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e194ef7 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 | import os
# ─────────────────────────────────────────────────────────────────────────────
# ✏️ EDIT THIS PATH
# ─────────────────────────────────────────────────────────────────────────────
DATASET_DIR = r"D:\merged_dataset"
NC = 20 # total number of classees
# ─────────────────────────────────────────────────────────────────────────────
IMG_EXTS = {".jpg", ".jpeg", ".png", ".bmp", ".webp", ".avif"}
def verify_split(split):
img_dir = os.path.join(DATASET_DIR, split, "images")
lbl_dir = os.path.join(DATASET_DIR, split, "labels")
errors = []
warnings = []
if not os.path.isdir(img_dir):
print(f" [SKIP] {split}/images folder not found — skipping")
return
img_files = {os.path.splitext(f)[0]: f
for f in os.listdir(img_dir)
if os.path.splitext(f)[1].lower() in IMG_EXTS}
lbl_files = {os.path.splitext(f)[0]: f
for f in os.listdir(lbl_dir)
if f.endswith(".txt")} if os.path.isdir(lbl_dir) else {}
total_images = len(img_files)
total_labels = len(lbl_files)
total_boxes = 0
# ── Check 1: every image has a label ─────────────────────────────────────
for stem in img_files:
if stem not in lbl_files:
errors.append(f" [NO LABEL] {img_files[stem]}")
# ── Check 2: every label has an image ────────────────────────────────────
for stem in lbl_files:
if stem not in img_files:
warnings.append(f" [NO IMAGE] {lbl_files[stem]}")
# ── Check 3–7: validate label content ────────────────────────────────────
for stem, lbl_fname in lbl_files.items():
lbl_path = os.path.join(lbl_dir, lbl_fname)
try:
with open(lbl_path, "r") as f:
lines = [l.strip() for l in f.readlines() if l.strip()]
except Exception as e:
errors.append(f" [READ ERROR] {lbl_fname}: {e}")
continue
if len(lines) == 0:
warnings.append(f" [EMPTY] {lbl_fname} — no annotations")
continue
for i, line in enumerate(lines, 1):
parts = line.split()
# Check 4: must have exactly 5 values
if len(parts) != 5:
errors.append(
f" [BAD FORMAT] {lbl_fname} line {i}: "
f"expected 5 values, got {len(parts)} → '{line}'"
)
continue
try:
cls_id = int(parts[0])
x, y, w, h = float(parts[1]), float(parts[2]), \
float(parts[3]), float(parts[4])
except ValueError:
errors.append(
f" [NOT NUMERIC] {lbl_fname} line {i}: '{line}'"
)
continue
# Check 3: class ID in range
if cls_id < 0 or cls_id >= NC:
errors.append(
f" [BAD CLASS ID] {lbl_fname} line {i}: "
f"class={cls_id} (valid range 0–{NC-1})"
)
# Check 5: bbox values in [0, 1]
for val_name, val in [("x", x), ("y", y), ("w", w), ("h", h)]:
if not (0.0 <= val <= 1.0):
errors.append(
f" [OUT OF RANGE] {lbl_fname} line {i}: "
f"{val_name}={val} (must be 0.0–1.0)"
)
total_boxes += 1
# ── Print split report ────────────────────────────────────────────────────
status = "PASS" if not errors else "FAIL"
print(f"\n [{status}] {split}/")
print(f" images : {total_images}")
print(f" labels : {total_labels}")
print(f" boxes : {total_boxes}")
if warnings:
print(f" warnings ({len(warnings)}):")
for w in warnings[:20]:
print(w)
if len(warnings) > 20:
print(f" ... and {len(warnings)-20} more warnings")
if errors:
print(f" errors ({len(errors)}):")
for e in errors[:30]:
print(e)
if len(errors) > 30:
print(f" ... and {len(errors)-30} more errors")
else:
print(" No errors found!")
return len(errors)
def main():
print("=" * 70)
print(" Label verification report")
print(f" Dataset : {DATASET_DIR}")
print(f" nc : {NC} classes (valid IDs: 0 – {NC-1})")
print("=" * 70)
total_errors = 0
for split in ("train", "valid", "test"):
result = verify_split(split)
if result:
total_errors += result
print("\n" + "=" * 70)
if total_errors == 0:
print(" ALL CHECKS PASSED — dataset is ready for training!")
else:
print(f" TOTAL ERRORS: {total_errors} — fix these before training.")
print("=" * 70)
# ── Class ID distribution ─────────────────────────────────────────────────
print("\n Class ID distribution across entire dataset:")
class_counts = {i: 0 for i in range(NC)}
for split in ("train", "valid", "test"):
lbl_dir = os.path.join(DATASET_DIR, split, "labels")
if not os.path.isdir(lbl_dir):
continue
for fname in os.listdir(lbl_dir):
if not fname.endswith(".txt"):
continue
with open(os.path.join(lbl_dir, fname), "r") as f:
for line in f:
parts = line.strip().split()
if parts:
try:
class_counts[int(parts[0])] += 1
except (ValueError, KeyError):
pass
class_names = [
'Mask','can','cellphone','electronics','gbottle','glove','metal',
'misc','net','pbag','pbottle','plastic','rod','sunglasses','tire',
'Microplastic','fiber','film','fragment','pallet'
]
print(f"\n {'ID':>3} {'Class':<15} {'Boxes':>8}")
print(f" {'─'*3} {'─'*15} {'─'*8}")
for i in range(NC):
name = class_names[i] if i < len(class_names) else f"class_{i}"
count = class_counts[i]
flag = " ← ZERO annotations!" if count == 0 else ""
print(f" {i:>3} {name:<15} {count:>8}{flag}")
if __name__ == "__main__":
main() |