Codespace augix sturdy palm tree pxqjgx66vh7rp6

Untitled.ipynb

@@ -0,0 +1,206 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "#!{sys.executable} -m pip install --upgrade pip -i https://pypi.tuna.tsinghua.edu.cn/simple\n",
+    "#!{sys.executable} -m pip install --upgrade pip"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!{sys.executable} -m pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple\n",
+    "#!{sys.executable} -m pip install -r requirements.txt "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run the following for cv2\n",
+    "#!sudo apt-get install libglu1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!/usr/bin/python\n",
+    "import cv2 as cv\n",
+    "import numpy as np\n",
+    "import sys\n",
+    "import glob\n",
+    "import multiprocessing\n",
+    "import roi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mCanceled future for execute_request message before replies were done"
+     ]
+    },
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     ]
+    }
+   ],
+   "source": [
+    "#TODO: Integrate with Henry's Code\n",
+    "#Get score\n",
+    "#Work on getting scale down\n",
+    "MIN_MATCH_COUNT = 4\n",
+    "DATAPATH = \"./data/standard/*\"\n",
+    "FILE = \"./data/demo-images/scattered4.jpg\"\n",
+    "\n",
+    "\n",
+    "def findInlier(img):\n",
+    "\t# Initiate SIFT detector\n",
+    "\n",
+    "\timg1 = img[0]\t\t# Image in question\n",
+    "\timg2 = img[1][0] \t# Gold standard image\n",
+    "\tsift = cv.SIFT()\n",
+    "\n",
+    "\tkp1, des1 = sift.detectAndCompute(img1, None)\n",
+    "\tkp2, des2 = sift.detectAndCompute(img2, None)\n",
+    "\n",
+    "\tFLANN_INDEX_KDTREE = 0\n",
+    "\tindex_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)\n",
+    "\tsearch_params = dict(checks=50)\n",
+    "\n",
+    "\tflann = cv.FlannBasedMatcher(index_params, search_params)\n",
+    "\n",
+    "\tmatches = flann.knnMatch(des1, des2, k=2)\n",
+    "\n",
+    "\t# Store all good matches as per Lowe's ratio test.\n",
+    "\tgood = [m for m, n in matches if m.distance < 0.7*n.distance]\n",
+    "\n",
+    "\tif len(good) > MIN_MATCH_COUNT:\n",
+    "\t\tsrc_pts = np.float32([kp1[m.queryIdx].pt for m in good]).reshape(-1, 1, 2)\n",
+    "\t\tdst_pts = np.float32([kp2[m.trainIdx].pt for m in good]).reshape(-1, 1, 2)\n",
+    "\n",
+    "\t\tM, mask = cv.findHomography(src_pts, dst_pts, cv.RANSAC, 5.0)\n",
+    "\t\tmatchesMask = mask.ravel().tolist()\n",
+    "\n",
+    "\telse:\n",
+    "\t\t#print \"Not enough matches are found - %d/%d\" % (len(good), MIN_MATCH_COUNT)\n",
+    "\t\tmatchesMask = []\n",
+    "\n",
+    "\t# Print Inliers\n",
+    "\treturn (len(matchesMask), img[1][1])\n",
+    "\n",
+    "\n",
+    "#Gets the nearest neighbor for each tile in\n",
+    "#a list of ROI tiles to the testData\n",
+    "def NN(tiles, testData):\n",
+    "\t\"\"\"param tiles: The regions of interest, in the form of images.\n",
+    "\tparam testData: Array of 30 or forty images.\n",
+    "\t\"\"\"\n",
+    "\t#store the max inliers for each tile\n",
+    "\tmaxInliers = []\n",
+    "\tfor tile in tiles:\n",
+    "\n",
+    "\t\tpairs = [(tile.copy(), img2) for img2 in testData]\n",
+    "\t\tif False:\n",
+    "\t\t\tp = multiprocessing.Pool(processes = 4)\n",
+    "\t\t\tinliers = p.map(findInlier, pairs)\n",
+    "\t\telse:\n",
+    "\t\t\tinliers = [findInlier(pair) for pair in pairs]\n",
+    "\n",
+    "\t\ttheMax = max(inliers)\n",
+    "\n",
+    "\t\t#Show the tile to identify\n",
+    "\t\tcv.imshow(\"Tile to identify\", tile)\n",
+    "\t\tcv.waitKey(1)\n",
+    "\n",
+    "\t\t#Show the best guess tile\n",
+    "\t\tguess = cv.imread(theMax[1])\n",
+    "\t\tguess = cv.resize(guess, (0,0), fx = 0.2, fy = 0.2)\n",
+    "\t\tcv.imshow(\"The guess\", guess)\n",
+    "\t\tcv.waitKey(0)\n",
+    "\n",
+    "\t\tmaxInliers.append(theMax)\n",
+    "\n",
+    "\n",
+    "#Converts a list of image names into a list of\n",
+    "#tuple's where the first value of the tuple is \n",
+    "#the OpenCV image and the second value of the \n",
+    "#tuple is the file name\n",
+    "def getImage(files):\n",
+    "\treturn [(cv.resize(cv.imread(f),(0,0), fx = 0.2, fy = 0.2), f) for f in files]\n",
+    "\n",
+    "def main():\n",
+    "\t#Get images\n",
+    "\n",
+    "\t#get list of images in golden data set\n",
+    "\t# Obtains the array of images to the golden standards\n",
+    "\ttestData = glob.glob(DATAPATH)\n",
+    "\timages = getImage(testData)\n",
+    "\t\n",
+    "\t#REMOVE ONCE HENRY'S DONE\n",
+    "\t# tiles contains an array of images\n",
+    "\ttiles = roi.findRoi(FILE)\n",
+    "\tinliers = NN(tiles, images)\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "\tmain()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "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"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "3ad933181bd8a04b432d3370b9dc3b0662ad032c4dfaa4e4f1596c548f763858"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

box.py

@@ -21,9 +21,9 @@ def create(cls, boxCon):
 
 	def __init__(self, x, y, width, height):
 		if width < 0 or height < 0:
-			print Fore.RED + "WARNING: Width or height is less than 0." + Fore.RESET
+			print( Fore.RED + "WARNING: Width or height is less than 0." + Fore.RESET )
 		elif x < 0 or height < 0:
-			print Fore.RED + "WARNING: x or y is less than 0." + Fore.RESET
+			print( Fore.RED + "WARNING: x or y is less than 0." + Fore.RESET )
 
 		self.startX = x
 		self.startY = y

requirements.txt

@@ -1,5 +1,13 @@
-Pillow==2.4.0
-numpy==1.8.1
-scipy==0.13.3
-wsgiref==0.1.2
-colorama==0.3.1
+# Pillow==2.4.0
+Pillow
+#numpy==1.8.1
+numpy
+#scipy==0.13.3
+scipy
+#wsgiref==0.1.2
+wsgiref
+# colorama==0.3.1
+colorama
+#opencv==3.1
+#opencv-python==2.4.9
+opencv-python