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Update InspireFace to 1.1.4

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JingyuYan
2024-07-05 21:54:55 +08:00
parent a1b9206a64
commit dfc72baf25
82 changed files with 1448 additions and 408 deletions
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40
cpp-package/inspireface/python/sample_face_detection.py
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@@ -3,6 +3,12 @@ import cv2
import inspireface as ifac
from inspireface.param import *
import click
import numpy as np
race_tags = ["Black", "Asian", "Latino/Hispanic", "Middle Eastern", "White"]
gender_tags = ["Female", "Male", ]
age_bracket_tags = ["0-2 years old", "3-9 years old", "10-19 years old", "20-29 years old", "30-39 years old",
"40-49 years old", "50-59 years old", "60-69 years old", "more than 70 years old"]
@click.command()
@click.argument("resource_path")
@@ -17,7 +23,7 @@ def case_face_detection_image(resource_path, image_path):
assert ret, "Launch failure. Please ensure the resource path is correct."
# Optional features, loaded during session creation based on the modules specified.
opt = HF_ENABLE_FACE_RECOGNITION | HF_ENABLE_QUALITY | HF_ENABLE_MASK_DETECT | HF_ENABLE_LIVENESS
opt = HF_ENABLE_FACE_RECOGNITION | HF_ENABLE_QUALITY | HF_ENABLE_MASK_DETECT | HF_ENABLE_LIVENESS | HF_ENABLE_INTERACTION | HF_ENABLE_FACE_ATTRIBUTE
session = ifac.InspireFaceSession(opt, HF_DETECT_MODE_ALWAYS_DETECT)
# Load the image using OpenCV.
@@ -35,12 +41,33 @@ def case_face_detection_image(resource_path, image_path):
print(f"idx: {idx}")
# Print Euler angles of the face.
print(f"roll: {face.roll}, yaw: {face.yaw}, pitch: {face.pitch}")
# Draw bounding box around the detected face.
# Get face bounding box
x1, y1, x2, y2 = face.location
cv2.rectangle(draw, (x1, y1), (x2, y2), (0, 0, 255), 2)
# Calculate center, size, and angle
center = ((x1 + x2) / 2, (y1 + y2) / 2)
size = (x2 - x1, y2 - y1)
angle = face.roll # 这里使用 roll 角度
# Get rotation matrix
rotation_matrix = cv2.getRotationMatrix2D(center, angle, 1.0)
# Apply rotation to the bounding box corners
rect = ((center[0], center[1]), (size[0], size[1]), angle)
box = cv2.boxPoints(rect)
box = box.astype(int)
# Draw the rotated bounding box
cv2.drawContours(draw, [box], 0, (100, 180, 29), 2)
# Draw landmarks
lmk = session.get_face_dense_landmark(face)
for x, y in lmk.astype(int):
cv2.circle(draw, (x, y), 0, (220, 100, 0), 2)
# Features must be enabled during session creation to use them here.
select_exec_func = HF_ENABLE_QUALITY | HF_ENABLE_MASK_DETECT | HF_ENABLE_LIVENESS
select_exec_func = HF_ENABLE_QUALITY | HF_ENABLE_MASK_DETECT | HF_ENABLE_LIVENESS | HF_ENABLE_INTERACTION | HF_ENABLE_FACE_ATTRIBUTE
# Execute the pipeline to obtain richer face information.
extends = session.face_pipeline(image, faces, select_exec_func)
for idx, ext in enumerate(extends):
@@ -50,6 +77,11 @@ def case_face_detection_image(resource_path, image_path):
print(f"quality: {ext.quality_confidence}")
print(f"rgb liveness: {ext.rgb_liveness_confidence}")
print(f"face mask: {ext.mask_confidence}")
print(
f"face eyes status: left eye: {ext.left_eye_status_confidence} right eye: {ext.right_eye_status_confidence}")
print(f"gender: {gender_tags[ext.gender]}")
print(f"race: {race_tags[ext.race]}")
print(f"age: {age_bracket_tags[ext.age_bracket]}")
# Save the annotated image to the 'tmp/' directory.
save_path = os.path.join("tmp/", "det.jpg")