├── README.md ├── orientaation.png ├── Orientation.py ├── crop.py ├── rotation.py ├── projet.py └── orientation_quelque_soit_angle.ipynb /README.md: -------------------------------------------------------------------------------- 1 | # DataScienceProject 2 | Projet-PI-4DS2 3 | -------------------------------------------------------------------------------- /orientaation.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MuhamedHabib/DataScienceProject/master/orientaation.png -------------------------------------------------------------------------------- /Orientation.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import argparse 3 | import cv2 4 | 5 | 6 | ap = argparse.ArgumentParser() 7 | ap.add_argument("-i", "--image", required=True, help="path to input image file") 8 | args = vars(ap.parse_args()) 9 | 10 | image = cv2.imread(args["image"]) 11 | 12 | gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) 13 | gray = cv2.bitwise_not(gray) 14 | 15 | thresh = cv2.threshold(gray, 0, 255, 16 | cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1] 17 | 18 | coords = np.column_stack(np.where(thresh > 0)) 19 | angle = cv2.minAreaRect(coords)[-1] 20 | 21 | 22 | # range [-90, 0); as the rectangle rotates clockwise the 23 | # returned angle trends to 0 -- in this special case we 24 | # need to add 90 degrees to the angle 25 | if angle < -45: 26 | angle = -(90 + angle) 27 | 28 | else: 29 | angle = -angle 30 | 31 | (h, w) = image.shape[:2] 32 | center = (w // 2, h // 2) 33 | M = cv2.getRotationMatrix2D(center, angle, 1.0) 34 | rotated = cv2.warpAffine(image, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE) 35 | 36 | cv2.putText(rotated, "Angle: {:.2f} degrees".format(angle),(10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2) 37 | print("[INFO] angle: {:.3f}".format(angle)) 38 | cv2.imshow("Input", image) 39 | cv2.imshow("Rotated", rotated) 40 | cv2.waitKey(0) 41 | -------------------------------------------------------------------------------- /crop.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import numpy as np 3 | 4 | 5 | ############################################## LITTLE FLAG + Carte grise########################## 6 | gr=cv2.imread('grise12.png') 7 | hsv = cv2.cvtColor(gr, cv2.COLOR_BGR2HSV) 8 | lower_red = np.array([0, 10, 120]) 9 | upper_red = np.array([15, 255, 255]) 10 | mask = cv2.inRange (hsv, lower_red, upper_red) 11 | 12 | contours, hierarchy = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) 13 | h, w = gr.shape[:2] 14 | thresh_area = 0.001 15 | list_contours = list() 16 | for c in contours: 17 | area = cv2.contourArea(c) 18 | 19 | if (area > thresh_area*h*w): 20 | rect_page = cv2.minAreaRect(c) 21 | box_page = np.int0(cv2.boxPoints(rect_page)) 22 | list_contours.append(box_page) 23 | 24 | sorted_contours= sorted(list_contours, key=cv2.contourArea, reverse= True) 25 | # 26 | for (i,c) in enumerate(sorted_contours): 27 | x,y,w,h= cv2.boundingRect(c) 28 | if(w>h)&(h*2>w)&(h<800)&(w<800): 29 | cropped_contour= gr[y:y+h, x:x+w] 30 | # pour savoir la couleur domiante 31 | pixels = np.float32(cropped_contour.reshape(-1, 3)) 32 | n_colors = 7 33 | criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 200, .1) 34 | flags = cv2.KMEANS_RANDOM_CENTERS 35 | _, labels, palette = cv2.kmeans(pixels, n_colors, None, criteria, 10, flags) 36 | _, counts = np.unique(labels, return_counts=True) 37 | dominant = palette[np.argmax(counts)] 38 | print(dominant) 39 | # si le rouge est plus dominant que le vert 40 | if(dominant[1]*1.8 360 avec un pas 5 degree######## 14 | for i in range (330,360,5): 15 | img = cv2.imread('exemple3.jpg') 16 | img=imutils.rotate(img,angle=i) 17 | cv2.imwrite('rotated.jpg', img) 18 | height, width, _ = img.shape 19 | roi = img[0: height, 0: width] 20 | url_api = "https://api.ocr.space/parse/image" 21 | _, compressedimage = cv2.imencode(".jpg", roi, [1, 90]) 22 | file_bytes = io.BytesIO(compressedimage) 23 | result = requests.post(url_api, 24 | files = {'pic.jpg': file_bytes}, 25 | data = {'apikey': API_KEY, 26 | 'language': "ara"}) 27 | result = result.content.decode() 28 | result = json.loads(result) 29 | #print(result) 30 | if (len(result.get("ParsedResults"))!=0): 31 | parsed_results = result.get("ParsedResults")[0] 32 | text_detected = parsed_results.get("ParsedText") 33 | #word_list = text_detected.split() 34 | #number_of_words = len(word_list) 35 | #print(number_of_words) 36 | 37 | print(len(text_detected)) 38 | if (k 30 avec un pas 5 degree######## 44 | for i in range (0,30,5): 45 | img = cv2.imread('exemple3.jpg') 46 | img=imutils.rotate(img,angle=i) 47 | cv2.imwrite('rotated_30.jpg', img) 48 | height, width, _ = img.shape 49 | roi = img[0: height, 0: width] 50 | url_api = "https://api.ocr.space/parse/image" 51 | _, compressedimage = cv2.imencode(".jpg", roi, [1, 90]) 52 | file_bytes = io.BytesIO(compressedimage) 53 | result = requests.post(url_api, 54 | files = {'pic.jpg': file_bytes}, 55 | data = {'apikey': API_KEY, 56 | 'language': "ara"}) 57 | result = result.content.decode() 58 | result = json.loads(result) 59 | if (len(result.get("ParsedResults"))!=0): 60 | parsed_results = result.get("ParsedResults")[0] 61 | text_detected = parsed_results.get("ParsedText") 62 | #word_list = text_detected.split() 63 | #number_of_words = len(word_list) 64 | #print(number_of_words) 65 | 66 | print(len(text_detected)) 67 | if (k lim] = 255 24 | v[v <= lim] += value 25 | 26 | final_hsv = cv2.merge((h, s, v)) 27 | img = cv2.cvtColor(final_hsv, cv2.COLOR_HSV2BGR) 28 | return img 29 | 30 | clahefilter = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(16,16)) 31 | 32 | ################################### eliminer le flash#################### 33 | img = cv2.imread('exemple5.jpg') 34 | 35 | gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 36 | grayimg = gray 37 | #GLARE_MIN = np.array([0, 0, 50],np.uint8) 38 | #GLARE_MAX = np.array([0, 0, 225],np.uint8) 39 | #hsv_img = cv2.cvtColor(img,cv2.COLOR_BGR2HSV) 40 | #frame_threshed = cv2.inRange(hsv_img, GLARE_MIN, GLARE_MAX) 41 | mask1 = cv2.threshold(grayimg , 220, 255, cv2.THRESH_BINARY)[1] 42 | result1 = cv2.inpaint(img, mask1, 0.1, cv2.INPAINT_TELEA) 43 | #cv2.imshow("Image without glare", result1) 44 | cv2.imwrite('exemple5_without_glare.jpg', result1) 45 | 46 | ########################### ajouter brightness ################### 47 | ######################### (BACKGROUND MUST BE OFF) ################# 48 | brightness_=brightness_calculator('exemple5_without_glare.jpg') 49 | print(brightness_) 50 | if (brightness_>200): 51 | value=0 52 | elif(brightness_>170): 53 | value=10 54 | elif(brightness_>150): 55 | value=17 56 | elif(brightness_>140): 57 | value=24 58 | elif(brightness_>130): 59 | value=31 60 | elif(brightness_>120): 61 | value=38 62 | elif(brightness_>110): 63 | value=46 64 | elif(brightness_>100): 65 | value=53 66 | elif(brightness_>90): 67 | value=60 68 | elif(brightness_>80): 69 | value=67 70 | elif(brightness_>70): 71 | value=74 72 | elif(brightness_>60): 73 | value=81 74 | elif(brightness_>50): 75 | value=88 76 | elif(brightness_>40): 77 | value=95 78 | elif(brightness_>30): 79 | value=102 80 | elif(brightness_>20): 81 | value=109 82 | elif(brightness_>10): 83 | value=116 84 | else: 85 | value=123 86 | image = increase_brightness(result1, value) 87 | cv2.imshow('brightness',image) 88 | ####################### modifier l'image en eliminant les champs nom prenom date naissance....###### 89 | 90 | lower_black = np.array([0,0,0], dtype = "uint16") 91 | upper_black = np.array([110,110,110], dtype = "uint16") 92 | modified_img =255- cv2.inRange(image, lower_black, upper_black) 93 | cv2.imshow('image_without_labels',modified_img) 94 | cv2.imwrite('test_exemple5.jpg', filtred_img) 95 | 96 | #reader = easyocr.Reader(['ar']) 97 | #result = reader.readtext('MyImage.jpg',paragraph="False") 98 | #with open ('file4.txt','w',encoding='utf-8')as myfile: 99 | # myfile.write(str(result)) 100 | cv2.waitKey(0) 101 | cv2.destroyAllWindows() 102 | 103 | #################################### ELiminer le champ nom,prenom, date naissance.... de la cin###### 104 | 105 | """ 106 | lower_range_black = np.array([0,0,0], dtype = "uint16") 107 | upper_range_black = np.array([110,110,110], dtype = "uint16") 108 | filtred_img =255- cv2.inRange(image, lower_range_black, upper_range_black) 109 | cv2.imshow('Pic_BW_without_labels',filtred_img) 110 | cv2.imwrite('test5.jpg', filtred_img) 111 | """ 112 | 113 | 114 | 115 | 116 | ################################# affichage des contours par ordre decroissant ################ 117 | """ 118 | img = cv2.imread('exemple4.jpg') 119 | img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) 120 | gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 121 | bilateral = cv2.bilateralFilter(gray, 5, 5,5) 122 | eq = cv2.equalizeHist(bilateral) 123 | edged = cv2.Canny(eq, 0, 150) 124 | 125 | contours, hierarchy = cv2.findContours(edged, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) 126 | h, w = img.shape[:2] 127 | thresh_area = 0.001 128 | list_contours = list() 129 | for c in contours: 130 | area = cv2.contourArea(c) 131 | 132 | if (area > thresh_area*h*w): 133 | rect_page = cv2.minAreaRect(c) 134 | box_page = np.int0(cv2.boxPoints(rect_page)) 135 | list_contours.append(box_page) 136 | 137 | sorted_contours= sorted(list_contours, key=cv2.contourArea, reverse= True) 138 | # 139 | for (i,c) in enumerate(sorted_contours): 140 | x,y,w,h= cv2.boundingRect(c) 141 | 142 | cropped_contour= img[y:y+h, x:x+w] 143 | cv2.imwrite("contour.jpg", cropped_contour) 144 | readimage= cv2.imread("contour.jpg") 145 | cv2.imshow('Image', readimage) 146 | cv2.waitKey(0) 147 | 148 | cv2.destroyAllWindows() 149 | """ 150 | 151 | ################################ IMAGE BLACK AND WHITE ################## 152 | #image = cv2.imread(IMAGE_PTH) 153 | #img_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) 154 | #ret, thresh = cv2.threshold(img_gray, 140, 255, cv2.THRESH_BINARY) 155 | #cv2.imshow('image BW', thresh) 156 | #cv2.waitKey(0) 157 | #cv2.imwrite('image_BW.jpg', thresh) 158 | #cv2.destroyAllWindows() -------------------------------------------------------------------------------- /orientation_quelque_soit_angle.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "03eeeacf", 6 | "metadata": {}, 7 | "source": [ 8 | "# orientation horizontale: \n", 9 | "---" 10 | ] 11 | }, 12 | { 13 | "cell_type": "code", 14 | "execution_count": 53, 15 | "id": "b642ad7d", 16 | "metadata": {}, 17 | "outputs": [ 18 | { 19 | "data": { 20 | "text/plain": [ 21 | "True" 22 | ] 23 | }, 24 | "execution_count": 53, 25 | "metadata": {}, 26 | "output_type": "execute_result" 27 | } 28 | ], 29 | "source": [ 30 | "import cv2 as cv\n", 31 | "import matplotlib.pyplot as plt\n", 32 | "from math import atan2, cos, sin, sqrt, pi\n", 33 | "import numpy as np\n", 34 | "import math\n", 35 | "def drawAxis(img, p_, q_, color, scale):\n", 36 | " p = list(p_)\n", 37 | " q = list(q_)\n", 38 | " \n", 39 | " ## [visualization1]\n", 40 | " angle = atan2(p[1] - q[1], p[0] - q[0]) # angle in radians\n", 41 | " hypotenuse = sqrt((p[1] - q[1]) * (p[1] - q[1]) + (p[0] - q[0]) * (p[0] - q[0]))\n", 42 | " \n", 43 | " # Here we lengthen the arrow by a factor of scale\n", 44 | " q[0] = p[0] - scale * hypotenuse * cos(angle)\n", 45 | " q[1] = p[1] - scale * hypotenuse * sin(angle)\n", 46 | " cv.line(img, (int(p[0]), int(p[1])), (int(q[0]), int(q[1])), color, 3, cv.LINE_AA)\n", 47 | " \n", 48 | " # create the arrow hooks\n", 49 | " p[0] = q[0] + 9 * cos(angle + pi / 4)\n", 50 | " p[1] = q[1] + 9 * sin(angle + pi / 4)\n", 51 | " cv.line(img, (int(p[0]), int(p[1])), (int(q[0]), int(q[1])), color, 3, cv.LINE_AA)\n", 52 | " \n", 53 | " p[0] = q[0] + 9 * cos(angle - pi / 4)\n", 54 | " p[1] = q[1] + 9 * sin(angle - pi / 4)\n", 55 | " cv.line(img, (int(p[0]), int(p[1])), (int(q[0]), int(q[1])), color, 3, cv.LINE_AA)\n", 56 | " ## [visualization1]\n", 57 | " \n", 58 | "def getOrientation(pts, img):\n", 59 | " ## [pca]\n", 60 | " # Construct a buffer used by the pca analysis\n", 61 | " sz = len(pts)\n", 62 | " data_pts = np.empty((sz, 2), dtype=np.float64)\n", 63 | " for i in range(data_pts.shape[0]):\n", 64 | " data_pts[i,0] = pts[i,0,0]\n", 65 | " data_pts[i,1] = pts[i,0,1]\n", 66 | " \n", 67 | " # Perform PCA analysis\n", 68 | " mean = np.empty((0))\n", 69 | " mean, eigenvectors, eigenvalues = cv.PCACompute2(data_pts, mean)\n", 70 | " \n", 71 | " # Store the center of the object\n", 72 | " cntr = (int(mean[0,0]), int(mean[0,1]))\n", 73 | " ## [pca]\n", 74 | " \n", 75 | " ## [visualization]\n", 76 | " # Draw the principal components\n", 77 | " cv.circle(img, cntr, 3, (255, 0, 255), 2)\n", 78 | " p1 = (cntr[0] + 0.02 * eigenvectors[0,0] * eigenvalues[0,0], cntr[1] + 0.02 * eigenvectors[0,1] * eigenvalues[0,0])\n", 79 | " p2 = (cntr[0] - 0.02 * eigenvectors[1,0] * eigenvalues[1,0], cntr[1] - 0.02 * eigenvectors[1,1] * eigenvalues[1,0])\n", 80 | " drawAxis(img, cntr, p1, (255, 255, 0), 1)\n", 81 | " drawAxis(img, cntr, p2, (0, 0, 255), 5)\n", 82 | " \n", 83 | " angle = atan2(eigenvectors[0,1], eigenvectors[0,0]) # orientation in radians\n", 84 | " ## [visualization]\n", 85 | " \n", 86 | " # Label with the rotation angle\n", 87 | " label = \" Rotation Angle: \" + str(-int(np.rad2deg(angle)) - 90) + \" degrees\"\n", 88 | " print(-int(np.rad2deg(angle)))\n", 89 | " textbox = cv.rectangle(img, (cntr[0], cntr[1]-25), (cntr[0] + 250, cntr[1] + 10), (255,255,255), -1)\n", 90 | " cv.putText(img, label, (cntr[0], cntr[1]), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,0), 1, cv.LINE_AA)\n", 91 | " \n", 92 | " return angle\n", 93 | " \n", 94 | "# Load the image\n", 95 | "img_real = cv.imread(\"5.jpg\")\n", 96 | " \n", 97 | "# Was the image there?\n", 98 | "if img_real is None:\n", 99 | " print(\"Error: File not found\")\n", 100 | " exit(0)\n", 101 | "img = img_real.copy()\n", 102 | " \n", 103 | "cv.imshow('Input Image', img)\n", 104 | " \n", 105 | "# Convert image to grayscale\n", 106 | "gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\n", 107 | " \n", 108 | "# Convert image to binary\n", 109 | "_, bw = cv.threshold(gray, 50, 255, cv.THRESH_BINARY | cv.THRESH_OTSU)\n", 110 | " \n", 111 | "# Find all the contours in the thresholded image\n", 112 | "contours, _ = cv.findContours(bw, cv.RETR_LIST, cv.CHAIN_APPROX_NONE)\n", 113 | " \n", 114 | "for i, c in enumerate(contours):\n", 115 | " \n", 116 | " # Calculate the area of each contour\n", 117 | " area = cv.contourArea(c)\n", 118 | "\n", 119 | " # Ignore contours that are too small or too large\n", 120 | " if area < 3700 or 100000 < area:\n", 121 | " continue\n", 122 | "\n", 123 | " # cv.minAreaRect returns:\n", 124 | " # (center(x, y), (width, height), angle of rotation) = cv2.minAreaRect(c)\n", 125 | " rect = cv.minAreaRect(c)\n", 126 | " box = cv.boxPoints(rect)\n", 127 | " box = np.int0(box)\n", 128 | "\n", 129 | " # Retrieve the key parameters of the rotated bounding box\n", 130 | " center = (int(rect[0][0]),int(rect[0][1])) \n", 131 | " width = int(rect[1][0])\n", 132 | " height = int(rect[1][1])\n", 133 | " angle = int(rect[2])\n", 134 | "\n", 135 | "\n", 136 | " if width < height:\n", 137 | " angle = 90 - angle\n", 138 | " else:\n", 139 | " angle = -angle\n", 140 | "\n", 141 | " label = \" Rotation Angle: \" + str(angle) + \" degrees\"\n", 142 | " textbox = cv.rectangle(img, (center[0]-35, center[1]-25), \n", 143 | " (center[0] + 295, center[1] + 10), (255,255,255), -1)\n", 144 | " cv.putText(img, label, (center[0]-50, center[1]), \n", 145 | " cv.FONT_HERSHEY_SIMPLEX, 0.7, (0,0,0), 1, cv.LINE_AA)\n", 146 | " cv.drawContours(img,[box],0,(0,0,255),2)\n", 147 | " \n", 148 | "cv.imshow('Output Image', img)\n", 149 | "cv.waitKey(0)\n", 150 | "cv.destroyAllWindows()\n", 151 | " \n", 152 | "# Save the output image to the current directory\n", 153 | "cv.imwrite(\"output_img.jpg\", img)\n" 154 | ] 155 | }, 156 | { 157 | "cell_type": "code", 158 | "execution_count": 58, 159 | "id": "3d932be9", 160 | "metadata": {}, 161 | "outputs": [], 162 | "source": [ 163 | "#a=getOrientation(c, img)" 164 | ] 165 | }, 166 | { 167 | "cell_type": "code", 168 | "execution_count": 59, 169 | "id": "c565ccd5", 170 | "metadata": {}, 171 | "outputs": [ 172 | { 173 | "data": { 174 | "text/plain": [ 175 | "111" 176 | ] 177 | }, 178 | "execution_count": 59, 179 | "metadata": {}, 180 | "output_type": "execute_result" 181 | } 182 | ], 183 | "source": [ 184 | "angle" 185 | ] 186 | }, 187 | { 188 | "cell_type": "code", 189 | "execution_count": 60, 190 | "id": "6ce7448c", 191 | "metadata": {}, 192 | "outputs": [], 193 | "source": [ 194 | "num_rows, num_cols = img_real.shape[:2]" 195 | ] 196 | }, 197 | { 198 | "cell_type": "code", 199 | "execution_count": 61, 200 | "id": "3997492f", 201 | "metadata": {}, 202 | "outputs": [], 203 | "source": [ 204 | "img_rotation = cv.warpAffine(img_real, cv.getRotationMatrix2D((num_cols/2, num_rows/2),-angle, 0.6), (num_cols, num_rows))" 205 | ] 206 | }, 207 | { 208 | "cell_type": "code", 209 | "execution_count": 62, 210 | "id": "6b76299e", 211 | "metadata": {}, 212 | "outputs": [ 213 | { 214 | "data": { 215 | "text/plain": [ 216 | "" 217 | ] 218 | }, 219 | "execution_count": 62, 220 | "metadata": {}, 221 | "output_type": "execute_result" 222 | }, 223 | { 224 | "data": { 225 | "image/png": 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\n", 226 | "text/plain": [ 227 | "
" 228 | ] 229 | }, 230 | "metadata": { 231 | "needs_background": "light" 232 | }, 233 | "output_type": "display_data" 234 | } 235 | ], 236 | "source": [ 237 | "plt.imshow(img_rotation)" 238 | ] 239 | }, 240 | { 241 | "cell_type": "code", 242 | "execution_count": 63, 243 | "id": "b1e47a17", 244 | "metadata": {}, 245 | "outputs": [ 246 | { 247 | "data": { 248 | "text/plain": [ 249 | "True" 250 | ] 251 | }, 252 | "execution_count": 63, 253 | "metadata": {}, 254 | "output_type": "execute_result" 255 | }, 256 | { 257 | "data": { 258 | "image/png": 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\n", 259 | "text/plain": [ 260 | "
" 261 | ] 262 | }, 263 | "metadata": { 264 | "needs_background": "light" 265 | }, 266 | "output_type": "display_data" 267 | } 268 | ], 269 | "source": [ 270 | "plt.imshow(img_real)\n" 271 | ] 272 | }, 273 | { 274 | "cell_type": "code", 275 | "execution_count": null, 276 | "id": "362803a2", 277 | "metadata": {}, 278 | "outputs": [], 279 | "source": [ 280 | "cv.imwrite(\"skew_img.jpg\", img_rotation)\n" 281 | ] 282 | }, 283 | { 284 | "cell_type": "code", 285 | "execution_count": null, 286 | "id": "c8eea0d4", 287 | "metadata": {}, 288 | "outputs": [], 289 | "source": [] 290 | }, 291 | { 292 | "cell_type": "code", 293 | "execution_count": null, 294 | "id": "8860b75e", 295 | "metadata": {}, 296 | "outputs": [], 297 | "source": [] 298 | }, 299 | { 300 | "cell_type": "code", 301 | "execution_count": null, 302 | "id": "ddcdc61a", 303 | "metadata": {}, 304 | "outputs": [], 305 | "source": [] 306 | }, 307 | { 308 | "cell_type": "code", 309 | "execution_count": null, 310 | "id": "2b75cdcc", 311 | "metadata": {}, 312 | "outputs": [], 313 | "source": [] 314 | }, 315 | { 316 | "cell_type": "code", 317 | "execution_count": null, 318 | "id": "78ebab58", 319 | "metadata": {}, 320 | "outputs": [], 321 | "source": [] 322 | }, 323 | { 324 | "cell_type": "code", 325 | "execution_count": null, 326 | "id": "b5e64b3d", 327 | "metadata": {}, 328 | "outputs": [], 329 | "source": [] 330 | }, 331 | { 332 | "cell_type": "code", 333 | "execution_count": null, 334 | "id": "a35fbe85", 335 | "metadata": {}, 336 | "outputs": [], 337 | "source": [] 338 | }, 339 | { 340 | "cell_type": "code", 341 | "execution_count": null, 342 | "id": "5075b353", 343 | "metadata": {}, 344 | "outputs": [], 345 | "source": [] 346 | }, 347 | { 348 | "cell_type": "code", 349 | "execution_count": null, 350 | "id": "63de15f0", 351 | "metadata": {}, 352 | "outputs": [], 353 | "source": [] 354 | }, 355 | { 356 | "cell_type": "code", 357 | "execution_count": null, 358 | "id": "8c6086fe", 359 | "metadata": {}, 360 | "outputs": [], 361 | "source": [] 362 | }, 363 | { 364 | "cell_type": "code", 365 | "execution_count": null, 366 | "id": "d22a1cab", 367 | "metadata": {}, 368 | "outputs": [], 369 | "source": [] 370 | } 371 | ], 372 | "metadata": { 373 | "kernelspec": { 374 | "display_name": "Python 3", 375 | "language": "python", 376 | "name": "python3" 377 | }, 378 | "language_info": { 379 | "codemirror_mode": { 380 | "name": "ipython", 381 | "version": 3 382 | }, 383 | "file_extension": ".py", 384 | "mimetype": "text/x-python", 385 | "name": "python", 386 | "nbconvert_exporter": "python", 387 | "pygments_lexer": "ipython3", 388 | "version": "3.8.8" 389 | } 390 | }, 391 | "nbformat": 4, 392 | "nbformat_minor": 5 393 | } 394 | --------------------------------------------------------------------------------