post

distance_calculate3.ipynb

@@ -1,205 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "0: 384x512 1 focused_medium_core, 1 modular_small_core, 341.3ms\n",
-      "Speed: 1.5ms preprocess, 341.3ms inference, 1.5ms postprocess per image at shape (1, 3, 384, 512)\n"
-     ]
-    }
-   ],
-   "source": [
-    "import math\n",
-    "import cv2\n",
-    "import numpy as np\n",
-    "from ultralytics import YOLO\n",
-    "import json\n",
-    "\n",
-    "model = YOLO(\"posmPJSTRIKE_v1.3.pt\")\n",
-    "image = cv2.imread(\"1.jpeg\")\n",
-    "res = model(image)\n",
-    "\n",
-    "with open(\"base_width.json\", \"r\") as f:\n",
-    "    base_width = json.load(f)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def find_position(objects_names_points, par_pix_cm, image):\n",
-    "    object_positions = {}\n",
-    "    for obj in objects_names_points:\n",
-    "        name = list(obj.keys())[0]\n",
-    "        points = list(obj.values())[0]\n",
-    "\n",
-    "        top_distance = round((points[0][1] - 0) * par_pix_cm[name],2)\n",
-    "        bottom_distance = round((image.shape[0] - points[3][1]) * par_pix_cm[name],2)\n",
-    "        left_distance = round((points[0][0] - 0) * par_pix_cm[name],2)\n",
-    "        right_distance = round((image.shape[1] - points[3][0]) * par_pix_cm[name],2)\n",
-    "        \n",
-    "        object_positions.update({name: {\"top\": top_distance, \"bottom\": bottom_distance, \"left\": left_distance, \"right\": right_distance}})\n",
-    "    return object_positions\n",
-    "\n",
-    "def get_actual_distance(closest_points, par_pix_cm):\n",
-    "    actual_results_n_distance = {}\n",
-    "    for i in closest_points:\n",
-    "        avg_px_cm = ((par_pix_cm[i[0]] + par_pix_cm[i[1]])/2)\n",
-    "        actual_results_n_distance.update({i:round(closest_points[i]*avg_px_cm,2)})\n",
-    "    return actual_results_n_distance\n",
-    "\n",
-    "def pixel_per_cm(objects_names_width_pix):\n",
-    "    par_pix_cm = {}\n",
-    "    for i in objects_names_width_pix:\n",
-    "        par_pix_cm_width = base_width[i][0]/objects_names_width_pix[i][0]\n",
-    "        par_pix_cm_hight = base_width[i][1]/objects_names_width_pix[i][1]\n",
-    "        avg_par_pix_cm = (par_pix_cm_width + par_pix_cm_hight)/2\n",
-    "        par_pix_cm.update({i:avg_par_pix_cm})\n",
-    "    return par_pix_cm\n",
-    "\n",
-    "def get_points_n_names(results):\n",
-    "    objects_names_points = []\n",
-    "    objects_names_width_pix = {}\n",
-    "    for box, cls in zip(results[0].boxes.xyxy, results[0].boxes.cls):\n",
-    "        x1, y1, x2, y2 = map(int, box)\n",
-    "        class_name = results[0].names[int(cls)]\n",
-    "        width = x2 - x1\n",
-    "        hight = y2 - y1\n",
-    "        objects_names_points.append({class_name: [(x1, y1), (x2, y1), (x1, y2), (x2, y2)]})\n",
-    "        objects_names_width_pix.update({class_name: [width, hight]})\n",
-    "    \n",
-    "    return objects_names_points, objects_names_width_pix\n",
-    "\n",
-    "def euclidean_distance(point1, point2):\n",
-    "    dist_pixels = math.sqrt((point2[0] - point1[0])**2 + (point2[1] - point1[1])**2)\n",
-    "    return dist_pixels\n",
-    "\n",
-    "def find_closest_points(lst):\n",
-    "    closest_points = {}\n",
-    "    \n",
-    "    for i in range(len(lst)):\n",
-    "        for j in range(i + 1, len(lst)):\n",
-    "            list1 = lst[i]\n",
-    "            list2 = lst[j]\n",
-    "            min_distance = float('inf')\n",
-    "            closest_objects_pair = None\n",
-    "            \n",
-    "            for obj1 in list1.values():\n",
-    "                points1 = obj1\n",
-    "                for obj2 in list2.values():\n",
-    "                    points2 = obj2\n",
-    "                    \n",
-    "                    for point1 in points1:\n",
-    "                        for point2 in points2:\n",
-    "                            distance = euclidean_distance(point1, point2)\n",
-    "                            if distance < min_distance:\n",
-    "                                min_distance = distance\n",
-    "                                closest_objects_pair = (list1.keys(), list2.keys())\n",
-    "            \n",
-    "            closest_points.update({(list(closest_objects_pair[0])[0],list(closest_objects_pair[1])[0]):round(min_distance, 2)})\n",
-    "    return closest_points\n",
-    "\n",
-    "objects_names_points, objects_names_width_pix = get_points_n_names(res)\n",
-    "par_pix_cm = pixel_per_cm(objects_names_width_pix)\n",
-    "closest_points = find_closest_points(objects_names_points)\n",
-    "actual_distances = get_actual_distance(closest_points, par_pix_cm)\n",
-    "object_position = find_position(objects_names_points, par_pix_cm, image)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import requests\n",
-    "\n",
-    "url = \"https://WalidAlHassan-Distance.hf.space/process_image\"\n",
-    "data = {\"image_url\": \"https://i.postimg.cc/BQ0gHXjQ/1.jpg\"}\n",
-    "\n",
-    "response = requests.post(url, json=data)\n",
-    "\n",
-    "# print(response.status_code)\n",
-    "# print(response.json())  # Prints the response data\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Response [500]>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "response"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "icr",
-   "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": "3.12.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

post.ipynb

@@ -0,0 +1,46 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'object_positions': {'Regular Modular FnS': {'top': '107.55 cm', 'bottom': '0.0 cm', 'left': '103.1 cm', 'right': '33.44 cm'}, 'Focused Medium Core': {'top': '101.69 cm', 'bottom': '0.0 cm', 'left': '47.57 cm', 'right': '85.74 cm'}, 'Modular Small FnS': {'top': '83.9 cm', 'bottom': '34.75 cm', 'left': '49.15 cm', 'right': '86.44 cm'}}, 'actual_distances': [{'object': ['Regular Modular FnS', 'Focused Medium Core'], 'distances': '8.73 cm'}, {'object': ['Regular Modular FnS', 'Modular Small FnS'], 'distances': '12.97 cm'}, {'object': ['Focused Medium Core', 'Modular Small FnS'], 'distances': '1.42 cm'}]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import requests\n",
+    "url = \"https://WalidAlHassan-Distance.hf.space/process_image\"\n",
+    "data = {\"image_url\": \"https://i.postimg.cc/BQ0gHXjQ/1.jpg\"}\n",
+    "response = requests.post(url, json=data)\n",
+    "print(response.json())"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "icr",
+   "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": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}