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@@ -52,3 +52,14 @@ dependency/openvino/libopenvino_tensorflow_fe.so filter=lfs diff=lfs merge=lfs -
 dependency/openvino/libopenvino.so filter=lfs diff=lfs merge=lfs -text
 dependency/openvino/pcie-ma2x8x.mvcmd filter=lfs diff=lfs merge=lfs -text
 dependency/openvino/usb-ma2x8x.mvcmd filter=lfs diff=lfs merge=lfs -text
+fl/engine/libliveness_v7.so filter=lfs diff=lfs merge=lfs -text
+fl/examples/att_1.jpg filter=lfs diff=lfs merge=lfs -text
+fl/examples/att_10.jpg filter=lfs diff=lfs merge=lfs -text
+fl/examples/att_2.jpg filter=lfs diff=lfs merge=lfs -text
+fl/examples/att_3.jpg filter=lfs diff=lfs merge=lfs -text
+fl/examples/att_4.jpg filter=lfs diff=lfs merge=lfs -text
+fl/examples/att_6.jpg filter=lfs diff=lfs merge=lfs -text
+fl/examples/att_7.jpg filter=lfs diff=lfs merge=lfs -text
+fl/examples/att_8.jpg filter=lfs diff=lfs merge=lfs -text
+fl/examples/att_9.jpg filter=lfs diff=lfs merge=lfs -text
+fr/engine/librecognition_v6.so filter=lfs diff=lfs merge=lfs -text

fl/engine/bin/data1.bin

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fl/engine/bin/data2.bin

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fl/engine/bin/data3.bin

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+size 11875339

fl/engine/bin/detect.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0b505c320dd8add047f107549849a307d0c6f518f01c1d3402bce9e13a765146
+size 28463173

fl/engine/header.py

@@ -0,0 +1,96 @@
+import os
+import sys
+import numpy as np
+import ctypes, ctypes.util
+from enum import Enum
+from ctypes import *
+from numpy.ctypeslib import ndpointer
+
+def print_log(fmt): print("[LOG] \033[98m{}\033[00m" .format(fmt))
+def print_info(fmt): print("[INFO] \033[92m{}\033[00m" .format(fmt))
+def print_error(fmt): print("[ERR] \033[91m{}\033[00m" .format(fmt)) 
+def print_warning(fmt): print("[WARNING] \033[93m{}\033[00m" .format(fmt))
+
+class ENGINE_CODE(Enum):
+    E_NO_FACE = 0
+    E_ACTIVATION_ERROR = -1
+    E_ENGINE_INIT_ERROR = -2
+    
+class LIVENESS_CODE(Enum):
+    L_TOO_SMALL_FACE = -100
+    L_BORDERLINE_FACE = -200
+    L_TOO_TURNED_FACE = -300
+    L_COVERED_FACE = -400
+    L_MULTIPLE_FACE = -500
+    L_DEEP_FAKE = -600
+
+lib_path = os.path.abspath(os.path.dirname(__file__)) + '/libliveness_v7.so'
+lib = cdll.LoadLibrary(lib_path)
+
+get_version = lib.ttv_version
+get_version.argtypes = []
+get_version.restype = ctypes.c_char_p
+
+get_deviceid = lib.ttv_get_hwid
+get_deviceid.argtypes = []
+get_deviceid.restype = ctypes.c_char_p
+
+init_sdk = lib.ttv_init
+init_sdk.argtypes = [ctypes.c_char_p, ctypes.c_char_p]
+init_sdk.restype = ctypes.c_int32
+
+init_sdk_offline = lib.ttv_init_offline
+init_sdk_offline.argtypes = [ctypes.c_char_p, ctypes.c_char_p]
+init_sdk_offline.restype = ctypes.c_int32
+
+
+detect_face_rgb = lib.ttv_detect_face
+detect_face_rgb.argtypes = [ndpointer(ctypes.c_ubyte, flags='C_CONTIGUOUS'), ctypes.c_int32, ctypes.c_int32, ndpointer(ctypes.c_int32, flags='C_CONTIGUOUS'), ndpointer(ctypes.c_double, flags='C_CONTIGUOUS'), ndpointer(ctypes.c_double, flags='C_CONTIGUOUS')]
+detect_face_rgb.restype = ctypes.c_int32
+
+DEFAULT_THRESHOLD = 0
+def check_liveness(image_mat, spoof_threshold = DEFAULT_THRESHOLD):
+    result = ""
+    score = 0
+
+    if image_mat is None:
+        result = "Failed to open image"
+        return result, None, None, None
+    
+    face_rect = np.zeros([4], dtype=np.int32)
+    liveness_score = np.zeros([1], dtype=np.double)
+    angles = np.zeros([3], dtype=np.double)
+
+    width = image_mat.shape[1]
+    height = image_mat.shape[0]
+
+    ret = detect_face_rgb(image_mat, width, height, face_rect, liveness_score, angles)
+
+    if ret <= 0:
+        if ret == ENGINE_CODE.E_ACTIVATION_ERROR.value:
+            result = "ACTIVATION ERROR"
+        elif ret == ENGINE_CODE.E_ENGINE_INIT_ERROR.value:
+            result = "ENGINE INIT ERROR"
+        elif ret == ENGINE_CODE.E_NO_FACE.value:
+            result = "NO FACE"
+        return result, None, None, None
+    
+    score = liveness_score[0]
+    if score == LIVENESS_CODE.L_TOO_SMALL_FACE.value:
+        result = "TOO SMALL FACE"
+    elif score == LIVENESS_CODE.L_BORDERLINE_FACE.value:
+        result = "FACE CUT OFF"
+    elif score == LIVENESS_CODE.L_TOO_TURNED_FACE.value:
+        result = "TOO TURNED FACE"
+    elif score == LIVENESS_CODE.L_COVERED_FACE.value:
+        result = "COVERED FACE"
+    elif score == LIVENESS_CODE.L_MULTIPLE_FACE.value:
+        result = "MULTIPLE FACES"
+    elif score == LIVENESS_CODE.L_DEEP_FAKE.value:
+        result = "DEEP FAKE DETECTED"
+    elif score > spoof_threshold:
+        result = "REAL"
+    else:
+        result = "SPOOF"
+
+    return result, face_rect, score, angles

fl/engine/libliveness_v7.so

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ff4b8566df6107af6e3564c7c5c4167c213d87e671b286c5d4b799013f0aab9b
+size 5447208

fl/flask/app.py

@@ -0,0 +1,129 @@
+import sys
+sys.path.append('../')
+
+import os
+import base64
+import json
+import cv2
+import numpy as np
+from time import gmtime, strftime
+from flask import Flask, request, jsonify
+
+from engine.header import get_version
+from engine.header import get_deviceid
+from engine.header import init_sdk
+from engine.header import init_sdk_offline
+
+from engine.header import check_liveness
+from engine.header import print_log, print_error, print_info, print_warning
+
+file_path = os.path.abspath(__file__)
+dir_path = os.path.dirname(file_path)
+root_path = os.path.dirname(dir_path)
+
+SPOOF_THRESHOLD = 0.5
+
+app = Flask(__name__) 
+app.config['SITE'] = "http://0.0.0.0:8000/"
+app.config['DEBUG'] = False
+
+version = get_version().decode('utf-8')
+print_info('\t <Recognito Liveness> \t version {}'.format(version))
+
+device_id = get_deviceid().decode('utf-8')
+print_info('\t <Hardware ID> \t\t {}'.format(device_id))
+
+def activate_sdk():
+    online_key = os.environ.get("FL_LICENSE_KEY")
+    offline_key_path = os.path.join(root_path, "license.txt")
+    dict_path = os.path.join(root_path, "engine/bin")
+
+    ret = -1
+    if online_key is None:
+        print_warning("Liveness online license key not found!")
+    else:
+        print_info(f"FL_LICENSE_KEY: {online_key}")
+        ret = init_sdk(dict_path.encode('utf-8'), online_key.encode('utf-8'))
+
+    if ret == 0:
+        print_log("Successfully online init SDK!")
+    else:
+        print_error(f"Failed to online init SDK, Error code {ret}\n Trying offline init SDK...");
+        if os.path.exists(offline_key_path) is False:
+            print_warning("Liveness offline license key file not found!")
+            print_error(f"Falied to offline init SDK, Error code {ret}")
+            return ret
+        else:
+            ret = init_sdk_offline(dict_path.encode('utf-8'), offline_key_path.encode('utf-8'))
+            if ret == 0:
+                print_log("Successfully offline init SDK!")
+            else:
+                print_error(f"Falied to offline init SDK, Error code {ret}")
+                return ret
+    return ret
+
+def generate_response(result, face_rect, score, angles):
+    status = "ok"
+    data = {
+        "status": status, 
+        "data": {}
+    }
+
+    data["data"]["result"] = result    
+
+    if score is not None:
+        data["data"]["liveness_score"] = score
+
+    if face_rect is not None:
+        data["data"]["face_rect"] = {
+            "x": int(face_rect[0]), 
+            "y": int(face_rect[1]), 
+            "w": int(face_rect[2] - face_rect[0] + 1), 
+            "h": int(face_rect[3] - face_rect[1] + 1)
+        }
+
+    if angles is not None:
+        data["data"]["angles"] = {
+            "yaw": angles[0], 
+            "roll": angles[1], 
+            "pitch": angles[2]
+        }
+
+    response = jsonify(data)
+    response.status_code = 200
+    response.headers["Content-Type"] = "application/json; charset=utf-8"
+    return response
+    
+@app.route('/api/check_liveness', methods=['POST'])
+def check_liveness_api():
+    try:
+        file = request.files['image']
+        image_mat = cv2.imdecode(np.frombuffer(file.read(), np.uint8), cv2.IMREAD_COLOR)
+    except:
+        response = generate_response("Failed to open file!", None, None, None)
+        return response
+    
+    result, face_rect, score, angles = check_liveness(image_mat, SPOOF_THRESHOLD)
+    response = generate_response(result, face_rect, score, angles)
+    return response
+
+@app.route('/api/check_liveness_base64', methods=['POST'])
+def check_liveness_base64_api():
+    try: 
+        content = request.get_json()
+        imageBase64 = content['image']
+        image_mat = cv2.imdecode(np.frombuffer(base64.b64decode(imageBase64), dtype=np.uint8), cv2.IMREAD_COLOR)
+    except:
+        response = generate_response("Failed to open file!", None, None, None)
+        return response
+    
+    result, face_rect, score, angles = check_liveness(image_mat, SPOOF_THRESHOLD)
+    response = generate_response(result, face_rect, score, angles)
+    return response
+
+if __name__ == '__main__':
+    ret = activate_sdk()
+    if ret != 0:
+        exit(-1)
+    port = int(os.environ.get("PORT", 8000))
+    app.run(host='0.0.0.0', port=port)

fl/gradio/app.py

@@ -0,0 +1,317 @@
+import sys
+sys.path.append('../')
+
+import os
+import gradio as gr
+import cv2
+import time
+import numpy as np
+from PIL import Image
+
+from engine.header import get_version
+from engine.header import get_deviceid
+from engine.header import init_sdk
+from engine.header import init_sdk_offline
+
+from engine.header import check_liveness
+from engine.header import print_log, print_error, print_info, print_warning
+
+file_path = os.path.abspath(__file__)
+dir_path = os.path.dirname(file_path)
+root_path = os.path.dirname(dir_path)
+
+version = get_version().decode('utf-8')
+print_info('\t <Recognito Liveness> \t version {}'.format(version))
+
+device_id = get_deviceid().decode('utf-8')
+print_info('\t <Hardware ID> \t\t {}'.format(device_id))
+
+g_activation_result = -1
+SPOOF_THRESHOLD = 0.5
+
+css = """
+.example-image img{
+    display: flex; /* Use flexbox to align items */
+    justify-content: center; /* Center the image horizontally */
+    align-items: center; /* Center the image vertically */
+    height: 300px; /* Set the height of the container */
+    object-fit: contain; /* Preserve aspect ratio while fitting the image within the container */
+}
+
+.example-image{
+    display: flex; /* Use flexbox to align items */
+    justify-content: center; /* Center the image horizontally */
+    align-items: center; /* Center the image vertically */
+    height: 350px; /* Set the height of the container */
+    object-fit: contain; /* Preserve aspect ratio while fitting the image within the container */
+}
+
+.face-row {
+    display: flex;
+    justify-content: space-around; /* Distribute space evenly between elements */
+    align-items: center; /* Align items vertically */
+    width: 100%; /* Set the width of the row to 100% */
+}
+
+.face-image{
+    justify-content: center; /* Center the image horizontally */
+    align-items: center; /* Center the image vertically */
+    height: 160px; /* Set the height of the container */
+    width: 160px;
+    object-fit: contain; /* Preserve aspect ratio while fitting the image within the container */
+}
+
+.face-image img{
+    justify-content: center; /* Center the image horizontally */
+    align-items: center; /* Center the image vertically */
+    height: 160px; /* Set the height of the container */
+    object-fit: contain; /* Preserve aspect ratio while fitting the image within the container */
+}
+
+.markdown-success-container {
+    background-color: #F6FFED;
+    padding: 20px;
+    margin: 20px;
+    border-radius: 1px;
+    border: 2px solid green;
+    text-align: center;
+}
+
+.markdown-fail-container {
+    background-color: #FFF1F0;
+    padding: 20px;
+    margin: 20px;
+    border-radius: 1px;
+    border: 2px solid red;
+    text-align: center;
+}
+
+.markdown-attribute-container {
+    display: flex;
+    justify-content: space-around; /* Distribute space evenly between elements */
+    align-items: center; /* Align items vertically */
+    padding: 10px;
+    margin: 10px;
+}
+
+.block-background {
+    # background-color: #202020; /* Set your desired background color */
+    border-radius: 5px;
+}
+
+table, th, td {
+    text-align: center;    
+}
+"""
+
+
+def activate_sdk():
+    online_key = os.environ.get("FL_LICENSE_KEY")
+    offline_key_path = os.path.join(root_path, "license.txt")
+    dict_path = os.path.join(root_path, "engine/bin")
+
+    ret = -1
+    if online_key is None:
+        print_warning("Liveness online license key not found!")
+    else:
+        print_info(f"FL_LICENSE_KEY: {online_key}")
+        ret = init_sdk(dict_path.encode('utf-8'), online_key.encode('utf-8'))
+
+    if ret == 0:
+        print_log("Successfully online init SDK!")
+    else:
+        print_error(f"Failed to online init SDK, Error code {ret}\n Trying offline init SDK...");
+        if os.path.exists(offline_key_path) is False:
+            print_warning("Liveness offline license key file not found!")
+            print_error(f"Falied to offline init SDK, Error code {ret}")
+            return ret
+        else:
+            ret = init_sdk_offline(dict_path.encode('utf-8'), offline_key_path.encode('utf-8'))
+            if ret == 0:
+                print_log("Successfully offline init SDK!")
+            else:
+                print_error(f"Falied to offline init SDK, Error code {ret}")
+                return ret
+    return ret
+
+def convert_fun(input_str):
+    # Remove line breaks and extra whitespaces
+    return ' '.join(input_str.split())
+
+def check_liveness_clicked(frame, threshold):   
+    global g_activation_result
+    if g_activation_result != 0:
+        gr.Warning("SDK Activation Failed!")
+        return None, None, None, None
+
+    try:
+        image = open(frame, 'rb')
+    except:
+        raise gr.Error("Please select image file!")
+    
+    image_mat = cv2.imdecode(np.frombuffer(image.read(), np.uint8), cv2.IMREAD_COLOR)
+    start_time = time.time()
+    result, face_rect, score, angles = check_liveness(image_mat, float(threshold))
+    end_time = time.time()
+    process_time = (end_time - start_time) * 1000
+
+    face_crop, one_line_attribute = None, ""
+    try:
+        image = Image.open(frame)
+
+        face = Image.new('RGBA',(150, 150), (80,80,80,0))
+        
+        if face_rect is not None:
+            x1 = int(face_rect[0])
+            y1 = int(face_rect[1])
+            x2 = int(face_rect[2])
+            y2 = int(face_rect[3])
+
+            if x1 < 0:
+                x1 = 0
+            if y1 < 0:
+                y1 = 0
+            if x2 >= image.width:
+                x2 = image.width - 1
+            if y2 >= image.height:
+                y2 = image.height - 1
+
+            face_crop = image.crop((x1, y1, x2, y2))
+            face_image_ratio = face_crop.width / float(face_crop.height)
+            resized_w = int(face_image_ratio * 150)
+            resized_h = 150
+
+            face_crop = face_crop.resize((int(resized_w), int(resized_h)))
+
+        if angles is not None:
+            yaw = angles[0]
+            roll = angles[1]
+            pitch = angles[2]
+
+        attribute = f"""
+        <br/>
+        <div class="markdown-attribute-container">
+        <table>
+        <tr>
+            <th>Field</th>
+            <th colspan="2">Value</th>
+        </tr>
+        <tr>
+            <th rowspan="4">Face Rect</th>
+            <td>x</td>
+            <td>{x1}</td>
+        </tr>
+        <tr>
+            <td>y</td>
+            <td>{y1}</td>
+        </tr>
+        <tr>
+            <td>width</td>
+            <td>{x2 - x1 + 1}</td>
+        </tr>
+        <tr>
+            <td>height</td>
+            <td>{y2 - y1 + 1}</td>
+        </tr>
+        <tr>
+            <th rowspan="3">Face Angle</th>
+            <td>Pitch</td>
+            <td>{"{:.4f}".format(pitch)}</td>
+        </tr>
+        <tr>
+            <td>Yaw</td>
+            <td>{"{:.4f}".format(yaw)}</td>
+        </tr>
+        <tr>
+            <td>Roll</td>
+            <td>{"{:.4f}".format(roll)}</td>
+        </tr>
+        </table>
+        </div>
+        """
+        
+        one_line_attribute = convert_fun(attribute)
+    except:
+        pass
+
+    str_score = str("{:.4f}".format(score))
+    if result == "REAL":
+        liveness_result = f"""<br/><div class="markdown-success-container"><p style="text-align: center; font-size: 20px; color: green;">Liveness Check:  REAL<br/>Score: {str_score}</p></div>"""        
+    else:
+        liveness_result = f"""<br/><div class="markdown-fail-container"><p style="text-align: center; font-size: 20px; color: red;">Liveness Check:  {result}<br/>Score: {str_score}</p></div>"""  
+        
+    return face_crop, liveness_result, one_line_attribute, process_time
+
+def launch_demo(activate_result):
+    with gr.Blocks(css=css) as demo:
+        gr.Markdown(
+            f"""
+            <a href="https://recognito.vision" style="display: flex; align-items: center;">
+                <img src="https://recognito.vision/wp-content/uploads/2024/03/Recognito-modified.png" style="width: 3%; margin-right: 15px;"/>
+            </a>
+            <div style="display: flex; align-items: center;justify-content: center;">
+                <p style="font-size: 36px; font-weight: bold;">Face Liveness Detection {version}</p>
+            </div>
+            <p style="font-size: 20px; font-weight: bold;">🤝 Contact us for our on-premise Face Recognition, Liveness Detection SDKs deployment</p>
+            </div>
+            <div style="display: flex; align-items: center;">
+                &emsp;&emsp;<a target="_blank" href="mailto:[email protected]"><img src="https://img.shields.io/badge/[email protected]?logo=gmail " alt="www.recognito.vision"></a>
+                &nbsp;&nbsp;&nbsp;&nbsp;<a target="_blank" href="https://wa.me/+14158003112"><img src="https://img.shields.io/badge/whatsapp-recognito-blue.svg?logo=whatsapp " alt="www.recognito.vision"></a>
+                &nbsp;&nbsp;&nbsp;&nbsp;<a target="_blank" href="https://t.me/recognito_vision"><img src="https://img.shields.io/badge/[email protected]?logo=telegram " alt="www.recognito.vision"></a>
+                &nbsp;&nbsp;&nbsp;&nbsp;<a target="_blank" href="https://join.slack.com/t/recognito-workspace/shared_invite/zt-2d4kscqgn-"><img src="https://img.shields.io/badge/slack-recognito-blue.svg?logo=slack " alt="www.recognito.vision"></a>
+            </div>
+            <br/>
+            <div style="display: flex; align-items: center;">
+                &emsp;&emsp;<a href="https://recognito.vision" style="display: flex; align-items: center;"><img src="https://recognito.vision/wp-content/uploads/2024/03/recognito_64.png" style="width: 24px; margin-right: 5px;"/></a>
+                &nbsp;&nbsp;&nbsp;&nbsp;<a href="https://www.linkedin.com/company/recognito-vision" style="display: flex; align-items: center;"><img src="https://recognito.vision/wp-content/uploads/2024/03/linkedin64.png" style="width: 24px; margin-right: 5px;"/></a>
+                &nbsp;&nbsp;&nbsp;&nbsp;<a href="https://huggingface.co/Recognito" style="display: flex; align-items: center;"><img src="https://recognito.vision/wp-content/uploads/2024/03/hf1_64.png" style="width: 24px; margin-right: 5px;"/></a>
+                &nbsp;&nbsp;&nbsp;&nbsp;<a href="https://github.com/Recognito-Vision" style="display: flex; align-items: center;"><img src="https://recognito.vision/wp-content/uploads/2024/03/github64.png" style="width: 24px; margin-right: 5px;"/></a>
+                &nbsp;&nbsp;&nbsp;&nbsp;<a href="https://hub.docker.com/u/recognito" style="display: flex; align-items: center;"><img src="https://recognito.vision/wp-content/uploads/2024/03/docker64.png" style="width: 24px; margin-right: 5px;"/></a>
+            </div>
+            <br/>
+            """
+        )
+    
+                
+        with gr.Group():
+            if activate_result == 0:
+                gr.Markdown("""<p style="text-align: left; font-size: 20px; color: green;">&emsp;Activation Success!</p>""")         
+            else:
+                gr.Markdown("""<p style="text-align: left; font-size: 20px; color: red;">&emsp;Activation Failed!</p>""") 
+                    
+            gr.Textbox(device_id, label="Hardware ID")
+            
+
+        with gr.Row():
+            with gr.Column(scale=1):
+                face_input = gr.Image(label="Image", type='filepath', elem_classes="example-image")
+                gr.Examples([os.path.join(root_path,'examples/att_1.jpg'), 
+                                    os.path.join(root_path,'examples/att_2.jpg'), 
+                                    os.path.join(root_path,'examples/att_3.jpg'), 
+                                    os.path.join(root_path,'examples/att_4.jpg'), 
+                                    os.path.join(root_path,'examples/att_5.jpg'), 
+                                    os.path.join(root_path,'examples/att_6.jpg'), 
+                                    os.path.join(root_path,'examples/att_7.jpg'), 
+                                    os.path.join(root_path,'examples/att_8.jpg'), 
+                                    os.path.join(root_path,'examples/att_9.jpg'), 
+                                    os.path.join(root_path,'examples/att_10.jpg')], 
+                                    inputs=face_input)
+
+            with gr.Blocks():
+                with gr.Column(scale=1, elem_classes="block-background"):     
+                    txt_threshold = gr.Textbox(f"{SPOOF_THRESHOLD}", label="Spoof Threshold", interactive=True)
+                    check_liveness_button = gr.Button("Check Liveness", variant="primary", size="lg")
+                    with gr.Row(elem_classes="face-row"):
+                        face_output = gr.Image(value=os.path.join(dir_path,'icons/face.jpg'), label="Face", scale=0, elem_classes="face-image")
+                    
+                    liveness_result = gr.Markdown("")
+                    txt_speed = gr.Textbox(f"", label="Processing Time (ms)", interactive=False, visible=False)
+                    attribute_result = gr.Markdown("")
+                
+                check_liveness_button.click(check_liveness_clicked, inputs=[face_input, txt_threshold], outputs=[face_output, liveness_result, attribute_result, txt_speed])
+
+    demo.launch(server_name="0.0.0.0", server_port=7860, show_api=False)
+    
+if __name__ == '__main__':
+    g_activation_result = activate_sdk()
+    launch_demo(g_activation_result)

fr/engine/bin/data1.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:36cf5fcc49345989a86839a53529314ec1fe5d621c377a1952bc7538d55e7f1b
+size 16255630

fr/engine/bin/data2.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f25fb0cd3d70cb84c258e7109620f411c087e0875828d6ab86cc9c4838d49bec
+size 11875339

fr/engine/bin/data31.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d791d9e975be933db24fa029ab5d0d752b85fdbda8a540c5d1baf385acf23aa9
+size 130663307

fr/engine/bin/detect.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0b505c320dd8add047f107549849a307d0c6f518f01c1d3402bce9e13a765146
+size 28463173

fr/engine/header.py

@@ -0,0 +1,96 @@
+import os
+import sys
+import numpy as np
+import ctypes, ctypes.util
+from enum import Enum
+from ctypes import *
+from numpy.ctypeslib import ndpointer
+
+def print_log(fmt): print("[LOG] \033[98m{}\033[00m" .format(fmt))
+def print_info(fmt): print("[INFO] \033[92m{}\033[00m" .format(fmt))
+def print_error(fmt): print("[ERR] \033[91m{}\033[00m" .format(fmt)) 
+def print_warning(fmt): print("[WARNING] \033[93m{}\033[00m" .format(fmt))
+
+class ENGINE_CODE(Enum):
+    E_NO_FACE = 0
+    E_ACTIVATION_ERROR = -1
+    E_ENGINE_INIT_ERROR = -2    
+
+lib_path = os.path.abspath(os.path.dirname(__file__)) + '/librecognition_v6.so'
+lib = cdll.LoadLibrary(lib_path)
+
+get_version = lib.ttv_version
+get_version.argtypes = []
+get_version.restype = ctypes.c_char_p
+
+get_deviceid = lib.ttv_get_hwid
+get_deviceid.argtypes = []
+get_deviceid.restype = ctypes.c_char_p
+
+init_sdk = lib.ttv_init
+init_sdk.argtypes = [ctypes.c_char_p, ctypes.c_char_p]
+init_sdk.restype = ctypes.c_int32
+
+init_sdk_offline = lib.ttv_init_offline
+init_sdk_offline.argtypes = [ctypes.c_char_p, ctypes.c_char_p]
+init_sdk_offline.restype = ctypes.c_int32
+
+extract_template = lib.ttv_extract_feature
+extract_template.argtypes = [ndpointer(ctypes.c_ubyte, flags='C_CONTIGUOUS'), ctypes.c_int32, ctypes.c_int32, ndpointer(ctypes.c_int32, flags='C_CONTIGUOUS'), ndpointer(ctypes.c_ubyte, flags='C_CONTIGUOUS'), ndpointer(ctypes.c_int32, flags='C_CONTIGUOUS')]
+extract_template.restype = ctypes.c_int
+
+calculate_similarity = lib.ttv_compare_feature
+calculate_similarity.argtypes = [ndpointer(ctypes.c_ubyte, flags='C_CONTIGUOUS'), ndpointer(ctypes.c_ubyte, flags='C_CONTIGUOUS')]
+calculate_similarity.restype = ctypes.c_double
+
+DEFAULT_THRESHOLD = 0.67
+def compare_face(image_mat1, image_mat2, match_threshold=DEFAULT_THRESHOLD):
+    result = ""
+    if image_mat1 is None:
+        result = "Failed to open image1"
+        return result, None, None, None
+
+    if image_mat2 is None:
+        result = "Failed to open image2"
+        return result, None, None, None
+    
+    face_bbox_1 = np.zeros([4], dtype=np.int32)
+    template_1 = np.zeros([2048], dtype=np.uint8)
+    template_len_1 = np.zeros([1], dtype=np.int32)
+    width_1 = image_mat1.shape[1]
+    height_1 = image_mat1.shape[0]
+
+    ret = extract_template(image_mat1, width_1, height_1, face_bbox_1, template_1, template_len_1)
+    if ret <= 0:
+        if ret == ENGINE_CODE.E_ACTIVATION_ERROR.value:
+            result = "ACTIVATION ERROR"
+        elif ret == ENGINE_CODE.E_ENGINE_INIT_ERROR.value:
+            result = "ENGINE INIT ERROR"
+        elif ret == ENGINE_CODE.E_NO_FACE.value:
+            result = "NO FACE in image1"
+        return result, None, None, None
+    
+
+    face_bbox_2 = np.zeros([4], dtype=np.int32)
+    template_2 = np.zeros([2048], dtype=np.uint8)
+    template_len_2 = np.zeros([1], dtype=np.int32)
+    width_2 = image_mat2.shape[1]
+    height_2 = image_mat2.shape[0]
+
+    ret = extract_template(image_mat2, width_2, height_2, face_bbox_2, template_2, template_len_2)
+    if ret <= 0:
+        if ret == ENGINE_CODE.E_ACTIVATION_ERROR.value:
+            result = "ACTIVATION ERROR"
+        elif ret == ENGINE_CODE.E_ENGINE_INIT_ERROR.value:
+            result = "ENGINE INIT ERROR"
+        elif ret == ENGINE_CODE.E_NO_FACE.value:
+            result = "NO FACE in image2"
+        return result, None, None, None
+
+    match_score = calculate_similarity(template_1, template_2)
+    if match_score > match_threshold:
+        result = "SAME PERSON"
+    else:
+        result = "DIFFERENT PERSON"
+
+    return result, match_score, [face_bbox_1, face_bbox_2], [template_1, template_2]

fr/engine/librecognition_v6.so

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3305d2f3b42dccfd83f6ce80b26b737e40cefc676261ad0b849fe4c9946aae3e
+size 5067600

fr/flask/app.py

@@ -0,0 +1,142 @@
+import sys
+sys.path.append('../')
+
+import os
+import base64
+import json
+import cv2
+import numpy as np
+from flask import Flask, request, jsonify
+from time import gmtime, strftime
+
+from engine.header import *
+
+file_path = os.path.abspath(__file__)
+dir_path = os.path.dirname(file_path)
+root_path = os.path.dirname(dir_path)
+
+MATCH_THRESHOLD = 0.67
+
+app = Flask(__name__) 
+app.config['SITE'] = "http://0.0.0.0:8000/"
+app.config['DEBUG'] = False
+
+version = get_version().decode('utf-8')
+print_info('\t <Recognito Face Recognition> \t version {}'.format(version))
+
+device_id = get_deviceid().decode('utf-8')
+print_info('\t <Hardware ID> \t\t {}'.format(device_id))
+
+def activate_sdk():
+    online_key = os.environ.get("FR_LICENSE_KEY")
+    offline_key_path = os.path.join(root_path, "license.txt")
+    dict_path = os.path.join(root_path, "engine/bin")
+
+    ret = -1
+    if online_key is None:
+        print_warning("Recognition online license key not found!")
+    else:
+        print_info(f"FR_LICENSE_KEY: {online_key}")
+        ret = init_sdk(dict_path.encode('utf-8'), online_key.encode('utf-8'))
+
+    if ret == 0:
+        print_log("Successfully online init SDK!")
+    else:
+        print_error(f"Failed to online init SDK, Error code {ret}\n Trying offline init SDK...");
+        if os.path.exists(offline_key_path) is False:
+            print_warning("Recognition offline license key file not found!")
+            print_error(f"Falied to offline init SDK, Error code {ret}")
+            return ret
+        else:
+            ret = init_sdk_offline(dict_path.encode('utf-8'), offline_key_path.encode('utf-8'))
+            if ret == 0:
+                print_log("Successfully offline init SDK!")
+            else:
+                print_error(f"Falied to offline init SDK, Error code {ret}")
+                return ret
+                
+    return ret
+
+def generate_response(result, similarity=None, face_bboxes=None, face_features=None):
+    status = "ok"
+    data = {
+        "status": status, 
+        "data": {}
+    }
+    
+    data["data"]["result"] = result    
+    if similarity is not None:
+        data["data"]["similarity"] = float(similarity)    
+    images = [{}, {}]
+    if face_bboxes is not None:
+        for i, bbox in enumerate(face_bboxes):
+            box = {
+                "x" : int(bbox[0]),
+                "y" : int(bbox[1]),
+                "width" : int(bbox[2] - bbox[0] + 1),
+                "height" : int(bbox[3] - bbox[1] + 1)
+            }
+            images[i]["detection"] = box
+        
+    if face_features is not None:
+        for i, feat in enumerate(face_features):
+            json_string = json.dumps(feat.tolist(), indent=0).replace('\n','')
+            images[i]["feature"] = json_string
+            
+    data["data"]["image1"] = images[0]
+    data["data"]["image2"] = images[1]
+
+    response = jsonify(data)
+    response.status_code = 200
+    response.headers["Content-Type"] = "application/json; charset=utf-8"
+    return response
+    
+@app.route('/api/compare_face', methods=['POST'])
+def compare_face_api():
+    try:
+        file1 = request.files['image1']
+        image_mat1 = cv2.imdecode(np.frombuffer(file1.read(), np.uint8), cv2.IMREAD_COLOR)
+    except:
+        response = generate_response("Failed to open image1")
+        return response
+
+    try:
+        file2 = request.files['image2']
+        image_mat2 = cv2.imdecode(np.frombuffer(file2.read(), np.uint8), cv2.IMREAD_COLOR)
+    except:
+        response = generate_response("Failed to open image2")
+        return response        
+
+    result, score, face_bboxes, face_features = compare_face(image_mat1, image_mat2, MATCH_THRESHOLD)
+    response = generate_response(result, score, face_bboxes, face_features)
+    return response
+
+
+@app.route('/api/compare_face_base64', methods=['POST'])
+def coompare_face_base64_api():
+    content = request.get_json()
+    
+    try:
+        image_base64_1 = content['image1']
+        image_mat1 = cv2.imdecode(np.frombuffer(base64.b64decode(image_base64_1), dtype=np.uint8), cv2.IMREAD_COLOR)
+    except:
+        response = generate_response("Failed to open image1")
+        return response
+
+    try:
+        image_base64_2 = content['image2']
+        image_mat2 = cv2.imdecode(np.frombuffer(base64.b64decode(image_base64_2), dtype=np.uint8), cv2.IMREAD_COLOR)
+    except:
+        response = generate_response("Failed to open image2")
+        return response
+
+    result, score, face_bboxes, face_features = compare_face(image_mat1, image_mat2, MATCH_THRESHOLD)
+    response = generate_response(result, score, face_bboxes, face_features)
+    return response
+
+if __name__ == '__main__':
+    ret = activate_sdk()
+    if ret != 0:
+        exit(-1)
+    port = int(os.environ.get("PORT", 8000))
+    app.run(host='0.0.0.0', port=port)

fr/gradio/app.py

@@ -0,0 +1,271 @@
+import sys
+sys.path.append('../')
+
+import os
+import gradio as gr
+import cv2
+import time
+import numpy as np
+from PIL import Image
+
+from engine.header import *
+
+file_path = os.path.abspath(__file__)
+gradio_path = os.path.dirname(file_path)
+root_path = os.path.dirname(gradio_path)
+
+version = get_version().decode('utf-8')
+print_info('\t <Recognito Face Recognition> \t version {}'.format(version))
+
+device_id = get_deviceid().decode('utf-8')
+print_info('\t <Hardware ID> \t\t {}'.format(device_id))
+
+g_activation_result = -1
+MATCH_THRESHOLD = 0.67
+
+css = """
+.example-image img{
+    display: flex; /* Use flexbox to align items */
+    justify-content: center; /* Center the image horizontally */
+    align-items: center; /* Center the image vertically */
+    height: 300px; /* Set the height of the container */
+    object-fit: contain; /* Preserve aspect ratio while fitting the image within the container */
+}
+
+.example-image{
+    display: flex; /* Use flexbox to align items */
+    justify-content: center; /* Center the image horizontally */
+    align-items: center; /* Center the image vertically */
+    height: 350px; /* Set the height of the container */
+    object-fit: contain; /* Preserve aspect ratio while fitting the image within the container */
+}
+
+.face-row {
+    display: flex;
+    justify-content: space-around; /* Distribute space evenly between elements */
+    align-items: center; /* Align items vertically */
+    width: 100%; /* Set the width of the row to 100% */
+}
+
+.face-image{
+    justify-content: center; /* Center the image horizontally */
+    align-items: center; /* Center the image vertically */
+    height: 160px; /* Set the height of the container */
+    width: 160px;
+    object-fit: contain; /* Preserve aspect ratio while fitting the image within the container */
+}
+
+.face-image img{
+    justify-content: center; /* Center the image horizontally */
+    align-items: center; /* Center the image vertically */
+    height: 160px; /* Set the height of the container */
+    object-fit: contain; /* Preserve aspect ratio while fitting the image within the container */
+}
+
+.markdown-success-container {
+    background-color: #F6FFED;
+    padding: 20px;
+    margin: 20px;
+    border-radius: 1px;
+    border: 2px solid green;
+    text-align: center;
+}
+
+.markdown-fail-container {
+    background-color: #FFF1F0;
+    padding: 20px;
+    margin: 20px;
+    border-radius: 1px;
+    border: 2px solid red;
+    text-align: center;
+}
+
+.block-background {
+    # background-color: #202020; /* Set your desired background color */
+    border-radius: 5px;
+}
+
+"""
+
+def activate_sdk():
+    online_key = os.environ.get("FR_LICENSE_KEY")
+    offline_key_path = os.path.join(root_path, "license.txt")
+    dict_path = os.path.join(root_path, "engine/bin")
+
+    ret = -1
+    if online_key is None:
+        print_warning("Recognition online license key not found!")
+    else:
+        print_info(f"FR_LICENSE_KEY: {online_key}")
+        ret = init_sdk(dict_path.encode('utf-8'), online_key.encode('utf-8'))
+
+    if ret == 0:
+        print_log("Successfully online init SDK!")
+    else:
+        print_error(f"Failed to online init SDK, Error code {ret}\n Trying offline init SDK...");
+        if os.path.exists(offline_key_path) is False:
+            print_warning("Recognition offline license key file not found!")
+            print_error(f"Falied to offline init SDK, Error code {ret}")
+            return ret
+        else:
+            ret = init_sdk_offline(dict_path.encode('utf-8'), offline_key_path.encode('utf-8'))
+            if ret == 0:
+                print_log("Successfully offline init SDK!")
+            else:
+                print_error(f"Falied to offline init SDK, Error code {ret}")
+                return ret
+                
+    return ret
+
+def compare_face_clicked(frame1, frame2, threshold):
+    global g_activation_result
+    if g_activation_result != 0:
+        gr.Warning("SDK Activation Failed!")
+        return None, None, None, None, None, None, None, None, None
+
+    try:
+        image1 = open(frame1, 'rb')
+        image2 = open(frame2, 'rb')
+    except:
+        raise gr.Error("Please select images files!")
+
+    image_mat1 = cv2.imdecode(np.frombuffer(image1.read(), np.uint8), cv2.IMREAD_COLOR)
+    image_mat2 = cv2.imdecode(np.frombuffer(image2.read(), np.uint8), cv2.IMREAD_COLOR)
+    start_time = time.time()
+    result, score, face_bboxes, face_features = compare_face(image_mat1, image_mat2, float(threshold))
+    end_time = time.time()
+    process_time = (end_time - start_time) * 1000
+
+    try:
+        image1 = Image.open(frame1)
+        image2 = Image.open(frame2)
+        images = [image1, image2]
+
+        face1 = Image.new('RGBA',(150, 150), (80,80,80,0))
+        face2 = Image.new('RGBA',(150, 150), (80,80,80,0))
+        faces = [face1, face2]
+
+        face_bboxes_result = []
+        if face_bboxes is not None:
+            for i, bbox in enumerate(face_bboxes):
+                x1 = bbox[0]
+                y1 = bbox[1]
+                x2 = bbox[2]
+                y2 = bbox[3]
+                if x1 < 0:
+                    x1 = 0
+                if y1 < 0:
+                    y1 = 0
+                if x2 >= images[i].width:
+                    x2 = images[i].width - 1
+                if y2 >= images[i].height:
+                    y2 = images[i].height - 1
+
+                face_bbox_str = f"x1: {x1}, y1: {y1}, x2: {x2}, y2: {y2}"
+                face_bboxes_result.append(face_bbox_str)
+
+                faces[i] = images[i].crop((x1, y1, x2, y2))
+                face_image_ratio = faces[i].width / float(faces[i].height)
+                resized_w = int(face_image_ratio * 150)
+                resized_h = 150
+
+                faces[i] = faces[i].resize((int(resized_w), int(resized_h)))
+    except:
+        pass
+        
+    matching_result = Image.open(os.path.join(gradio_path, "icons/blank.png"))
+    similarity_score = ""
+    if faces[0] is not None and faces[1] is not None:
+        if score is not None:
+            str_score = str("{:.4f}".format(score))
+            if result == "SAME PERSON":
+                matching_result = Image.open(os.path.join(gradio_path, "icons/same.png"))
+                similarity_score = f"""<br/><div class="markdown-success-container"><p style="text-align: center; font-size: 20px; color: green;">Similarity score: {str_score}</p></div>"""
+            else:
+                matching_result = Image.open(os.path.join(gradio_path, "icons/different.png"))
+                similarity_score = f"""<br/><div class="markdown-fail-container"><p style="text-align: center; font-size: 20px; color: red;">Similarity score: {str_score}</p></div>"""
+    
+    return faces[0], faces[1], matching_result, similarity_score, face_bboxes_result[0], face_bboxes_result[1], face_features[0], face_features[1], str(process_time)
+
+def launch_demo(activate_result):
+    with gr.Blocks(css=css) as demo:
+        gr.Markdown(
+            f"""
+            <a href="https://recognito.vision" style="display: flex; align-items: center;">
+                <img src="https://recognito.vision/wp-content/uploads/2024/03/Recognito-modified.png" style="width: 3%; margin-right: 15px;"/>
+            </a>
+            <div style="display: flex; align-items: center;justify-content: center;">
+                <p style="font-size: 36px; font-weight: bold;">Face Recognition {version}</p>
+            </div>
+            <p style="font-size: 20px; font-weight: bold;">🤝 Contact us for our on-premise Face Recognition, Liveness Detection SDKs deployment</p>
+            </div>
+            <div style="display: flex; align-items: center;">
+                &emsp;&emsp;<a target="_blank" href="mailto:[email protected]"><img src="https://img.shields.io/badge/[email protected]?logo=gmail " alt="www.recognito.vision"></a>
+                &nbsp;&nbsp;&nbsp;&nbsp;<a target="_blank" href="https://wa.me/+14158003112"><img src="https://img.shields.io/badge/whatsapp-recognito-blue.svg?logo=whatsapp " alt="www.recognito.vision"></a>
+                &nbsp;&nbsp;&nbsp;&nbsp;<a target="_blank" href="https://t.me/recognito_vision"><img src="https://img.shields.io/badge/[email protected]?logo=telegram " alt="www.recognito.vision"></a>
+                &nbsp;&nbsp;&nbsp;&nbsp;<a target="_blank" href="https://join.slack.com/t/recognito-workspace/shared_invite/zt-2d4kscqgn-"><img src="https://img.shields.io/badge/slack-recognito-blue.svg?logo=slack " alt="www.recognito.vision"></a>
+            </div>
+            <br/>
+            <div style="display: flex; align-items: center;">
+                &emsp;&emsp;<a href="https://recognito.vision" style="display: flex; align-items: center;"><img src="https://recognito.vision/wp-content/uploads/2024/03/recognito_64.png" style="width: 24px; margin-right: 5px;"/></a>
+                &nbsp;&nbsp;&nbsp;&nbsp;<a href="https://www.linkedin.com/company/recognito-vision" style="display: flex; align-items: center;"><img src="https://recognito.vision/wp-content/uploads/2024/03/linkedin64.png" style="width: 24px; margin-right: 5px;"/></a>
+                &nbsp;&nbsp;&nbsp;&nbsp;<a href="https://huggingface.co/Recognito" style="display: flex; align-items: center;"><img src="https://recognito.vision/wp-content/uploads/2024/03/hf1_64.png" style="width: 24px; margin-right: 5px;"/></a>
+                &nbsp;&nbsp;&nbsp;&nbsp;<a href="https://github.com/Recognito-Vision" style="display: flex; align-items: center;"><img src="https://recognito.vision/wp-content/uploads/2024/03/github64.png" style="width: 24px; margin-right: 5px;"/></a>
+                &nbsp;&nbsp;&nbsp;&nbsp;<a href="https://hub.docker.com/u/recognito" style="display: flex; align-items: center;"><img src="https://recognito.vision/wp-content/uploads/2024/03/docker64.png" style="width: 24px; margin-right: 5px;"/></a>
+            </div>
+            <br/>
+            """
+        )
+        
+        with gr.Group():
+            if activate_result == 0:
+                gr.Markdown("""<p style="text-align: left; font-size: 20px; color: green;">&emsp;Activation Success!</p>""")         
+            else:
+                gr.Markdown("""<p style="text-align: left; font-size: 20px; color: red;">&emsp;Activation Failed!</p>""") 
+                    
+            gr.Textbox(device_id, label="Hardware ID")
+
+        with gr.Row():
+            with gr.Column(scale=2):
+                with gr.Row():
+                    with gr.Column(scale=1):
+                        compare_face_input1 = gr.Image(label="Image1", type='filepath', elem_classes="example-image")
+                        gr.Examples([os.path.join(root_path,'examples/1.jpg'), 
+                                    os.path.join(root_path,'examples/2.jpg'), 
+                                    os.path.join(root_path,'examples/3.jpg'), 
+                                    os.path.join(root_path,'examples/4.jpg')], 
+                                    inputs=compare_face_input1)
+                    with gr.Column(scale=1):
+                        compare_face_input2 = gr.Image(label="Image2", type='filepath', elem_classes="example-image")
+                        gr.Examples([os.path.join(root_path,'examples/5.jpg'),
+                                    os.path.join(root_path,'examples/6.jpg'),
+                                    os.path.join(root_path,'examples/7.jpg'),
+                                    os.path.join(root_path,'examples/8.jpg')],
+                                    inputs=compare_face_input2)
+                        
+            with gr.Blocks():
+                with gr.Column(scale=1, min_width=400, elem_classes="block-background"):   
+                    txt_threshold = gr.Textbox(f"{MATCH_THRESHOLD}", label="Matching Threshold", interactive=True)
+                    compare_face_button = gr.Button("Compare Face", variant="primary", size="lg")
+                    with gr.Row(elem_classes="face-row"):
+                        face_output1 = gr.Image(value=os.path.join(gradio_path,'icons/face.jpg'), label="Face 1", scale=0, elem_classes="face-image")
+                        compare_result = gr.Image(value=os.path.join(gradio_path,'icons/blank.png'), min_width=30, scale=0, show_download_button=False, show_label=False)
+                        face_output2 = gr.Image(value=os.path.join(gradio_path,'icons/face.jpg'), label="Face 2", scale=0, elem_classes="face-image")
+                    similarity_markdown = gr.Markdown("")
+                    txt_speed = gr.Textbox(f"", label="Processing Time (ms)", interactive=False, visible=False)
+                    with gr.Group():
+                        gr.Markdown("""&nbsp;face1""")
+                        txt_bbox1 = gr.Textbox(f"", label="Rect", interactive=False)
+                        txt_feature1 = gr.Textbox(f"", label="Feature", interactive=False, max_lines=5)
+                    with gr.Group():
+                        gr.Markdown("""&nbsp;face2""")
+                        txt_bbox2 = gr.Textbox(f"", label="Rect", interactive=False)
+                        txt_feature2 = gr.Textbox(f"", label="Feature", interactive=False, max_lines=5)
+
+                    compare_face_button.click(compare_face_clicked, inputs=[compare_face_input1, compare_face_input2, txt_threshold], outputs=[face_output1, face_output2, compare_result, similarity_markdown, txt_bbox1, txt_bbox2, txt_feature1, txt_feature2, txt_speed])
+            
+    demo.launch(server_name="0.0.0.0", server_port=7860, show_api=False)
+
+if __name__ == '__main__':
+    g_activation_result = activate_sdk()
+    launch_demo(g_activation_result)