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98 lines
4.0 KiB
Python
98 lines
4.0 KiB
Python
import os
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import cv2
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import inspireface as isf
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import click
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import numpy as np
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race_tags = ["Black", "Asian", "Latino/Hispanic", "Middle Eastern", "White"]
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gender_tags = ["Female", "Male"]
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age_bracket_tags = [
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"0-2 years old", "3-9 years old", "10-19 years old", "20-29 years old", "30-39 years old",
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"40-49 years old", "50-59 years old", "60-69 years old", "more than 70 years old"
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]
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emotion_tags = ["Neutral", "Happy", "Sad", "Surprise", "Fear", "Disgust", "Anger"]
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@click.command()
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@click.argument('image_path')
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@click.option('--show', is_flag=True, help='Display the image with detected faces.')
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def case_face_detection_image(image_path, show):
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"""
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This is a sample application for face detection and tracking using an image.
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It also includes pipeline extensions such as RGB liveness, mask detection, and face quality evaluation.
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"""
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opt = isf.HF_ENABLE_FACE_RECOGNITION | isf.HF_ENABLE_QUALITY | isf.HF_ENABLE_MASK_DETECT | \
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isf.HF_ENABLE_LIVENESS | isf.HF_ENABLE_INTERACTION | isf.HF_ENABLE_FACE_ATTRIBUTE | isf.HF_ENABLE_FACE_EMOTION
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session = isf.InspireFaceSession(opt, isf.HF_DETECT_MODE_ALWAYS_DETECT)
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session.set_detection_confidence_threshold(0.5)
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# Load image
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image = cv2.imread(image_path)
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assert image is not None, "Please check that the image path is correct."
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# Dynamic drawing parameters (adjusted to image size)
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h, w = image.shape[:2]
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scale = max(w, h) / 1000.0
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line_thickness = max(1, int(2 * scale))
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circle_radius = max(1, int(1.5 * scale))
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font_scale = 0.5 * scale
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# Detect faces
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faces = session.face_detection(image)
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print(faces)
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print(f"face detection: {len(faces)} found")
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draw = image.copy()
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for idx, face in enumerate(faces):
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print(f"{'==' * 20}")
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print(f"idx: {idx}")
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print(f"detection confidence: {face.detection_confidence}")
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print(f"roll: {face.roll}, yaw: {face.yaw}, pitch: {face.pitch}")
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x1, y1, x2, y2 = face.location
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center = ((x1 + x2) / 2, (y1 + y2) / 2)
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size = (x2 - x1, y2 - y1)
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angle = face.roll
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rect = ((center[0], center[1]), (size[0], size[1]), angle)
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box = cv2.boxPoints(rect).astype(int)
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cv2.drawContours(draw, [box], 0, (100, 180, 29), line_thickness)
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# Draw landmark
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lmk = session.get_face_dense_landmark(face)
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for x, y in lmk.astype(int):
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cv2.circle(draw, (x, y), circle_radius, (220, 100, 0), -1)
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# Optional: Add detection confidence (text) on the face box
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# label = f"{face.detection_confidence:.2f}"
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# cv2.putText(draw, label, (x1, max(y1 - 10, 0)), cv2.FONT_HERSHEY_SIMPLEX,
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# font_scale, (255, 255, 255), line_thickness)
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# Execute extended functions (optional modules)
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select_exec_func = isf.HF_ENABLE_QUALITY | isf.HF_ENABLE_MASK_DETECT | \
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isf.HF_ENABLE_LIVENESS | isf.HF_ENABLE_INTERACTION | isf.HF_ENABLE_FACE_ATTRIBUTE | isf.HF_ENABLE_FACE_EMOTION
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extends = session.face_pipeline(image, faces, select_exec_func)
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print(extends)
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for idx, ext in enumerate(extends):
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print(f"{'==' * 20}")
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print(f"idx: {idx}")
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print(f"quality: {ext.quality_confidence}")
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print(f"rgb liveness: {ext.rgb_liveness_confidence}")
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print(f"face mask: {ext.mask_confidence}")
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print(f"face eyes status: left eye: {ext.left_eye_status_confidence} right eye: {ext.right_eye_status_confidence}")
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print(f"gender: {gender_tags[ext.gender]}")
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print(f"race: {race_tags[ext.race]}")
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print(f"age: {age_bracket_tags[ext.age_bracket]}")
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print(f"emotion: {emotion_tags[ext.emotion]}")
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# Save the annotated image
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save_path = os.path.join("tmp", "det.jpg")
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os.makedirs("tmp", exist_ok=True)
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cv2.imwrite(save_path, draw)
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if show:
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cv2.imshow("Face Detection", draw)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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print(f"\nSave annotated image to {save_path}")
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if __name__ == '__main__':
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case_face_detection_image()
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