replace face_recognition with dlib+numpy; support for cnn detector; calculate scaling_factor outside of main cycle; use grayscale image for darkframe and face detection; fix winning model index detection; some minor code cleanups
This commit is contained in:
dmig
parent
c8c481aed2
commit
1b8a7dc449
1 changed files with 71 additions and 55 deletions
126
src/compare.py
126
src/compare.py
|
|
@ -84,11 +84,33 @@ video_capture.grab()
|
|||
timings.append(time.time())
|
||||
|
||||
# Import face recognition, takes some time
|
||||
import face_recognition
|
||||
import dlib
|
||||
import numpy as np
|
||||
|
||||
use_cnn = config.getboolean('core', 'use_cnn', fallback=False)
|
||||
if use_cnn:
|
||||
face_detector = dlib.cnn_face_detection_model_v1(
|
||||
PATH + '/dlib-data/mmod_human_face_detector.dat'
|
||||
)
|
||||
else:
|
||||
face_detector = dlib.get_frontal_face_detector()
|
||||
|
||||
pose_predictor = dlib.shape_predictor(
|
||||
PATH + '/dlib-data/shape_predictor_5_face_landmarks.dat'
|
||||
)
|
||||
face_encoder = dlib.face_recognition_model_v1(
|
||||
PATH + '/dlib-data/dlib_face_recognition_resnet_model_v1.dat'
|
||||
)
|
||||
|
||||
timings.append(time.time())
|
||||
|
||||
# Fetch the max frame height
|
||||
max_height = config.getfloat("video", "max_height", fallback=0.0)
|
||||
# Get the height of the image
|
||||
height = video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT) or 1
|
||||
|
||||
# Calculate the amount the image has to shrink
|
||||
scaling_factor = (max_height / height) or 1
|
||||
|
||||
# Start the read loop
|
||||
frames = 0
|
||||
|
|
@ -105,80 +127,74 @@ while True:
|
|||
stop(11)
|
||||
|
||||
# Grab a single frame of video
|
||||
# Don't remove ret, it doesn't work without it
|
||||
ret, frame = video_capture.read()
|
||||
_, frame = video_capture.read()
|
||||
gsframe = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
|
||||
|
||||
# Create a histogram of the image with 8 values
|
||||
hist = cv2.calcHist([frame], [0], None, [8], [0, 256])
|
||||
hist = cv2.calcHist([gsframe], [0], None, [8], [0, 256])
|
||||
# All values combined for percentage calculation
|
||||
hist_total = int(sum(hist)[0])
|
||||
hist_total = np.sum(hist)
|
||||
|
||||
# If the image is fully black, skip to the next frame
|
||||
if hist_total == 0:
|
||||
# If the image is fully black or the frame exceeds threshold,
|
||||
# skip to the next frame
|
||||
if hist_total == 0 or (hist[0] / hist_total * 100 > dark_threshold):
|
||||
dark_tries += 1
|
||||
continue
|
||||
|
||||
# Scrip the frame if it exceeds the threshold
|
||||
if float(hist[0]) / hist_total * 100 > dark_threshold:
|
||||
dark_tries += 1
|
||||
continue
|
||||
|
||||
# Get the height and with of the image
|
||||
height, width = frame.shape[:2]
|
||||
|
||||
# If the hight is too high
|
||||
if max_height < height:
|
||||
# Calculate the amount the image has to shrink
|
||||
scaling_factor = max_height / float(height)
|
||||
if scaling_factor != 1:
|
||||
# Apply that factor to the frame
|
||||
frame = cv2.resize(frame, None, fx=scaling_factor, fy=scaling_factor, interpolation=cv2.INTER_AREA)
|
||||
|
||||
# Save the new size for diagnostics
|
||||
scale_height, scale_width = frame.shape[:2]
|
||||
gsframe = cv2.resize(gsframe, None, fx=scaling_factor, fy=scaling_factor, interpolation=cv2.INTER_AREA)
|
||||
|
||||
# Get all faces from that frame as encodings
|
||||
face_encodings = face_recognition.face_encodings(frame)
|
||||
face_locations = face_detector(gsframe, 1) # upsample 1 time
|
||||
|
||||
# Loop through each face
|
||||
for face_encoding in face_encodings:
|
||||
for fl in face_locations:
|
||||
if use_cnn:
|
||||
fl = fl.rect
|
||||
|
||||
face_landmark = pose_predictor(frame, fl)
|
||||
face_encoding = np.array(
|
||||
face_encoder.compute_face_descriptor(frame, face_landmark, 1) # num_jitters=1
|
||||
)
|
||||
# Match this found face against a known face
|
||||
matches = face_recognition.face_distance(encodings, face_encoding)
|
||||
matches = np.linalg.norm(encodings - face_encoding, axis=1)
|
||||
|
||||
# Check if any match is certain enough to be the user we're looking for
|
||||
match_index = 0
|
||||
for match in matches:
|
||||
match_index += 1
|
||||
# Get best match
|
||||
match_index = np.argmin(matches)
|
||||
match = matches[match_index]
|
||||
|
||||
# Try to find a match that's confident enough
|
||||
if 0 < match < video_certainty:
|
||||
timings.append(time.time())
|
||||
# Check if a match that's confident enough
|
||||
if 0 < match < video_certainty:
|
||||
timings.append(time.time())
|
||||
|
||||
# If set to true in the config, print debug text
|
||||
if end_report:
|
||||
def print_timing(label, offset):
|
||||
"""Helper function to print a timing from the list"""
|
||||
print(" %s: %dms" % (label, round((timings[1 + offset] - timings[offset]) * 1000)))
|
||||
# If set to true in the config, print debug text
|
||||
if end_report:
|
||||
def print_timing(label, offset):
|
||||
"""Helper function to print a timing from the list"""
|
||||
print(" %s: %dms" % (label, round((timings[1 + offset] - timings[offset]) * 1000)))
|
||||
|
||||
print("Time spent")
|
||||
print_timing("Starting up", 0)
|
||||
print_timing("Opening the camera", 1)
|
||||
print_timing("Importing face_recognition", 2)
|
||||
print_timing("Searching for known face", 3)
|
||||
print("Time spent")
|
||||
print_timing("Starting up", 0)
|
||||
print_timing("Opening the camera", 1)
|
||||
print_timing("Importing recognition libs", 2)
|
||||
print_timing("Searching for known face", 3)
|
||||
|
||||
print("\nResolution")
|
||||
print(" Native: %dx%d" % (height, width))
|
||||
print(" Used: %dx%d" % (scale_height, scale_width))
|
||||
print("\nResolution")
|
||||
width = video_capture.get(cv2.CAP_PROP_FRAME_WIDTH) or 1
|
||||
print(" Native: %dx%d" % (height, width))
|
||||
# Save the new size for diagnostics
|
||||
scale_height, scale_width = frame.shape[:2]
|
||||
print(" Used: %dx%d" % (scale_height, scale_width))
|
||||
|
||||
# Show the total number of frames and calculate the FPS by deviding it by the total scan time
|
||||
print("\nFrames searched: %d (%.2f fps)" % (frames, frames / (timings[4] - timings[3])))
|
||||
print("Dark frames ignored: %d " % (dark_tries, ))
|
||||
print("Certainty of winning frame: %.3f" % (match * 10, ))
|
||||
# Show the total number of frames and calculate the FPS by deviding it by the total scan time
|
||||
print("\nFrames searched: %d (%.2f fps)" % (frames, frames / (timings[4] - timings[3])))
|
||||
print("Dark frames ignored: %d " % (dark_tries, ))
|
||||
print("Certainty of winning frame: %.3f" % (match * 10, ))
|
||||
|
||||
# Catch older 3-encoding models
|
||||
if not match_index in models:
|
||||
match_index = 0
|
||||
print("Winning model: %d (\"%s\")" % (match_index, models[match_index]["label"]))
|
||||
|
||||
print("Winning model: %d (\"%s\")" % (match_index, models[match_index]["label"]))
|
||||
|
||||
# End peacefully
|
||||
stop(0)
|
||||
# End peacefully
|
||||
stop(0)
|
||||
|
|
|
|||
Loading…
Reference in a new issue