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import cv2
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
def plot_depth_with_boxes(depth_map, depth_data):
"""
Plots the depth map with bounding boxes overlayed.
Args:
depth_map (numpy.ndarray): The depth map to visualize.
depth_data (pandas.DataFrame): DataFrame containing bounding box coordinates, depth statistics, and class labels.
"""
# Normalize the depth map for better visualization
depth_map_normalized = cv2.normalize(depth_map, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8)
# Create a figure and axis
fig, ax = plt.subplots(1, figsize=(12, 6))
# Display the depth map
ax.imshow(depth_map_normalized, cmap='plasma') # You can change the colormap as desired
ax.axis('off') # Hide the axes
# Loop through the DataFrame and add rectangles
for index, row in depth_data.iterrows():
xmin, ymin, xmax, ymax = row[['xmin', 'ymin', 'xmax', 'ymax']]
class_label = row['class']
score = row['depth_mean'] # or whichever statistic you prefer to display
# Create a rectangle patch
rect = patches.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, linewidth=2, edgecolor='yellow', facecolor='none')
# Add the rectangle to the plot
ax.add_patch(rect)
# Add a text label
ax.text(xmin, ymin - 5, f'{class_label}: {score:.2f}', color='white', fontsize=12, weight='bold')
plt.title('Depth Map with Object Detection Bounding Boxes', fontsize=16)
plt.show()
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