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MASK_RCNN_ObjectDetection.py

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+import pixellib
+from pixellib.instance import instance_segmentation
+segment_image=instance_segmentation()
+segment_image.load_model("mask_rcnn_coco.h5")
+segment_image.segmentImage("test2.jpg",show_bboxes=True,output_image_name="output_image.jpg")

blackboard-detection/R-CNN ResNet-50 FPN [Training].ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4077a973",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from detecto import core"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "19f75e36",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "torch.cuda.is_available()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "75df1bc3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/hariraagavtr/.local/bin/.virtualenvs/cv/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead.\n",
+      "  warnings.warn(\n",
+      "/home/hariraagavtr/.local/bin/.virtualenvs/cv/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=FasterRCNN_ResNet50_FPN_Weights.COCO_V1`. You can also use `weights=FasterRCNN_ResNet50_FPN_Weights.DEFAULT` to get the most up-to-date weights.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset = core.Dataset('/home/hariraagavtr/Recapyt/dataset/blackboard/')\n",
+    "\n",
+    "# Model: R-CNN ResNet-50 FPN\n",
+    "model = core.Model(['board'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "3f1c10aa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "It looks like you're training your model on a CPU. Consider switching to a GPU; otherwise, this method can take hours upon hours or even days to finish. For more information, see https://detecto.readthedocs.io/en/latest/usage/quickstart.html#technical-requirements\n",
+      "Epoch 1 of 10\n",
+      "Begin iterating over training dataset\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [09:06<00:00, 10.93s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2 of 10\n",
+      "Begin iterating over training dataset\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [08:29<00:00, 10.19s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 3 of 10\n",
+      "Begin iterating over training dataset\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [08:54<00:00, 10.69s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 4 of 10\n",
+      "Begin iterating over training dataset\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [08:08<00:00,  9.77s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 5 of 10\n",
+      "Begin iterating over training dataset\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [08:38<00:00, 10.37s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 6 of 10\n",
+      "Begin iterating over training dataset\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [08:15<00:00,  9.90s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 7 of 10\n",
+      "Begin iterating over training dataset\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [08:48<00:00, 10.57s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 8 of 10\n",
+      "Begin iterating over training dataset\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [10:11<00:00, 12.22s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 9 of 10\n",
+      "Begin iterating over training dataset\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [08:35<00:00, 10.31s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 10 of 10\n",
+      "Begin iterating over training dataset\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [08:08<00:00,  9.76s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.fit(dataset)\n",
+    "model.save('BlackboardDetectionWeights.pth')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "cv",
+   "language": "python",
+   "name": "cv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

handtracking_basics.py

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+# If you are using pycharm go to settings -> project xyx->python interpr ADD opencv-python and mediapipe
+
+
+import cv2 as cv2
+import mediapipe as mp
+import time
+
+cap=cv2.VideoCapture(0)
+
+mpHands=mp.solutions.hands
+hands=mpHands.Hands() #to check param ctrl +click
+mpDraw=mp.solutions.drawing_utils
+pTime=0
+cTime=0
+
+while True:
+    success,img=cap.read()
+    imgRGB=cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
+    results=hands.process(imgRGB)
+    #print(results.multi_hand_landmarks) to check if its detecting hands
+    #print(img)
+    #To extract hands
+    if results.multi_hand_landmarks:
+        for handLms in results.multi_hand_landmarks:
+            for  id,lm in enumerate(handLms.landmark): #to extract cordinates
+                #print(id,lm)
+                h,w,c=img.shape
+                cx,cy=int(lm.x*w),int(lm.y*h)
+                #print(id,cx,cy)
+                if id==0: # ID 0 IS WRIST
+                    cv2.circle(img,(cx,cy),25,(255,0,255),cv2.FILLED)
+            mpDraw.draw_landmarks(img,handLms,mpHands.HAND_CONNECTIONS)#This is a single hand
+    cTime=time.time()
+    fps=1/(cTime-pTime)
+    pTime=cTime
+
+    cv2.putText(img,str(int(fps)),(10,70),cv2.FONT_HERSHEY_PLAIN,3,
+                (255,0,255),3)
+    cv2.imshow("Image",img)
+    cv2.waitKey(1)