Spaces:
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Running
Upload 11 files
Browse files- .gitattributes +1 -0
- Dockerfile +45 -0
- app.py +214 -0
- libopencv.zip +3 -0
- libvein.so +3 -0
- license.txt +5 -0
- model/vein1v0.bin +3 -0
- ncnn.zip +3 -0
- requirements.txt +6 -0
- roi.py +128 -0
- run.sh +3 -0
- veinsdk.py +26 -0
.gitattributes
CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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libvein.so filter=lfs diff=lfs merge=lfs -text
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Dockerfile
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FROM openvino/ubuntu20_runtime:2024.5.0
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USER root
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RUN rm -rf /var/lib/apt/lists/* && apt update && apt install -y unzip \
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libjpeg8 \
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libwebp6 \
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libpng16-16 \
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libtbb2 \
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libtiff5 \
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libtbb-dev \
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libopenexr-dev \
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libgl1-mesa-glx \
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libglib2.0-0 \
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libgomp1
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# Set up working directory
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RUN mkdir -p /home/openvino/kby-ai-palmvein
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WORKDIR /home/openvino/kby-ai-palmvein
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# Copy shared libraries and application files
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COPY ./libopencv.zip .
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RUN unzip libopencv.zip
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RUN cp -f libopencv/* /usr/local/lib/
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RUN ldconfig
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# Copy Python and application files
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COPY ./model ./model
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COPY ./libvein.so .
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COPY ./app.py .
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COPY ./roi.py .
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COPY ./veinsdk.py .
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COPY ./requirements.txt .
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COPY ./run.sh .
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COPY ./license.txt .
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COPY ./ncnn.zip .
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RUN unzip ncnn.zip
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# Install Python dependencies
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RUN pip3 install --no-cache-dir -r requirements.txt
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# Set up entrypoint
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CMD ["bash", "./run.sh"]
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# Expose ports
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EXPOSE 8080 9000
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app.py
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import sys
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sys.path.append('.')
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import os
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import base64
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import json
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from ctypes import *
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import cv2
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import numpy as np
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from flask import Flask, request, jsonify
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from veinsdk import *
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from roi import *
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licensePath = "license.txt"
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license = ""
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machineCode = getMachineCode()
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print("\nmachineCode: ", machineCode.decode('utf-8'))
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try:
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with open(licensePath, 'r') as file:
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license = file.read().strip()
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except IOError as exc:
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print("failed to open license.txt: ", exc.errno)
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print("\nlicense: ", license)
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ret = setActivation(license.encode('utf-8'))
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print("\nactivation: ", ret)
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ret = initSDK()
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print("init: ", ret)
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app = Flask(__name__)
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def mat_to_bytes(mat):
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"""
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Convert cv::Mat image data (NumPy array in Python) to raw bytes.
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"""
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# Encode cv::Mat as PNG bytes
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is_success, buffer = cv2.imencode(".png", mat)
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if not is_success:
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raise ValueError("Failed to encode cv::Mat image")
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return buffer.tobytes()
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@app.route('/palmvein', methods=['POST'])
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def palmvein():
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result = None
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score = None
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file1 = request.files['file1']
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file2 = request.files['file2']
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try:
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image1 = cv2.imdecode(np.frombuffer(file1.read(), np.uint8), cv2.IMREAD_COLOR)
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except:
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result = "Failed to open file1"
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response = jsonify({"result": result, "score": float(score)})
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response.status_code = 200
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response.headers["Content-Type"] = "application/json; charset=utf-8"
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return response
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try:
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image2 = cv2.imdecode(np.frombuffer(file2.read(), np.uint8), cv2.IMREAD_COLOR)
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except:
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result = "Failed to open file2"
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response = jsonify({"result": result, "score": float(score)})
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response.status_code = 200
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response.headers["Content-Type"] = "application/json; charset=utf-8"
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return response
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roi1, label1 = get_roi_image(cv2.flip(image1, 1))
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roi2, label2 = get_roi_image(cv2.flip(image2, 1))
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if label1 != label2:
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result = "2 images are from the different hand"
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score = 0.0
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response = jsonify({"result": result, "score": float(score)})
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response.status_code = 200
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response.headers["Content-Type"] = "application/json; charset=utf-8"
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return response
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if roi1 is None or roi2 is None:
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result = "\n hand detection failed !\n plesae make sure that input hand image is valid or not."
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response = jsonify({"result": result, "score": float(score)})
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response.status_code = 200
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response.headers["Content-Type"] = "application/json; charset=utf-8"
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return response
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roi_byte1 = mat_to_bytes(roi1)
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roi_byte2 = mat_to_bytes(roi2)
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feature_array1, feature_array2 = (c_float * 1024)(), (c_float * 1024)() # Assuming a maximum of 256 rectangles
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cnt1 = getFeature(roi_byte1, len(roi_byte1), feature_array1)
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cnt2 = getFeature(roi_byte2, len(roi_byte2), feature_array2)
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if cnt1 == 0 or cnt2 ==0:
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result = "feature extraction failed !"
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response = jsonify({"result": result, "score": float(score)})
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response.status_code = 200
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response.headers["Content-Type"] = "application/json; charset=utf-8"
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return response
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score = getScore(feature_array1, cnt1, feature_array2, cnt2)
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if score >= 0.65:
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result = "Same Hand !"
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# print(f"\n 2 images are from the same hand\n similarity: {score}")
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response = jsonify({"result": result, "score": float(score)})
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response.status_code = 200
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response.headers["Content-Type"] = "application/json; charset=utf-8"
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return response
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else:
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result = "Different Hand !"
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# print(f"\n 2 images are from the different hand\n similarity: {score}")
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response = jsonify({"result": result, "score": float(score)})
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response.status_code = 200
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response.headers["Content-Type"] = "application/json; charset=utf-8"
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return response
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@app.route('/palmvein_base64', methods=['POST'])
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def palmvein_base64():
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result = None
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score = None
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content = request.get_json()
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try:
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imageBase64 = content['base64_1']
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image_data = base64.b64decode(imageBase64)
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np_array = np.frombuffer(image_data, np.uint8)
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image1 = cv2.imdecode(np_array, cv2.IMREAD_COLOR)
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except:
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result = "Failed to open file1"
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response = jsonify({"result": result, "score": float(score)})
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response.status_code = 200
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response.headers["Content-Type"] = "application/json; charset=utf-8"
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return response
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try:
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imageBase64 = content['base64_2']
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image_data = base64.b64decode(imageBase64)
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np_array = np.frombuffer(image_data, np.uint8)
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image2 = cv2.imdecode(np_array, cv2.IMREAD_COLOR)
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except:
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result = "Failed to open file2"
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response = jsonify({"result": result, "score": float(score)})
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response.status_code = 200
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response.headers["Content-Type"] = "application/json; charset=utf-8"
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return response
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roi1, label1 = get_roi_image(cv2.flip(image1, 1))
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roi2, label2 = get_roi_image(cv2.flip(image2, 1))
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if label1 != label2:
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result = "2 images are from the different hand"
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score = 0.0
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response = jsonify({"result": result, "score": float(score)})
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response.status_code = 200
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response.headers["Content-Type"] = "application/json; charset=utf-8"
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return response
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if roi1 is None or roi2 is None:
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result = "\n hand detection failed !\n plesae make sure that input hand image is valid or not."
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response = jsonify({"result": result, "score": float(score)})
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response.status_code = 200
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response.headers["Content-Type"] = "application/json; charset=utf-8"
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return response
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roi_byte1 = mat_to_bytes(roi1)
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roi_byte2 = mat_to_bytes(roi2)
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feature_array1, feature_array2 = (c_float * 1024)(), (c_float * 1024)() # Assuming a maximum of 256 rectangles
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cnt1 = getFeature(roi_byte1, len(roi_byte1), feature_array1)
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cnt2 = getFeature(roi_byte2, len(roi_byte2), feature_array2)
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if cnt1 == 0 or cnt2 ==0:
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result = "feature extraction failed !"
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response = jsonify({"result": result, "score": float(score)})
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response.status_code = 200
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response.headers["Content-Type"] = "application/json; charset=utf-8"
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return response
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score = getScore(feature_array1, cnt1, feature_array2, cnt2)
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if score >= 0.65:
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result = "Same Hand !"
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# print(f"\n 2 images are from the same hand\n similarity: {score}")
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response = jsonify({"result": result, "score": float(score)})
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response.status_code = 200
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response.headers["Content-Type"] = "application/json; charset=utf-8"
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return response
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else:
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result = "Different Hand !"
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# print(f"\n 2 images are from the different hand\n similarity: {score}")
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response = jsonify({"result": result, "score": float(score)})
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response.status_code = 200
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response.headers["Content-Type"] = "application/json; charset=utf-8"
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return response
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if __name__ == '__main__':
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port = int(os.environ.get("PORT", 8080))
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app.run(host='0.0.0.0', port=port)
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libopencv.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:8845c1412c45c484e054235269944e2ac43c90a148ce3444215fe52049cf7479
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size 61014815
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libvein.so
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:85bf6dbeae145a4de388a31fbd0a066ab151ef3202acaef8a918aa732ccd9980
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size 3030231
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license.txt
ADDED
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gE2IfAwDsg/dQZwcfNkjWKKn2Q85X46TXZ1RimFkGkpQZIj58/YnxQRVcEQDTpHfLzl7RuemGG0E
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+
e4j1UL2ed2g3dbMJaJb6EWCyihr3NqtwQcXYTLLDUjrs0Yca9BeeToggaReRzFzwXGZgSPLnPdQ3
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+
RwOF1nPLEjcD/u/Z1ZdM487PaMlUivP3t8ZHlzUZvSOowfzHOlt8SIPP8C2BL3ZpqjA+dp2ycLC+
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+
Ak7lYVHnaGwiI3mjq4Gv/rUp9HK6/OkwAPxdZ7PbHpMahVyqoyy861LB9WNPi7ngbPxHWmaJQR26
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+
mi/+m+dew1YJ4pRQtqNsxLRNQBtgULkaZHx/qw==
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model/vein1v0.bin
ADDED
@@ -0,0 +1,3 @@
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+
version https://git-lfs.github.com/spec/v1
|
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+
oid sha256:98b3becafba3078506f57a7a63270e7b8e5adbd9f33e36facefe9c13fcc1dcf7
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+
size 96046676
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ncnn.zip
ADDED
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:6a702183c67f189f3a29c32186ab99e35b2df47a7f61c74aef2c600e7386059b
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size 32239397
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requirements.txt
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flask
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flask-cors
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gradio==3.50.2
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datadog_api_client
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opencv-python
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mediapipe
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roi.py
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import cv2
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import numpy as np
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import mediapipe as mp
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mp_drawing = mp.solutions.drawing_utils
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mp_drawing_styles = mp.solutions.drawing_styles
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mp_hands = mp.solutions.hands
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roi_bad_pixel_number = 500 # the number of pixel where 3 values are zero in RGB channel on ROI image
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roi_aspect_ratio_threshold = 100 # the passable aspect ratio threshold of ROI image
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roi_size_threshold = 0.23
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padding_size = 300 # the extra marine size of the frame inputted into Google Mediapipe Graph (this value must be a multiple of two)
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def img_padding(img):
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h, w, _ = img.shape
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image = np.zeros((h + padding_size, w + padding_size, 3), np.uint8)
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image[int(padding_size / 2):-int(padding_size / 2), int(padding_size / 2):-int(padding_size / 2), :] = img
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return image
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def img_crop(img_original, x2, x1, y2, y1, label):
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h, w, _ = img_original.shape
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img = np.zeros((h + 20, w + 20, 3), np.uint8)
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img[10:-10, 10:-10, :] = img_original
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if label == "Right":
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v1 = np.array([x2 * w, y2 * h])
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v2 = np.array([x1 * w, y1 * h])
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else:
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v2 = np.array([x2 * w, y2 * h])
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v1 = np.array([x1 * w, y1 * h])
|
30 |
+
|
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theta = np.arctan2((v2 - v1)[1], (v2 - v1)[0]) * 180 / np.pi
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R = cv2.getRotationMatrix2D(tuple([int(v2[0]), int(v2[1])]), theta, 1)
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v1 = (R[:, :2] @ v1 + R[:, -1]).astype(int)
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v2 = (R[:, :2] @ v2 + R[:, -1]).astype(int)
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img_r = cv2.warpAffine(img, R, (w, h))
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37 |
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if 1:
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ux = int(v1[0] - (v2 - v1)[0] * 0.05)
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uy = int(v1[1] + (v2 - v1)[0] * 0.05)
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lx = int(v2[0] + (v2 - v1)[0] * 0.05)
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ly = int(v2[1] + (v2 - v1)[0] * 1)
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else:
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ux = int(v1[0] - (v2 - v1)[0] * 0.1)
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uy = int(v1[1] + (v2 - v1)[0] * 0.1)
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lx = int(v2[0] + (v2 - v1)[0] * 0.1)
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ly = int(v2[1] + (v2 - v1)[0] * 1.2)
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# delta_y is movement value in y ward
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delta_y = (ly - uy) * 0.15
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ly = int(ly - delta_y)
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uy = int(uy - delta_y)
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delta_x = (lx - ux) * 0.01
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lx = int(lx + delta_x)
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ux = int(ux + delta_x)
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if label == "Right":
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delta_x = (lx - ux) * 0.05
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lx = int(lx + delta_x)
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ux = int(ux + delta_x)
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# roi = img_r
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roi = img_r[uy:ly, ux:lx]
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if roi.shape[0] == 0 or roi.shape[1] == 0:
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print("error 1")
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return None, 3
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if abs(roi.shape[0] - roi.shape[1]) > roi_aspect_ratio_threshold:
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print("error 2", abs(roi.shape[0] - roi.shape[1]))
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return None, 4
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if roi.shape[1] / w < roi_size_threshold:
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print("error 3", roi.shape[1] / w)
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return None, 7
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n_zeros = np.count_nonzero(roi == 0)
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if n_zeros > roi_bad_pixel_number:
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print("error 4", n_zeros)
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return None, 5
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return roi, 0
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def cupped_hand_filter(hand_landmarks):
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return hand_landmarks.landmark[12].y - hand_landmarks.landmark[11].y
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def get_roi(path, hand_type, x1, y1, x2, y2):
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img = cv2.imread(path)
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if hand_type != 0:
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label = "Left"
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else:
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label = "Right"
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roi, _ = img_crop(img, x1, x2, y1, y2, label)
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return roi
|
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+
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def get_roi_image(img):
|
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|
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label = ""
|
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with mp_hands.Hands(
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99 |
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static_image_mode=True,
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max_num_hands=2,
|
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min_detection_confidence=0.5) as hands:
|
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# Read an image, flip it around y-axis for correct handedness output (see
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# above).
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if 1:
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image = img_padding(img)
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else:
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107 |
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image = cv2.flip(cv2.imread(file), 1)
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# Convert the BGR image to RGB before processing.
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results = hands.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
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111 |
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112 |
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# Print handedness and draw hand landmarks on the image.
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if results.multi_handedness is not None:
|
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label = results.multi_handedness[0].classification[0].label
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116 |
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if results.multi_hand_landmarks is None:
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return None, None
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image_height, image_width, _ = image.shape
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hand_landmarks = results.multi_hand_landmarks[0]
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if cupped_hand_filter(hand_landmarks) > 0:
|
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return None, None
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else:
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roi, roi_msg_index = img_crop(image, hand_landmarks.landmark[5].x, hand_landmarks.landmark[17].x,
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126 |
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hand_landmarks.landmark[5].y, hand_landmarks.landmark[17].y, label)
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return roi, label
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run.sh
ADDED
@@ -0,0 +1,3 @@
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1 |
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# cd /home/openvino/kby-ai-fire
|
2 |
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# exec python3 demo.py &
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3 |
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exec python3 app.py
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veinsdk.py
ADDED
@@ -0,0 +1,26 @@
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1 |
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import os
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2 |
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3 |
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from ctypes import *
|
4 |
+
|
5 |
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libPath = os.path.abspath(os.path.dirname(__file__)) + '/libvein.so'
|
6 |
+
veinsdk = cdll.LoadLibrary(libPath)
|
7 |
+
|
8 |
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getMachineCode = veinsdk.getMachineCode
|
9 |
+
getMachineCode.argtypes = []
|
10 |
+
getMachineCode.restype = c_char_p
|
11 |
+
|
12 |
+
setActivation = veinsdk.setActivation
|
13 |
+
setActivation.argtypes = [c_char_p]
|
14 |
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setActivation.restype = c_int32
|
15 |
+
|
16 |
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initSDK = veinsdk.initSDK
|
17 |
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initSDK.argtypes = []
|
18 |
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initSDK.restype = c_int32
|
19 |
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|
20 |
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getFeature = veinsdk.get_feature
|
21 |
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getFeature.argtypes = [c_char_p, c_ulong, POINTER(c_float)]
|
22 |
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getFeature.restype = c_int32
|
23 |
+
|
24 |
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getScore = veinsdk.get_score
|
25 |
+
getScore.argtypes = [POINTER(c_float), c_ulong, POINTER(c_float), c_ulong]
|
26 |
+
getScore.restype = c_float
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