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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

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dmig 2018-12-09 13:21:44 +07:00
parent c8c481aed2
commit 1b8a7dc449
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1 changed files with 71 additions and 55 deletions
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126
src/compare.py
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@ -84,11 +84,33 @@ video_capture.grab()
timings.append(time.time()) timings.append(time.time())
# Import face recognition, takes some 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()) timings.append(time.time())
# Fetch the max frame height # Fetch the max frame height
max_height = config.getfloat("video", "max_height", fallback=0.0) 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 # Start the read loop
frames = 0 frames = 0
@ -105,80 +127,74 @@ while True:
stop(11) stop(11)
# Grab a single frame of video # Grab a single frame of video
# Don't remove ret, it doesn't work without it _, frame = video_capture.read()
ret, frame = video_capture.read() gsframe = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Create a histogram of the image with 8 values # 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 # 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 the image is fully black or the frame exceeds threshold,
if hist_total == 0: # skip to the next frame
if hist_total == 0 or (hist[0] / hist_total * 100 > dark_threshold):
dark_tries += 1 dark_tries += 1
continue 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 the hight is too high
if max_height < height: if scaling_factor != 1:
# Calculate the amount the image has to shrink
scaling_factor = max_height / float(height)
# Apply that factor to the frame # Apply that factor to the frame
frame = cv2.resize(frame, None, fx=scaling_factor, fy=scaling_factor, interpolation=cv2.INTER_AREA) frame = cv2.resize(frame, None, fx=scaling_factor, fy=scaling_factor, interpolation=cv2.INTER_AREA)
gsframe = cv2.resize(gsframe, None, fx=scaling_factor, fy=scaling_factor, interpolation=cv2.INTER_AREA)
# Save the new size for diagnostics
scale_height, scale_width = frame.shape[:2]
# Get all faces from that frame as encodings # 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 # 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 # 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 # Get best match
match_index = 0 match_index = np.argmin(matches)
for match in matches: match = matches[match_index]
match_index += 1
# Try to find a match that's confident enough # Check if a match that's confident enough
if 0 < match < video_certainty: if 0 < match < video_certainty:
timings.append(time.time()) timings.append(time.time())
# If set to true in the config, print debug text # If set to true in the config, print debug text
if end_report: if end_report:
def print_timing(label, offset): def print_timing(label, offset):
"""Helper function to print a timing from the list""" """Helper function to print a timing from the list"""
print(" %s: %dms" % (label, round((timings[1 + offset] - timings[offset]) * 1000))) print(" %s: %dms" % (label, round((timings[1 + offset] - timings[offset]) * 1000)))
print("Time spent") print("Time spent")
print_timing("Starting up", 0) print_timing("Starting up", 0)
print_timing("Opening the camera", 1) print_timing("Opening the camera", 1)
print_timing("Importing face_recognition", 2) print_timing("Importing recognition libs", 2)
print_timing("Searching for known face", 3) print_timing("Searching for known face", 3)
print("\nResolution") print("\nResolution")
print(" Native: %dx%d" % (height, width)) width = video_capture.get(cv2.CAP_PROP_FRAME_WIDTH) or 1
print(" Used: %dx%d" % (scale_height, scale_width)) 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 # 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("\nFrames searched: %d (%.2f fps)" % (frames, frames / (timings[4] - timings[3])))
print("Dark frames ignored: %d " % (dark_tries, )) print("Dark frames ignored: %d " % (dark_tries, ))
print("Certainty of winning frame: %.3f" % (match * 10, )) print("Certainty of winning frame: %.3f" % (match * 10, ))
# Catch older 3-encoding models print("Winning model: %d (\"%s\")" % (match_index, models[match_index]["label"]))
if not match_index in models:
match_index = 0
print("Winning model: %d (\"%s\")" % (match_index, models[match_index]["label"])) # End peacefully
stop(0)
# End peacefully
stop(0)