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Add notebooks for pose tracking, ReID
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‎notebooks/HPE_with_tracking.ipynb‎

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‎notebooks/Motion_Appearance_ReID.ipynb‎

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‎notebooks/age_det.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "O9kr2EPop_lf"
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},
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"source": [
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"# deep face"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "UHTVi3xHrLCb"
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},
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"outputs": [],
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"source": [
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"!pip install deepface"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "xQEBd7Mnp9c8"
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},
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"outputs": [],
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"source": [
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"from deepface import DeepFace\n",
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"import cv2\n",
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"from google.colab.patches import cv2_imshow\n",
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"\n",
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"\n",
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"img = cv2.imread(\"/content/input3.png\")\n",
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"analysis = DeepFace.analyze(img, actions=['age'], enforce_detection=False)\n",
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"age = analysis[0]['age']\n",
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"cv2.putText(img, f\"Age: {age}\", (50, 50), cv2.FONT_HERSHEY_SIMPLEX,\n",
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" 1, (0, 255, 0), 2)\n",
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"cv2_imshow(img)\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "zo1QbV1YqxFv"
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},
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"source": [
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"# opencv"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "vow9EhJKuhPh"
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},
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"outputs": [],
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"source": [
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"!wget -O opencv_face_detector_uint8.pb https://github.com/spmallick/learnopencv/raw/master/AgeGender/opencv_face_detector_uint8.pb\n",
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"!wget -O opencv_face_detector.pbtxt https://github.com/spmallick/learnopencv/raw/master/AgeGender/opencv_face_detector.pbtxt\n",
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"!wget -O age_deploy.prototxt https://github.com/spmallick/learnopencv/raw/master/AgeGender/age_deploy.prototxt\n",
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"!wget -O age_net.caffemodel https://github.com/spmallick/learnopencv/raw/master/AgeGender/age_net.caffemodel\n",
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"!wget -O age_net.caffemodel \"https://huggingface.co/AjaySharma/genderDetection/resolve/5cde30ccaa3ebff2e3d06876bf6412f19be183c3/age_net.caffemodel\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "5Afwlsj5Cp7V"
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},
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"outputs": [],
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"source": [
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"import cv2\n",
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"import numpy as np\n",
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"from google.colab.patches import cv2_imshow\n",
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"\n",
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"face_proto = \"/content/Gender-and-Age-Detection/opencv_face_detector.pbtxt\"\n",
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"face_model = \"/content/Gender-and-Age-Detection/opencv_face_detector_uint8.pb\"\n",
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"age_proto = \"/content/Gender-and-Age-Detection/age_deploy.prototxt\"\n",
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"age_model = \"/content/Gender-and-Age-Detection/age_net.caffemodel\"\n",
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"\n",
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"face_net = cv2.dnn.readNetFromTensorflow(face_model, face_proto)\n",
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"age_net = cv2.dnn.readNetFromCaffe(age_proto, age_model)\n",
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"\n",
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"AGE_BUCKETS = [\"(0-2)\", \"(4-6)\", \"(8-12)\", \"(15-20)\",\n",
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" \"(25-32)\", \"(38-43)\", \"(48-53)\", \"(60-100)\"]\n",
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"MODEL_MEAN_VALUES = (78.4263377603, 87.7689143744, 114.895847746)\n",
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"\n",
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"img = cv2.imread(\"/content/input.png\")\n",
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"h, w = img.shape[:2]\n",
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"\n",
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"blob = cv2.dnn.blobFromImage(img, 1.0, (300, 300), [104, 117, 123])\n",
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"face_net.setInput(blob)\n",
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"detections = face_net.forward()\n",
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"\n",
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"for i in range(detections.shape[2]):\n",
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" confidence = detections[0, 0, i, 2]\n",
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" if confidence > 0.5:\n",
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" x1, y1, x2, y2 = (detections[0, 0, i, 3:7] * np.array([w, h, w, h])).astype(int)\n",
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"\n",
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"\n",
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" x1, y1, x2, y2 = max(0, x1), max(0, y1), min(w, x2), min(h, y2)\n",
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" face = img[y1:y2, x1:x2]\n",
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"\n",
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" if face.size > 0:\n",
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" blob = cv2.dnn.blobFromImage(face, 1.0, (227, 227), MODEL_MEAN_VALUES, swapRB=False)\n",
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" age_net.setInput(blob)\n",
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" age_preds = age_net.forward()\n",
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" age = AGE_BUCKETS[age_preds[0].argmax()]\n",
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"\n",
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" cv2.rectangle(img, (x1, y1), (x2, y2), (255, 0, 0), 2)\n",
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" cv2.putText(img, f'Age: {age}', (x1, y1 - 10),\n",
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" cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)\n",
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"\n",
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"cv2_imshow(img)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "cUkB678gAfWi"
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},
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"source": [
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"# smahesh29/Gender-and-Age-Detection"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "OYY5XRC6vy4Q"
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},
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"outputs": [],
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"source": [
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"!git clone https://github.com/smahesh29/Gender-and-Age-Detection.git\n",
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"%cd Gender-and-Age-Detection\n",
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"!pip install opencv-python argparse\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "lieISP3pBD4l",
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"outputId": "bb0d3c6b-b3c0-4117-aa69-f9c631f23266"
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},
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"outputs": [],
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"source": [
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"%cd Gender-and-Age-Detection"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "uai0V9Q4Ae69",
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"outputId": "62ac4df6-a5aa-43c4-e0e0-106598f33c47"
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},
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"outputs": [],
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"source": [
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"!python detect.py --image /content/input3.png"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "BRzSgcZgDT3w"
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},
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"source": [
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"# yu4u/age-gender-estimation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "bdGjpEUYAe2S"
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},
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"outputs": [],
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"source": [
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"!git clone https://github.com/yu4u/age-gender-estimation.git\n",
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"%cd age-gender-estimation\n",
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"!pip install tensorflow keras opencv-python numpy\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "bfFpsB_mDdZ2",
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"outputId": "9aaea709-6014-4f85-98cc-e01e80aa0455"
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},
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"outputs": [],
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"source": [
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"!python demo.py --image_dir /content/input.png"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "yOBonjHoGloR"
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},
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"source": [
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"# MiVOLO"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "GbKTjF6fFFIC",
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"outputId": "765930ec-61d2-4da6-a50a-563636c28b03"
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},
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"outputs": [],
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"source": [
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"!git clone https://github.com/WildChlamydia/MiVOLO.git\n",
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"%cd MiVOLO\n",
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"!pip install -r requirements.txt\n",
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"!pip install .\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"id": "xh1a6IO1QkNx"
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},
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"outputs": [],
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"source": [
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"!mkdir /content/MiVOLO/models"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "dYrglLLKILC2",
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"outputId": "f8124239-624b-423b-ef94-affd331c4bea"
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},
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"outputs": [],
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"source": [
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"\n",
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"!gdown https://drive.google.com/uc?id=1CGNCkZQNj5WkP3rLpENWAOgrBQkUWRdw -O models/yolov8x_person_face.pt\n",
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"!gdown https://drive.google.com/uc?id=11i8pKctxz3wVkDBlWKvhYIh7kpVFXSZ4 -O models/mivolo_imbd.pth.tar\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "gC_x5Us_PCax",
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"outputId": "01ef1b51-f4bb-451a-a5fe-81b9cc430d28"
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},
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"outputs": [],
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"source": [
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"%cd MiVOLO"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "G2YDJD_2RPtJ"
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},
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"outputs": [],
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"source": [
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"#add in demo file\n",
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"import torch\n",
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"\n",
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"_old_torch_load = torch.load\n",
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"\n",
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"def torch_load_force_weights_only_false(*args, **kwargs):\n",
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" if 'weights_only' not in kwargs:\n",
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" kwargs['weights_only'] = False\n",
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" return _old_torch_load(*args, **kwargs)\n",
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"\n",
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"torch.load = torch_load_force_weights_only_false\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "m5nElLsnJI92",
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"outputId": "bf3a8bed-6198-428c-e31e-f461b0fb9f4d"
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},
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"outputs": [],
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"source": [
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"!python3 demo.py \\\n",
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"--input \"/content/input.png\" \\\n",
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"--output \"/content/output\" \\\n",
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"--detector-weights \"models/yolov8x_person_face.pt \" \\\n",
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"--checkpoint \"models/mivolo_imbd.pth.tar\" \\\n",
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"--device \"cuda:0\" \\\n",
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"--with-persons \\\n",
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"--draw"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# icatcher"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "PE9fcHzoqFRM"
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},
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"outputs": [],
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"source": [
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"!pip install icatcher\n",
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"video_path = '/content/video.mp4'\n",
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"!icatcher \"{video_path}\" \\\n",
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" --output_annotation /content/annotations \\\n",
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" --output_video_path /content/output_videos\n"
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]
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}
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],
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"metadata": {
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"accelerator": "GPU",
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"colab": {
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"gpuType": "T4",
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3",
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"name": "python3"
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},
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"language_info": {
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"name": "python"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}

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