# app.py
from flask import Flask, request, render_template, Response
import cv2
import numpy as np
import tensorflow as tf

# Load the TFLite model
interpreter = tf.lite.Interpreter(model_path=r'midas.tflite')
interpreter.allocate_tensors()

input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

app = Flask(__name__)

# Function to preprocess the image
def preprocess_image(image):
    image = cv2.resize(image, (256, 256))  # Resize to 256x256
    image = image.astype(np.float32) / 255.0  # Normalize to [0,1]
    image = np.expand_dims(image, axis=0)  # Add batch dimension
    return image

# Function to process the frame
def process_frame(frame):
    input_image = preprocess_image(frame)

    # Set the input tensor
    interpreter.set_tensor(input_details[0]['index'], input_image)

    # Run inference
    interpreter.invoke()

    # Get the output tensor
    depth_map = interpreter.get_tensor(output_details[0]['index'])

    # Process depth map
    depth_map = np.squeeze(depth_map)
    depth_map = (depth_map / np.max(depth_map) * 255).astype(np.uint8)
    depth_map_gray = cv2.cvtColor(depth_map, cv2.COLOR_GRAY2BGR)

    # Apply Canny edge detection on the original frame
    edges = cv2.Canny(frame, threshold1=100, threshold2=200)
    edges_colored = cv2.cvtColor(edges, cv2.COLOR_GRAY2BGR)

    # Resize edges_colored to match depth_map dimensions
    edges_colored = cv2.resize(edges_colored, depth_map_gray.shape[1::-1])

    # Create an overlay
    alpha = 0.5
    overlay = cv2.addWeighted(depth_map_gray, alpha, edges_colored, alpha, 0)
    overlay_gray = cv2.cvtColor(overlay, cv2.COLOR_BGR2GRAY)

    # Segment processing
    height, width = overlay_gray.shape
    segment_width = width // 7
    avg_pixel_densities = []

    for i in range(7):
        start_x = i * segment_width
        end_x = (i + 1) * segment_width if i < 6 else width
        segment_pixels = overlay_gray[:, start_x:end_x]
        avg_pixel_density = np.mean(segment_pixels)
        avg_pixel_densities.append(avg_pixel_density)

    # Draw vertical lines and pixel density values
    for i in range(7):
        x = i * segment_width
        cv2.line(overlay_gray, (x, 0), (x, height), (255, 255, 255), 1)
        cv2.putText(overlay_gray, f"{avg_pixel_densities[i]:.2f}", (x + 5, 40),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 255, 255), 1)

    # Draw a dot and horizontal line
    center_x = width // 2
    bottom_y = height - 10
    dot_position = (center_x, bottom_y)
    cv2.circle(overlay_gray, dot_position, 5, (255, 255, 255), -1)
    middle_y = height // 2
    cv2.line(overlay_gray, (0, middle_y), (width, middle_y), (255, 255, 255), 1)

    # Draw arrow
    lowest_index = np.argmin(avg_pixel_densities)
    lowest_x = lowest_index * segment_width + segment_width // 2
    arrow_end = (lowest_x, height // 2)
    cv2.arrowedLine(overlay_gray, dot_position, arrow_end, (255, 0, 0), 2, tipLength=0.1)

    return overlay_gray

@app.route('/')
def index():
    return render_template('depthmap.html')

@app.route('/video_feed', methods=['POST'])
def video_feed():
    # Receive the frame from the client
    frame_data = request.files['frame'].read()
    nparr = np.frombuffer(frame_data, np.uint8)
    frame = cv2.imdecode(nparr, cv2.IMREAD_COLOR)

    # Process the frame
    processed_frame = process_frame(frame)

    # Encode the processed frame as JPEG
    _, jpeg = cv2.imencode('.jpg', processed_frame)
    return Response(jpeg.tobytes(), mimetype='image/jpeg')

if __name__ == '__main__':
    app.run(debug=True)