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title UI changes #2

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149 changes: 121 additions & 28 deletions app.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,7 @@
import cv2
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from collections import defaultdict
Expand All @@ -16,6 +17,9 @@
cap = cv2.VideoCapture(video_path)
net = cv2.dnn.readNetFromCaffe(prototxt_path, model_path)

# Load the store data
store_data = pd.read_csv('store.csv')

def detect_people(frame):
h, w = frame.shape[:2]
blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)), 0.007843, (300, 300), 127.5)
Expand Down Expand Up @@ -58,45 +62,129 @@ def calculate_footfall(frame, people):
return footfall

def plot_heatmap(footfall):
plt.figure(figsize=(10, 4))
sns.heatmap(footfall, cmap='hot', annot=True)
plt.figure(figsize=(8, 6))
sns.heatmap(footfall, cmap='YlOrRd', annot=True, fmt='.0f', cbar_kws={'label': 'Foot Traffic'})
plt.title('Customer Footfall Heatmap')
plt.xlabel('Grid X')
plt.ylabel('Grid Y')
plt.xlabel('Store Width')
plt.ylabel('Store Length')
return plt.gcf()

def generate_report(footfall):
deadspots = []
threshold = np.percentile(footfall, 25) # Define a threshold for low footfall areas

grid_size = footfall.shape
items_per_grid = len(store_data) // (grid_size[0] * grid_size[1])

report_data = []

for i in range(footfall.shape[0]):
for j in range(footfall.shape[1]):
if footfall[i, j] <= threshold:
deadspots.append((i, j))

report = f"Total Grids: {footfall.size}\n"
report += f"Deadspots Identified: {len(deadspots)}\n\n"
report += "Deadspots Locations (Grid Y, Grid X):\n"
for (y, x) in deadspots:
report += f"- Grid Y: {y + 1}, Grid X: {x + 1}\n"

return report
start_index = (i * grid_size[1] + j) * items_per_grid
end_index = start_index + items_per_grid
items = store_data.iloc[start_index:end_index]
for _, item in items.iterrows():
report_data.append({
"Grid Location": f"Y: {i+1}, X: {j+1}",
"Product": item['Product'],
"Brand": item['Brand'],
"Weekly Sales": f"${item['Weekly Sales']}"
})

report_df = pd.DataFrame(report_data)

summary = f"Total Grids: {footfall.size}\n"
summary += f"Deadspots Identified: {len(deadspots)}"

return summary, report_df

def get_best_selling_products_with_brands(category):
category_data = store_data[store_data['Category'] == category]
best_selling = category_data.groupby(['Product', 'Brand'])['Weekly Sales'].sum().sort_values(ascending=False).head(5)
return best_selling

def plot_best_selling_products_with_brands(data):
plt.figure(figsize=(12, 6))
ax = data.plot(kind='bar', color='skyblue', edgecolor='navy')
plt.title('Top 5 Best-Selling Products with Brands', fontsize=16)
plt.xlabel('Product (Brand)', fontsize=12)
plt.ylabel('Total Weekly Sales ($)', fontsize=12)
plt.xticks(rotation=45, ha='right')

# Modify x-axis labels to show Product (Brand)
labels = [f"{product} ({brand})" for product, brand in data.index]
ax.set_xticklabels(labels)

# Add value labels on top of each bar
for i, v in enumerate(data):
ax.text(i, v, f'${v:,.0f}', ha='center', va='bottom')

plt.tight_layout()
return plt.gcf()

def run_app():
st.title("Store Deadspot Detection")

# Layout with two columns
col1, col2 = st.columns(2)

with col1:
st.subheader("Video Feed")
frame_placeholder = st.empty()
st.subheader("Customer Footfall Heatmap")
heatmap_placeholder = st.empty()

with col2:
st.set_page_config(layout="wide", page_title="Store Analysis Dashboard")

# Apply custom CSS for layout and styling
st.markdown("""
<style>
.reportview-container .main .block-container {
padding-top: 2rem;
padding-bottom: 2rem;
max-width: 1200px;
}
.stPlotlyChart {
height: 400px;
}
.stDataFrame {
height: 400px;
overflow-y: auto;
}
.title {
color: #2ca02c;
font-size: 36px;
font-weight: bold;
text-align: center;
margin-top: 20px;
}
</style>
""", unsafe_allow_html=True)

st.markdown('<div class="title">Store Analysis Dashboard</div>', unsafe_allow_html=True)

# Create tabs for different sections
tab1, tab2, tab3 = st.tabs(["Live Feed & Heatmap", "Category Analysis", "Deadspots Report"])

with tab1:
col1, col2 = st.columns(2)

with col1:
st.subheader("Live Video Feed")
frame_placeholder = st.empty()

with col2:
st.subheader("Customer Footfall Heatmap")
heatmap_placeholder = st.empty()

with tab2:
st.subheader("Category Analysis")
categories = store_data['Category'].unique()
selected_category = st.selectbox("Select a category", categories)
best_selling = get_best_selling_products_with_brands(selected_category)

st.pyplot(plot_best_selling_products_with_brands(best_selling))

with tab3:
st.subheader("Deadspots Report")
report_placeholder = st.empty()
col3, col4 = st.columns([1, 3])

with col3:
summary_placeholder = st.empty()

with col4:
table_placeholder = st.empty()

# Start the video processing
while cap.isOpened():
Expand All @@ -109,18 +197,23 @@ def run_app():
frame_with_boxes = draw_bounding_boxes(frame, people)

# Update the video feed
frame_placeholder.image(frame_with_boxes, channels="BGR")
frame_placeholder.image(frame_with_boxes, channels="BGR", use_column_width=True)

# Calculate and display the heatmap
footfall = calculate_footfall(frame, people)
heatmap_placeholder.pyplot(plot_heatmap(footfall))

# Generate and display the report
report = generate_report(footfall)
report_placeholder.text(report)
summary, report_df = generate_report(footfall)
summary_placeholder.text(summary)
table_placeholder.dataframe(report_df)

# Add a short pause to reduce CPU usage
plt.pause(0.1)

# Release the video capture
cap.release()

if __name__ == "__main__":
run_app()