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"""
Visualize ground truth annotations from COCO format on images.
This helps verify the accuracy of XML to COCO conversion.
"""
import os
import json
import sys
from pathlib import Path
import numpy as np
from PIL import Image, ImageDraw, ImageFont
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import matplotlib.colors as mcolors
# Add current directory to path
SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0, SCRIPT_DIR)
from original_annotations import load_ground_truth
try:
import pycocotools.mask as mask_util
HAS_PYCOCOTOOLS = True
except ImportError:
HAS_PYCOCOTOOLS = False
print("Warning: pycocotools not available. Mask visualization may be limited.")
def decode_rle(rle, width, height):
"""Decode COCO RLE to binary mask."""
if not HAS_PYCOCOTOOLS:
return None
try:
rle_decoded = rle.copy()
rle_decoded['counts'] = rle_decoded['counts'].encode('utf-8')
mask = mask_util.decode(rle_decoded)
return mask
except Exception as e:
print(f"Warning: Failed to decode RLE: {e}")
return None
def draw_coco_annotations(image_path, coco_json, output_path=None, show_labels=True):
"""
Draw COCO annotations on an image.
Args:
image_path: Path to image file
coco_json: COCO format dictionary
output_path: Path to save visualized image (if None, returns numpy array)
show_labels: Whether to show class labels
Returns:
numpy array of visualized image (if output_path is None)
"""
# Load image
img = Image.open(image_path).convert("RGB")
img_array = np.array(img)
# Get image info from COCO
image_name = os.path.basename(image_path)
img_info = None
for img_data in coco_json["images"]:
if img_data["file_name"] == image_name:
img_info = img_data
break
if img_info is None:
print(f"Warning: Image {image_name} not found in COCO data")
return img_array
img_id = img_info["id"]
# Get annotations for this image
annotations = [a for a in coco_json["annotations"] if a["image_id"] == img_id]
if len(annotations) == 0:
print(f"No annotations found for {image_name}")
return img_array
# Create category name map
id_to_name = {c["id"]: c["name"] for c in coco_json["categories"]}
# Create figure
fig, ax = plt.subplots(1, 1, figsize=(15, 20))
ax.imshow(img_array)
ax.axis("off")
ax.set_title(f"{image_name}\n({len(annotations)} annotations)",
fontsize=14, fontweight='bold', pad=20)
# Generate distinct colors for each category
num_categories = len(coco_json["categories"])
colors = plt.cm.tab20(np.linspace(0, 1, min(20, num_categories)))
if num_categories > 20:
# Use additional colormap for more categories
colors2 = plt.cm.Set3(np.linspace(0, 1, num_categories - 20))
colors = np.vstack([colors, colors2])
category_colors = {}
for idx, cat in enumerate(coco_json["categories"]):
category_colors[cat["id"]] = colors[idx % len(colors)]
# Draw each annotation
for ann in annotations:
cat_id = ann["category_id"]
cat_name = id_to_name.get(cat_id, f"category_{cat_id}")
color = category_colors.get(cat_id, [1, 0, 0, 0.5]) # Red fallback
# Get segmentation
segs = ann.get("segmentation", [])
bbox = ann.get("bbox", [0, 0, 0, 0])
# Draw segmentation (polygon or mask)
if segs:
if isinstance(segs, list) and len(segs) > 0:
# Check if it's RLE (dict) or polygon (list of coordinates)
if isinstance(segs, dict) or (isinstance(segs, list) and len(segs) > 0 and isinstance(segs[0], dict)):
# RLE mask
if isinstance(segs, list):
rle = segs[0]
else:
rle = segs
if HAS_PYCOCOTOOLS:
mask = decode_rle(rle, img_info["width"], img_info["height"])
if mask is not None:
# Draw mask with transparency
mask_colored = np.zeros((*mask.shape, 4))
mask_colored[mask > 0] = [*color[:3], 0.3] # Semi-transparent fill
ax.imshow(mask_colored, alpha=0.5)
# Draw mask outline
try:
from scipy import ndimage
contours = ndimage.binary_erosion(mask) ^ mask
ax.contour(contours, colors=[color[:3]], linewidths=2, alpha=0.8)
except ImportError:
# Fallback: just draw the mask without contour
pass
elif isinstance(segs[0], list) and len(segs[0]) >= 6:
# Polygon: flat list [x1, y1, x2, y2, ...]
coords = segs[0]
xs = coords[0::2]
ys = coords[1::2]
# Draw polygon with fill
poly = patches.Polygon(
list(zip(xs, ys)),
closed=True,
edgecolor=color[:3],
facecolor=color[:3],
linewidth=2.5,
alpha=0.3, # Semi-transparent fill
)
ax.add_patch(poly)
# Draw polygon outline
poly_edge = patches.Polygon(
list(zip(xs, ys)),
closed=True,
edgecolor=color[:3],
facecolor="none",
linewidth=2.5,
alpha=0.8, # More opaque edge
)
ax.add_patch(poly_edge)
# Draw bounding box if no segmentation or as fallback
if not segs or (isinstance(segs, list) and len(segs) == 0):
x, y, w, h = bbox
if w > 0 and h > 0:
rect = patches.Rectangle(
(x, y),
w,
h,
edgecolor=color[:3],
facecolor=color[:3],
linewidth=2.5,
alpha=0.3,
)
ax.add_patch(rect)
# Draw bbox outline
rect_edge = patches.Rectangle(
(x, y),
w,
h,
edgecolor=color[:3],
facecolor="none",
linewidth=2.5,
alpha=0.8,
)
ax.add_patch(rect_edge)
# Add label
if show_labels:
# Get position for label (use bbox or polygon center)
if segs and isinstance(segs, list) and len(segs) > 0:
if isinstance(segs[0], list) and len(segs[0]) >= 6:
# Polygon
coords = segs[0]
xs = coords[0::2]
ys = coords[1::2]
label_x = min(xs)
label_y = min(ys) - 10
else:
# Use bbox
x, y, w, h = bbox
label_x = x
label_y = y - 10
else:
x, y, w, h = bbox
label_x = x
label_y = y - 10
# Draw label with background
ax.text(
label_x,
label_y,
cat_name,
color='black',
fontsize=10,
fontweight='bold',
bbox=dict(
boxstyle="round,pad=0.5",
facecolor="white",
edgecolor=color[:3],
linewidth=2,
alpha=0.9,
),
zorder=10,
)
plt.tight_layout()
if output_path:
plt.savefig(output_path, dpi=150, bbox_inches='tight', facecolor='white')
plt.close()
print(f"Saved visualization to: {output_path}")
return None
else:
# Return as numpy array
fig.canvas.draw()
buf = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
buf = buf.reshape(fig.canvas.get_width_height()[::-1] + (3,))
plt.close()
return buf
def visualize_all_images(coco_json, images_dir, output_dir):
"""
Visualize annotations for all images in the COCO dataset.
Args:
coco_json: COCO format dictionary
images_dir: Directory containing images
output_dir: Directory to save visualized images
"""
os.makedirs(output_dir, exist_ok=True)
print(f"Visualizing {len(coco_json['images'])} images...")
for img_info in coco_json["images"]:
image_name = img_info["file_name"]
image_path = Path(images_dir) / image_name
if not image_path.exists():
print(f"Warning: Image {image_name} not found, skipping...")
continue
output_path = Path(output_dir) / f"{Path(image_name).stem}_annotated.png"
print(f"Processing {image_name}...")
draw_coco_annotations(
str(image_path),
coco_json,
output_path=str(output_path),
show_labels=True
)
print(f"\nAll visualizations saved to: {output_dir}")
def main():
"""Main function to visualize ground truth annotations."""
# Paths
data_dir = os.path.join(SCRIPT_DIR, "Aleyna 1 (2024)")
xml_path = os.path.join(data_dir, "Annotations", "annotations.xml")
images_dir = os.path.join(data_dir, "Images")
output_dir = os.path.join(SCRIPT_DIR, "visualizations_gt")
# Option 1: Load from existing COCO JSON
coco_json_path = os.path.join(SCRIPT_DIR, "ground_truth_coco.json")
if os.path.exists(coco_json_path):
print(f"Loading COCO JSON from: {coco_json_path}")
with open(coco_json_path, 'r') as f:
coco_json = json.load(f)
else:
# Option 2: Generate from XML
print(f"Loading from XML: {xml_path}")
coco_json = load_ground_truth(xml_path, images_dir)
if coco_json:
# Save for future use
with open(coco_json_path, 'w') as f:
json.dump(coco_json, f, indent=4)
print(f"Saved COCO JSON to: {coco_json_path}")
if not coco_json:
print("Error: Failed to load annotations")
return
print(f"\nLoaded {len(coco_json['images'])} images")
print(f"Loaded {len(coco_json['annotations'])} annotations")
print(f"Categories: {[c['name'] for c in coco_json['categories']]}")
# Visualize all images
visualize_all_images(coco_json, images_dir, output_dir)
print("\n" + "=" * 60)
print("Visualization complete!")
print("=" * 60)
print(f"\nCheck the visualizations in: {output_dir}")
print("Compare them with the original images to verify conversion accuracy.")
if __name__ == "__main__":
main()
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