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<|repo_name|>GeeShin/Handwriting-Detection<|file_sep|>/src/BackgroundSubtractor.py import numpy as np import cv2 class BackgroundSubtractor: def __init__(self): self.background = None self.subtracted = None self.prev_subtracted = None self.motion_history = None def initialize(self, image): self.background = np.zeros(image.shape) self.subtracted = np.zeros(image.shape) self.prev_subtracted = np.zeros(image.shape) self.motion_history = np.zeros(image.shape[:2]) def update(self, image): if self.background is None: self.initialize(image) self.subtracted = cv2.absdiff(image, self.background) self.subtracted[self.subtracted > (255 * .1)] = 255 diff = cv2.absdiff(self.subtracted, self.prev_subtracted) motion_mask = diff > (255 * .1) self.motion_history[motion_mask] += .1 self.motion_history[~motion_mask] *= .9 self.prev_subtracted[:] = self.subtracted[:] def get_motion_history(self): return self.motion_history def get_background(self): return self.background def get_subtracted(self): return self.subtracted<|repo_name|>GeeShin/Handwriting-Detection<|file_sep|>/src/FeatureExtractor.py import cv2 import numpy as np from utils import * class FeatureExtractor: def __init__(self): pass def extract_features(self, image): if len(image.shape) == 3: image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) features = [] features.extend(extract_lbp_features(image)) return features def extract_lbp_features(image): features = [] cells = extract_cells(image) for cell in cells: lbp = extract_lbp(cell) features.extend(lbp.flatten()) return features<|repo_name|>GeeShin/Handwriting-Detection<|file_sep|>/src/utils.py import numpy as np import cv2 def detect_edges(image): image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) kernel_size = (5,5) blurred_image = cv2.GaussianBlur(image_gray,kernel_size,(0,0)) sobelx_64f = cv2.Sobel(blurred_image,cv2.CV_64F,1,0,ksize=5) sobely_64f = cv2.Sobel(blurred_image,cv2.CV_64F,0,1,ksize=5) image_sobelx_8u=cv2.convertScaleAbs(sobelx_64f) #convert back to uint8 image_sobely_8u=cv2.convertScaleAbs(sobely_64f) #convert back to uint8 image_sobelxy_8u=cv2.addWeighted(image_sobelx_8u,0.5,image_sobely_8u,0.5,0) #combine sobel x and y kernel_size=(5,5) #kernel size for dilation and erosion kernel=np.ones(kernel_size,np.uint8) #kernel for dilation and erosion image_dilation_8u=cv2.dilate(image_sobelxy_8u,kernel) #dilate image return image_dilation_8u def detect_contours(edges): contours,hierarchy=cv2.findContours(edges,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) return contours def get_bounding_rect(contour): rect=cv2.boundingRect(contour) return rect def extract_cells(image): cells=[] rows=int(np.sqrt(20)) #extract cells from an image with at least rows x rows number of cells cols=int(np.sqrt(20)) rows_step=int(np.floor(image.shape[0]/rows)) #step size between cells along rows cols_step=int(np.floor(image.shape[1]/cols)) for i in range(rows): #iterate over each cell for j in range(cols): x_start=i*rows_step y_start=j*cols_step x_end=x_start+rows_step y_end=y_start+cols_step cell=image[x_start:x_end,y_start:y_end] #extract cell from image cell=cv2.resize(cell,(24,24)) #resize cell to fixed size cells.append(cell) #append cell to list return cells def extract_lbp(cell): radius=1 n_points=8*radius lbp_image=np.zeros((cell.shape[0],cell.shape[1])) for i in range(radius+1): for j in range(radius+1): if (i==radius or j==radius): pixel=cell[i:i+radius*2,j:j+radius*2] #extract pixel neighborhood center_pixel=cell[i+radius,j+radius] #center pixel pattern=[] for m in range(pixel.shape[0]): for n in range(pixel.shape[1]): if (m!=radius or n!=radius): #exclude center pixel from pattern if (pixel[m,n]>=center_pixel): pattern.append(1) else: pattern.append(0) lbp_value=0 for p in range(len(pattern)): lbp_value+=pattern[p]*(pow(2,p)) lbp_image[i:i+radius*2,j:j+radius*2]=lbp_value return lbp_image<|file_sep|># Handwriting Detection This project is aimed at detecting handwritten text within an image using background subtraction and contour detection. ## Prerequisites The following software packages are required: - Python >= v3 - OpenCV >= v4 - scikit-learn >= v0.21 To install these packages run the following command: pip install -r requirements.txt ## Usage Run the following command to start the application: python main.py A window will pop up displaying the video stream from your camera device (default: device index `0`). Press `q` or `esc` to quit. ## License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. <|repo_name|>GeeShin/Handwriting-Detection<|file_sep|>/src/main.py import os import sys import time import numpy as np import cv2 from sklearn import svm from sklearn.externals import joblib from BackgroundSubtractor import BackgroundSubtractor from FeatureExtractor import FeatureExtractor class HandwritingDetector: def __init__(self): self.classifier_filename = os.path.join(os.path.dirname(__file__), 'classifier.pkl') self.bg_subtractor = BackgroundSubtractor() self.feature_extractor = FeatureExtractor() def train_classifier(self): print("Loading training data...") training_data_path = os.path.join(os.path.dirname(__file__), 'training_data') handwritten_data_filename = os.path.join(training_data_path,'handwritten.csv') handwritten_data_file=open(handwritten_data_filename,'r') handwritten_data_file.readline() #skip first line handwritten_data=[] #list for storing handwritten data for line in handwritten_data_file: #iterate over handwritten data file line by line line_split=line.strip().split(',') #split line at commas line_split.pop(0) #remove first column handwritten_data.append([float(x) for x in line_split]) #append row to handwritten data list print("Loading background data...") background_data_filename=os.path.join(training_data_path,'background.csv') background_data_file=open(background_data_filename,'r') background_data_file.readline() #skip first line background_data=[] #list for storing background data for line in background_data_file: #iterate over background data file line by line line_split=line.strip().split(',') #split line at commas line_split.pop(0) #remove first column background_data.append([float(x) for x in line_split]) #append row to background data list print("Training classifier...") classifier=svm.SVC(gamma='scale',C=10000) print("Training classifier with handwritten data...") classifier.fit(handwritten_data,[1]*len(handwritten_data)) print("Training classifier with background data...") classifier.fit(background_data,[0]*len(background_data)) print("Saving classifier...") joblib.dump(classifier,self.classifier_filename) def detect_handwriting(self,image,motion_threshold=.6,image_threshold=.6): edges=detect_edges(image) contours=detect_contours(edges) if contours!=None: contours=sorted(contours,key=cv2.contourArea)[::-1] for contour in contours: bounding_rect=get_bounding_rect(contour) if bounding_rect[3]>bounding_rect[1]*image_threshold: x,y,w,h=bounding_rect if h>=image.shape[0]*motion_threshold: cell=image[y:y+h,x:x+w] cell=cv2.resize(cell,(24,24)) features=self.feature_extractor.extract_features(cell) prediction=self.classifier.predict([features])[0] if prediction==1: return True return False def run(self): print("Loading classifier...") classifier=joblib.load(self.classifier_filename) cap=cv2.VideoCapture(0) while True: ret,image=cap.read() self.bg_subtractor.update(cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)) motion_history=self.bg_subtractor.get_motion_history() subtracted=self.bg_subtractor.get_subtracted() subtracted=subtracted.astype('uint8') subtracted[np.where(subtracted > (255 * .1))] = 255 subtracted=np.where(subtracted > (255 * .05),255,0).astype('uint8') kernel=np.ones((5,5),np.uint8) subtracted=cv2.morphologyEx(subtracted,cv2.MORPH_CLOSE,kernel) motion_mask=motion_history > .25 subtracted[np.where(motion_mask)]=(subtracted.astype('uint16') + (255 * .25)).astype('uint8') subtracted=np.where(subtracted > (255 * .05),255,0).astype('uint8') kernel=np.ones((5,5),np.uint8) subtracted=cv2.morphologyEx(subtracted,cv2.MORPH_CLOSE,kernel) if self.detect_handwriting(motion_history,image=image): print("Handwriting detected!") text="Handwriting Detected!" font=cv2.FONT_HERSHEY_SIMPLEX font_scale=.6 thickness=4 color=(255,255,255) size=text_size=cv2.getTextSize(text,False,cv2.FONT_HERSHEY_SIMPLEX,.6)[0] offset=(10,size[1]+10) box_coords=((image.shape[1]-size[0]-offset[0],image.shape[0]-offset[1]),(image.shape[1]-offset[0],image.shape[0])) box_color=(100*(int(text_color:=color)).tolist()) box_thickness=-1 cv.rectangle(image,*box_coords,color=color,width=thickness) text_origin=(box_coords[0][0]+offset[0],box_coords[0][1]+offset[1]) text_origin=tuple(np.array(text_origin).astype('int32')) cv.putText(img=image,text=text_orgin.putputText(img=image,text=text_orgin,text=text_orgin,text=text_orgin,text=text_orgin,text=text_orgin,text=text_orgin,text=text_orgin,text=text_orgin,text=text_orgin,text=text_orgin,text=text_orgin,text=text_orgin,text=text_orgin,text=text_orgin,text=text_orgin,text=text,color=colororg,color=colororg,color=colororg,color=colororg,color=colororg,color=colororg,color=colororg,color=colororg,covorg=covorg,forg=forg,forg=forg,forg=forg,forg=forg,forg=forg,forg=forg,forg=forg,forg=forg,forg=forg,forgt:cv.FONT_HERSHEY_SIMPLEX,scale=font_scale,lineType=cv.LINE_AA) combined=np.concatenate((cv.cvtColor(cv.cvtColor(subtracted,cv.COLOR_GRAYTOBGR),cv.COLOR_BGRTOHSV)[...,1],cv.cvtColor(cv.cvtColor(motion_history,cv.COLOR_GRAYTOBGR),cv.COLOR_BGRTOHSV)[...,1]),axis=1) combined=np.where(combined>(255*.25),255.,combined)/255.