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import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import os
from collections import defaultdict
import numpy as np
def generate_heatmap_from_grid(heatmap_grid, output_path):
"""Generate a heatmap visualization from a grid of values"""
plt.figure(figsize=(10, 8))
sns.heatmap(heatmap_grid, cmap='hot', annot=False)
plt.title('Customer Traffic Heatmap')
plt.tight_layout()
plt.savefig(output_path)
plt.close()
return output_path
def generate_dwell_time_chart(dwell_df, output_path):
"""Generate a chart showing dwell time per shelf"""
plt.figure(figsize=(12, 6))
# Sort by dwell time descending
sorted_df = dwell_df.sort_values('dwell_time', ascending=False)
# Create bar chart
plt.bar(sorted_df['shelf_id'], sorted_df['dwell_time'], color='purple')
plt.title('Average Dwell Time per Shelf', fontsize=14)
plt.xlabel('Shelf ID')
plt.ylabel('Time (seconds)')
plt.xticks(rotation=45)
plt.tight_layout()
plt.savefig(output_path)
plt.close()
return output_path
def generate_action_distribution_chart(action_summary_df, output_path):
"""Generate a chart showing distribution of customer actions"""
plt.figure(figsize=(10, 6))
# Create pie chart
action_summary_df.plot.pie(y='count', autopct='%1.1f%%', startangle=90,
labels=action_summary_df['action'])
plt.ylabel('')
plt.title('Distribution of Customer Actions', fontsize=14)
plt.tight_layout()
plt.savefig(output_path)
plt.close()
return output_path
def generate_journey_chart(journey_df, output_path):
"""Generate a stacked bar chart showing customer journey outcomes per shelf"""
plt.figure(figsize=(12, 6))
# Create stacked bar chart
journey_df.plot(
kind='bar',
stacked=True,
figsize=(12, 6),
color=['#2ca02c', '#ff7f0e', '#d62728'] # Green, Orange, Red
)
plt.title('Customer Journey Analysis per Shelf', fontsize=14)
plt.xlabel('Shelf ID')
plt.ylabel('Percentage')
plt.legend(title='Journey Outcome')
plt.xticks(rotation=45)
plt.tight_layout()
plt.savefig(output_path)
plt.close()
return output_path
def generate_rak_timeline(interaction_csv_path, tracks, shelf_boxes_per_frame, fps, output_path):
"""Generate a timeline visualization of rack interactions"""
# Load rack interaction data
rak_df = pd.read_csv(interaction_csv_path)
valid_raks = set(rak_df['shelf_id'].tolist())
# Build timeline per rack
rak_timeline = defaultdict(list)
for pid, dets in tracks.items():
for d in dets:
f = d['frame']
x1, y1, x2, y2 = d['bbox']
px, py = (x1 + x2) / 2, (y1 + y2) / 2
for sid, (sx1, sy1, sx2, sy2) in shelf_boxes_per_frame.get(f, []):
if sid not in valid_raks:
continue
if sx1 <= px <= sx2 and sy1 <= py <= sy2:
rak_timeline[sid].append(f)
# Create visualization
plt.figure(figsize=(12, max(4, len(rak_timeline) * 0.4)))
for i, (rak_id, frames) in enumerate(sorted(rak_timeline.items())):
if not frames:
continue
frames = sorted(frames)
start = frames[0]
for j in range(1, len(frames)):
if frames[j] != frames[j-1] + 1:
plt.plot([start / fps, frames[j-1] / fps], [i, i], linewidth=6)
start = frames[j]
plt.plot([start / fps, frames[-1] / fps], [i, i], linewidth=6)
plt.text(-1, i, rak_id, verticalalignment='center', fontsize=8)
plt.xlabel('Time (seconds)')
plt.title('Timeline of Shelf Interactions')
plt.yticks([])
plt.tight_layout()
plt.savefig(output_path)
plt.close()
return output_path |