diff --git a/app.py b/app.py
index 287e541..18f3f7d 100644
--- a/app.py
+++ b/app.py
@@ -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
@@ -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)
@@ -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("""
+
+ """, unsafe_allow_html=True)
+
+ st.markdown('Store Analysis Dashboard
', 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():
@@ -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()
+