ATRComplete.py

import numpy as np
import cv2 as cv2
import time
import face_recognition
from tracker import EuclideanDistTracker

# EpocCam
camera_input = 0
# Computer camera
#camera_input = 1

# initialize the trackers
body_tracker = EuclideanDistTracker("Person")

# initialize the HOG descriptor/person detector
hog = cv2.HOGDescriptor()
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())


# Identification
known_face_encodings = np.load('known_face_encodings.npy')
known_face_names = []
with open('known_face_names.txt', 'r') as f:
    for line in f:
        known_face_names.append(line[:-1])
print(known_face_names)

# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True

cv2.startWindowThread()

# open webcam video stream
# object for video capture, can receive either video file or an index corresponding to a camera
# currently, 0 is for elgato, and 1 is for computer camera
cap = cv2.VideoCapture(camera_input)

# the output will be written to output.avi
out = cv2.VideoWriter('output.avi', fourcc=cv2.VideoWriter_fourcc(*'MJPG'), fps=7., frameSize=(640, 400))
frame_count = 0
detect_times = []
frame_times = []
while True:
    # measurements I take for performance analysis
    t0 = time.time()
    frame_count += 1

    # Capture frame-by-frame, the frequency is limited by computer's camera or waitkey() command
    ret, frame = cap.read()
    frame_width = cap.get(3)
    frame_height = cap.get(4)

    # resizing for faster processing, this determines the picture size for imshow.
    # If we make it smaller, then go fullscreen, then the image is pixelated
    # incoming frames are at (1280, 720)=720p, my screen resolution is 2560x1600
    frame = cv2.resize(frame, (640, 400))

    # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
    rgb_frame = frame[:, :, ::-1]
    # using a greyscale picture, also for faster detection
    gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)

    td0 = time.time()

    # detect people and faces in the image
    # returns the bounding boxes for the detected objects
    bodies, weights_body = hog.detectMultiScale(img=gray, padding=(8, 8), scale=1.03, winStride=(8, 8))

    # convert bounding boxes to new format
    bodies = np.array([[x, y, w, h] for (x, y, w, h) in bodies])

    # id handling
    bodies_ids = body_tracker.update(bodies)

    # display the detected body boxes in the colour picture
    for body_id in bodies_ids:
        x, y, w, h, id = body_id
        cv2.putText(frame, 'Person '+str(id), (x, y - 15), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)
        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

    # Only process every other frame of video to save time
    if process_this_frame:
        # Find all the faces and face encodings in the current frame of video
        face_locations = face_recognition.face_locations(rgb_frame)
        face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)

        face_names = []
        for face_encoding in face_encodings:
            # See if the face is a match for the known face(s)
            matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
            name = "Unknown"
            face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
            best_match_index = np.argmin(face_distances)
            if matches[best_match_index]:
                name = known_face_names[best_match_index]

            face_names.append(name)

    process_this_frame = not process_this_frame

    # Display the results
    for (top, right, bottom, left), name in zip(face_locations, face_names):
        # Scale back up face locations since the frame we detected in was scaled to 1/4 size

        # Draw a box around the face
        cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 2)

        # Draw a label with a name below the face
        cv2.rectangle(frame, (left, bottom - 17), (right, bottom), (0, 255, 0), cv2.FILLED)
        font = cv2.FONT_HERSHEY_DUPLEX
        cv2.putText(frame, name, (left + 4, bottom - 4), font, 1, (0, 0, 255), 2)

    # Display the resulting image
    cv2.imshow('Image', frame)

    td1 = time.time()
    detect_times.append(td1-td0)
    out.write(frame)
    if cv2.waitKey(50) & 0xFF == ord('q'):
        break
    t1 = time.time()
    frame_times.append(t1-t0)

# When everything done, release the capture
cap.release()
# and release the output
out.release()
# finally, close the window
cv2.destroyAllWindows()
cv2.waitKey(1)