Files added for Drowsiness Detection post

Author: veb-101

Driver-Drowsiness-detection-using-Mediapipe-in-Python/.gitignore

@@ -0,0 +1,2 @@
+/.vscode
+__pycache__

Driver-Drowsiness-detection-using-Mediapipe-in-Python/Blog-Code-Demo.ipynb

@@ -0,0 +1,23 @@
+# Guide to Driver Drowsines Detection using Mediapipe in Python
+
+**This repository contains code for blog post: [Guide to Driver Drowsines Detection using Mediapipe in Python](https://learnopencv.com/guide-to-driver-drowsiness-detection-using-mediapipe-in-python/) blog post**.
+
+In this post, we will:
+
+* Learn how to detect eye landmarks using the Mediapipe Face Mesh solution pipeline in python.
+* Introduce and demonstrate the Eye Aspect Ratio (EAR) technique.
+* Create a Drowsiness Detection web application using streamlit.
+* Use streamlit-webrtc to help transmit real-time video/audio streams over the network.
+* Deploy it on a cloud service.
+
+<img src = 'app_image/page_SS.jpg'>
+
+# AI Courses by OpenCV
+
+Want to become an expert in AI? [AI Courses by OpenCV](https://opencv.org/courses/) is a great place to start.
+
+<a href="https://opencv.org/courses/">
+<p align="center">
+<img src="https://www.learnopencv.com/wp-content/uploads/2020/04/AI-Courses-By-OpenCV-Github.png">
+</p>
+</a>

Driver-Drowsiness-detection-using-Mediapipe-in-Python/README.md

@@ -0,0 +1,31 @@
+css_string = """
+             <style>
+
+            .sidebar {max-width:100%; float:right}
+            .side-block img {width:100%}
+            .side-block {margin-bottom:15px}
+            .side-block a.button {margin-bottom:30px; background: #006CFF; color: #fff;font-weight: 500;font-size: 16px;line-height: 20px;padding: 15px;display: inline-block;max-width: 300px;border-radius: 5px; text-decoration:none;   }
+            .side-block a.button:hover {background:#000}
+
+            </style> 
+
+            <div class="sidebar">
+
+            <div class="side-block">
+            <a target="_blank" href="https://opencv.org/courses" rel="noopener">
+            <img src="https://learnopencv.com/wp-content/uploads/2022/03/opencv-course1.png" alt="Opencv Courses">
+            </div>
+            <div class="side-block">
+            <a href="https://learnopencv.com" class="button ">Subscribe To My Newsletter</a>
+            <div class="side-block">
+            </div>
+            <a target="_blank" href="https://pallet.xyz/list/ai-jobs?" rel="noopener">
+            <img src="https://learnopencv.com/wp-content/uploads/2022/02/learnopencv-ai-jobs.jpg" alt="Opencv Courses">
+            </div>
+            <div class="side-block">
+            <a target="_blank" href="https://bigvision.ai" rel="noopener">
+            <img src="https://learnopencv.com/wp-content/uploads/2022/02/bigvision.jpg" alt="Opencv Courses"></a>
+            </div>
+            </div> 
+
+            """

Driver-Drowsiness-detection-using-Mediapipe-in-Python/ads.py

@@ -0,0 +1,93 @@
+import av
+import numpy as np
+from pydub import AudioSegment
+
+
+class AudioFrameHandler:
+    """To play/pass custom audio based on some event"""
+
+    def __init__(self, sound_file_path: str = ""):
+
+        self.custom_audio = AudioSegment.from_file(file=sound_file_path, format="wav")
+        self.custom_audio_len = len(self.custom_audio)
+
+        self.ms_per_audio_segment: int = 20
+        self.audio_segment_shape: tuple
+
+        self.play_state_tracker: dict = {"curr_segment": -1}  # Currently playing segment
+        self.audio_segments_created: bool = False
+        self.audio_segments: list = []
+
+    def prepare_audio(self, frame: av.AudioFrame):
+        raw_samples = frame.to_ndarray()
+        sound = AudioSegment(
+            data=raw_samples.tobytes(),
+            sample_width=frame.format.bytes,
+            frame_rate=frame.sample_rate,
+            channels=len(frame.layout.channels),
+        )
+
+        self.ms_per_audio_segment = len(sound)
+        self.audio_segment_shape = raw_samples.shape
+
+        self.custom_audio = self.custom_audio.set_channels(sound.channels)
+        self.custom_audio = self.custom_audio.set_frame_rate(sound.frame_rate)
+        self.custom_audio = self.custom_audio.set_sample_width(sound.sample_width)
+
+        self.audio_segments = [
+            self.custom_audio[i : i + self.ms_per_audio_segment]
+            for i in range(0, self.custom_audio_len - self.custom_audio_len % self.ms_per_audio_segment, self.ms_per_audio_segment)
+        ]
+        self.total_segments = len(self.audio_segments) - 1  # -1 because we start from 0.
+
+        self.audio_segments_created = True
+
+    def process(self, frame: av.AudioFrame, play_sound: bool = False):
+
+        """
+        Takes in the current input audio frame and based on play_sound boolean value
+        either starts sending the custom audio frame or dampens the frame wave to emulate silence.
+
+        For eg. playing a notification based on some event.
+        """
+
+        if not self.audio_segments_created:
+            self.prepare_audio(frame)
+
+        raw_samples = frame.to_ndarray()
+        _curr_segment = self.play_state_tracker["curr_segment"]
+
+        if play_sound:
+            if _curr_segment < self.total_segments:
+                _curr_segment += 1
+            else:
+                _curr_segment = 0
+
+            sound = self.audio_segments[_curr_segment]
+
+        else:
+            if -1 < _curr_segment < self.total_segments:
+                _curr_segment += 1
+                sound = self.audio_segments[_curr_segment]
+            else:
+                _curr_segment = -1
+                sound = AudioSegment(
+                    data=raw_samples.tobytes(),
+                    sample_width=frame.format.bytes,
+                    frame_rate=frame.sample_rate,
+                    channels=len(frame.layout.channels),
+                )
+                sound = sound.apply_gain(-100)
+
+        self.play_state_tracker["curr_segment"] = _curr_segment
+
+        channel_sounds = sound.split_to_mono()
+        channel_samples = [s.get_array_of_samples() for s in channel_sounds]
+
+        new_samples = np.array(channel_samples).T
+
+        new_samples = new_samples.reshape(self.audio_segment_shape)
+        new_frame = av.AudioFrame.from_ndarray(new_samples, layout=frame.layout.name)
+        new_frame.sample_rate = frame.sample_rate
+
+        return new_frame

Driver-Drowsiness-detection-using-Mediapipe-in-Python/app_image/page_SS.jpg

@@ -0,0 +1,186 @@
+import cv2
+import time
+import numpy as np
+import mediapipe as mp
+from mediapipe.python.solutions.drawing_utils import _normalized_to_pixel_coordinates as denormalize_coordinates
+
+
+def get_mediapipe_app(
+    max_num_faces=1,
+    refine_landmarks=True,
+    min_detection_confidence=0.5,
+    min_tracking_confidence=0.5,
+):
+    """Initialize and return Mediapipe FaceMesh Solution Graph object"""
+    face_mesh = mp.solutions.face_mesh.FaceMesh(
+        max_num_faces=max_num_faces,
+        refine_landmarks=refine_landmarks,
+        min_detection_confidence=min_detection_confidence,
+        min_tracking_confidence=min_tracking_confidence,
+    )
+
+    return face_mesh
+
+
+def distance(point_1, point_2):
+    """Calculate l2-norm between two points"""
+    dist = sum([(i - j) ** 2 for i, j in zip(point_1, point_2)]) ** 0.5
+    return dist
+
+
+def get_ear(landmarks, refer_idxs, frame_width, frame_height):
+    """
+    Calculate Eye Aspect Ratio for one eye.
+
+    Args:
+        landmarks: (list) Detected landmarks list
+        refer_idxs: (list) Index positions of the chosen landmarks
+                            in order P1, P2, P3, P4, P5, P6
+        frame_width: (int) Width of captured frame
+        frame_height: (int) Height of captured frame
+
+    Returns:
+        ear: (float) Eye aspect ratio
+    """
+    try:
+        # Compute the euclidean distance between the horizontal
+        coords_points = []
+        for i in refer_idxs:
+            lm = landmarks[i]
+            coord = denormalize_coordinates(lm.x, lm.y, frame_width, frame_height)
+            coords_points.append(coord)
+
+        # Eye landmark (x, y)-coordinates
+        P2_P6 = distance(coords_points[1], coords_points[5])
+        P3_P5 = distance(coords_points[2], coords_points[4])
+        P1_P4 = distance(coords_points[0], coords_points[3])
+
+        # Compute the eye aspect ratio
+        ear = (P2_P6 + P3_P5) / (2.0 * P1_P4)
+
+    except:
+        ear = 0.0
+        coords_points = None
+
+    return ear, coords_points
+
+
+def calculate_avg_ear(landmarks, left_eye_idxs, right_eye_idxs, image_w, image_h):
+    # Calculate Eye aspect ratio
+
+    left_ear, left_lm_coordinates = get_ear(landmarks, left_eye_idxs, image_w, image_h)
+    right_ear, right_lm_coordinates = get_ear(landmarks, right_eye_idxs, image_w, image_h)
+    Avg_EAR = (left_ear + right_ear) / 2.0
+
+    return Avg_EAR, (left_lm_coordinates, right_lm_coordinates)
+
+
+def plot_eye_landmarks(frame, left_lm_coordinates, right_lm_coordinates, color):
+    for lm_coordinates in [left_lm_coordinates, right_lm_coordinates]:
+        if lm_coordinates:
+            for coord in lm_coordinates:
+                cv2.circle(frame, coord, 2, color, -1)
+
+    frame = cv2.flip(frame, 1)
+    return frame
+
+
+def plot_text(image, text, origin, color, font=cv2.FONT_HERSHEY_SIMPLEX, fntScale=0.8, thickness=2):
+    image = cv2.putText(image, text, origin, font, fntScale, color, thickness)
+    return image
+
+
+class VideoFrameHandler:
+    def __init__(self):
+        """
+        Initialize the necessary constants, mediapipe app
+        and tracker variables
+        """
+        # Left and right eye chosen landmarks.
+        self.eye_idxs = {
+            "left": [362, 385, 387, 263, 373, 380],
+            "right": [33, 160, 158, 133, 153, 144],
+        }
+
+        # Used for coloring landmark points.
+        # Its value depends on the current EAR value.
+        self.RED = (0, 0, 255)  # BGR
+        self.GREEN = (0, 255, 0)  # BGR
+
+        # Initializing Mediapipe FaceMesh solution pipeline
+        self.facemesh_model = get_mediapipe_app()
+
+        # For tracking counters and sharing states in and out of callbacks.
+        self.state_tracker = {
+            "start_time": time.perf_counter(),
+            "DROWSY_TIME": 0.0,  # Holds the amount of time passed with EAR < EAR_THRESH
+            "COLOR": self.GREEN,
+            "play_alarm": False,
+        }
+
+        self.EAR_txt_pos = (10, 30)
+
+    def process(self, frame: np.array, thresholds: dict):
+        """
+        This function is used to implement our Drowsy detection algorithm
+
+        Args:
+            frame: (np.array) Input frame matrix.
+            thresholds: (dict) Contains the two threshold values
+                               WAIT_TIME and EAR_THRESH.
+
+        Returns:
+            The processed frame and a boolean flag to
+            indicate if the alarm should be played or not.
+        """
+
+        # To improve performance,
+        # mark the frame as not writeable to pass by reference.
+        frame.flags.writeable = False
+        frame_h, frame_w, _ = frame.shape
+
+        DROWSY_TIME_txt_pos = (10, int(frame_h // 2 * 1.7))
+        ALM_txt_pos = (10, int(frame_h // 2 * 1.85))
+
+        results = self.facemesh_model.process(frame)
+
+        if results.multi_face_landmarks:
+            landmarks = results.multi_face_landmarks[0].landmark
+            EAR, coordinates = calculate_avg_ear(landmarks, self.eye_idxs["left"], self.eye_idxs["right"], frame_w, frame_h)
+            frame = plot_eye_landmarks(frame, coordinates[0], coordinates[1], self.state_tracker["COLOR"])
+
+            if EAR < thresholds["EAR_THRESH"]:
+
+                # Increase DROWSY_TIME to track the time period with EAR less than the threshold
+                # and reset the start_time for the next iteration.
+                end_time = time.perf_counter()
+
+                self.state_tracker["DROWSY_TIME"] += end_time - self.state_tracker["start_time"]
+                self.state_tracker["start_time"] = end_time
+                self.state_tracker["COLOR"] = self.RED
+
+                if self.state_tracker["DROWSY_TIME"] >= thresholds["WAIT_TIME"]:
+                    self.state_tracker["play_alarm"] = True
+                    plot_text(frame, "WAKE UP! WAKE UP", ALM_txt_pos, self.state_tracker["COLOR"])
+
+            else:
+                self.state_tracker["start_time"] = time.perf_counter()
+                self.state_tracker["DROWSY_TIME"] = 0.0
+                self.state_tracker["COLOR"] = self.GREEN
+                self.state_tracker["play_alarm"] = False
+
+            EAR_txt = f"EAR: {round(EAR, 2)}"
+            DROWSY_TIME_txt = f"DROWSY: {round(self.state_tracker['DROWSY_TIME'], 3)} Secs"
+            plot_text(frame, EAR_txt, self.EAR_txt_pos, self.state_tracker["COLOR"])
+            plot_text(frame, DROWSY_TIME_txt, DROWSY_TIME_txt_pos, self.state_tracker["COLOR"])
+
+        else:
+            self.state_tracker["start_time"] = time.perf_counter()
+            self.state_tracker["DROWSY_TIME"] = 0.0
+            self.state_tracker["COLOR"] = self.GREEN
+            self.state_tracker["play_alarm"] = False
+
+            # Flip the frame horizontally for a selfie-view display.
+            frame = cv2.flip(frame, 1)
+
+        return frame, self.state_tracker["play_alarm"]

Driver-Drowsiness-detection-using-Mediapipe-in-Python/audio/wake_up.wav

@@ -0,0 +1,9 @@
+cryptography==38.0.0
+pyOpenSSL
+aiortc
+numpy
+pydub
+mediapipe
+streamlit==1.13.0
+streamlit_webrtc==0.43.4
+streamlit-nested-layout==0.1.1