckets.items(): d = os.path.join(args.out, split, cls) os.makedirs(d, exist_ok=True) for f in fs: shutil.copy2(f, os.path.join(d, os.path.basename(f))) # ---- report ---- print("\nPer-class image counts:") weak = [] for cls in MAPPING: c = counts[cls] tag = "" if c == 0: tag = " <-- EMPTY (fix the source path, or collect images)" weak.append(cls) elif c < 100: tag = " <-- thin (<100); model will be weak here" weak.append(cls) print(f" {cls:<22} {c:>6}{tag}") print(f"\nTotal images: {sum(counts.values())}") if missing: print(f"\n{len(missing)} mapped source folder(s) not found — edit the paths in MAPPING:") for cls, s in missing[:40]: print(f" {cls}: {s}") if weak: print(f"\n{len(weak)} class(es) empty or thin — collect local images for these before relying on them.") if args.check: print("\n(--check: nothing was copied)") else: print(f"\nDone. Train with:\n cd backend && python train.py --data ../{os.path.basename(args.out)} --arch efficientnet") if __name__ == "__main__": main()