├── .gitignore ├── LICENSE ├── README.md ├── manual ├── images │ ├── 01.jpg │ ├── 02.jpg │ ├── 03.png │ ├── 04.jpg │ ├── 08.jpg │ ├── 09.jpg │ ├── Fractured Bone │ │ ├── F1.JPG │ │ ├── F10.JPG │ │ ├── F100.JPG │ │ ├── F11.JPG │ │ ├── F12.jpg │ │ ├── F13.jpg │ │ ├── F14.jpg │ │ ├── F15.jpg │ │ ├── F16.jpg │ │ ├── F17.jpg │ │ ├── F18.jpg │ │ ├── F19.jpg │ │ ├── F2.JPG │ │ ├── F20.jpg │ │ ├── F21.jpg │ │ ├── F22.jpg │ │ ├── F23.jpg │ │ ├── F24.jpg │ │ ├── F25.jpg │ │ ├── F26.jpg │ │ ├── F27.jpg │ │ ├── F28.jpg │ │ ├── F29.jpg │ │ ├── F3.jpg │ │ ├── F30.jpg │ │ ├── F31.jpg │ │ ├── F32.jpg │ │ ├── F33.jpg │ │ ├── F34.jpg │ │ ├── F35.JPG │ │ ├── F36.JPG │ │ ├── F37.JPG │ │ ├── F38.JPG │ │ ├── F39.JPG │ │ ├── F4.jpg │ │ ├── F40.JPG │ │ ├── F41.JPG │ │ ├── F42.JPG │ │ ├── F43.JPG │ │ ├── F44.JPG │ │ ├── F45.JPG │ │ ├── F46.JPG │ │ ├── F47.JPG │ │ ├── F48.JPG │ │ ├── F49.JPG │ │ ├── F5.JPG │ │ ├── F50.JPG │ │ ├── F51.JPG │ │ ├── F52.JPG │ │ ├── F53.JPG │ │ ├── F54.JPG │ │ ├── F55.JPG │ │ ├── F56.JPG │ │ ├── F57.jpeg │ │ ├── F58.jpg │ │ ├── F59.JPG │ │ ├── F6.JPG │ │ ├── F60.JPG │ │ ├── F61.JPG │ │ ├── F62.jpg │ │ ├── F63.JPG │ │ ├── F64.JPG │ │ ├── F65.jpg │ │ ├── F66.JPG │ │ ├── F67.JPG │ │ ├── F68.jpg │ │ ├── F69.jpg │ │ ├── F7.JPG │ │ ├── F70.jpg │ │ ├── F71.JPG │ │ ├── F72.jpg │ │ ├── F73.JPG │ │ ├── F74.JPG │ │ ├── F75.jpg │ │ ├── F76.JPG │ │ ├── F77.JPG │ │ ├── F78.jpg │ │ ├── F79.JPG │ │ ├── F8.JPG │ │ ├── F80.jpg │ │ ├── F81.jpg │ │ ├── F82.JPG │ │ ├── F83.JPG │ │ ├── F84.JPG │ │ ├── F85.JPG │ │ ├── F86.jpg │ │ ├── F87.jpg │ │ ├── F88.jpg │ │ ├── F89.jpg │ │ ├── F9.JPG │ │ ├── F90.jpg │ │ ├── F91.jpg │ │ ├── F92.jpg │ │ ├── F93.jpg │ │ ├── F94.jpg │ │ ├── F95.jpg │ │ ├── F96.jpg │ │ ├── F97.jpg │ │ ├── F98.jpg │ │ ├── F99.JPG │ │ └── new.jpg │ └── resized │ │ ├── F1.JPG │ │ ├── F10.JPG │ │ ├── F100.JPG │ │ ├── F11.JPG │ │ ├── F12.jpg │ │ ├── F13.jpg │ │ ├── F14.jpg │ │ ├── F15.jpg │ │ ├── F16.jpg │ │ ├── F17.jpg │ │ ├── F18.jpg │ │ ├── F19.jpg │ │ ├── F2.JPG │ │ ├── F20.jpg │ │ ├── F21.jpg │ │ ├── F22.jpg │ │ ├── F23.jpg │ │ ├── F24.jpg │ │ ├── F25.jpg │ │ ├── F26.jpg │ │ ├── F27.jpg │ │ ├── F28.jpg │ │ ├── F29.jpg │ │ ├── F3.jpg │ │ ├── F30.jpg │ │ ├── F31.jpg │ │ ├── F32.jpg │ │ ├── F33.jpg │ │ ├── F34.jpg │ │ ├── F35.JPG │ │ ├── F36.JPG │ │ ├── F37.JPG │ │ ├── F38.JPG │ │ ├── F39.JPG │ │ ├── F4.jpg │ │ ├── F40.JPG │ │ ├── F41.JPG │ │ ├── F42.JPG │ │ ├── F43.JPG │ │ ├── F44.JPG │ │ ├── F45.JPG │ │ ├── F46.JPG │ │ ├── F47.JPG │ │ ├── F48.JPG │ │ ├── F49.JPG │ │ ├── F5.JPG │ │ ├── F50.JPG │ │ ├── F51.JPG │ │ ├── F52.JPG │ │ ├── F53.JPG │ │ ├── F54.JPG │ │ ├── F55.JPG │ │ ├── F56.JPG │ │ ├── F57.jpeg │ │ ├── F58.jpg │ │ ├── F59.JPG │ │ ├── F6.JPG │ │ ├── F60.JPG │ │ ├── F61.JPG │ │ ├── F62.jpg │ │ ├── F63.JPG │ │ ├── F64.JPG │ │ ├── F65.jpg │ │ ├── F66.JPG │ │ ├── F67.JPG │ │ ├── F68.jpg │ │ ├── F69.jpg │ │ ├── F7.JPG │ │ ├── F70.jpg │ │ ├── F71.JPG │ │ ├── F72.jpg │ │ ├── F73.JPG │ │ ├── F74.JPG │ │ ├── F75.jpg │ │ ├── F76.JPG │ │ ├── F77.JPG │ │ ├── F78.jpg │ │ ├── F79.JPG │ │ ├── F8.JPG │ │ ├── F80.jpg │ │ ├── F81.jpg │ │ ├── F82.JPG │ │ ├── F83.JPG │ │ ├── F84.JPG │ │ ├── F85.JPG │ │ ├── F86.jpg │ │ ├── F87.jpg │ │ ├── F88.jpg │ │ ├── F89.jpg │ │ ├── F9.JPG │ │ ├── F90.jpg │ │ ├── F91.jpg │ │ ├── F92.jpg │ │ ├── F93.jpg │ │ ├── F94.jpg │ │ ├── F95.jpg │ │ ├── F96.jpg │ │ ├── F97.jpg │ │ ├── F98.jpg │ │ ├── F99.JPG │ │ ├── H1.jpg │ │ ├── H10.jpg │ │ ├── H11.jpg │ │ ├── H12.jpg │ │ ├── H13.jpg │ │ ├── H14.jpg │ │ ├── H15.jpg │ │ ├── H16.jpg │ │ ├── H17.jpg │ │ ├── H18.jpg │ │ ├── H19.jpg │ │ ├── H2.jpg │ │ ├── H20.jpg │ │ ├── H21.jpg │ │ ├── H22.jpg │ │ ├── H23.jpg │ │ ├── H24.jpg │ │ ├── H25.jpg │ │ ├── H26.jpg │ │ ├── H27.jpg │ │ ├── H28.jpg │ │ ├── H29.jpg │ │ ├── H3.jpg │ │ ├── H30.jpg │ │ ├── H31.jpg │ │ ├── H32.jpg │ │ ├── H33.jpg │ │ ├── H34.jpg │ │ ├── H35.jpg │ │ ├── H36.jpg │ │ ├── H37.jpg │ │ ├── H38.jpg │ │ ├── H39.jpg │ │ ├── H4.jpg │ │ ├── H40.jpg │ │ ├── H41.jpg │ │ ├── H42.jpg │ │ ├── H43.jpg │ │ ├── H44.jpg │ │ ├── H45.jpg │ │ ├── H46.jpg │ │ ├── H47.jpg │ │ ├── H48.jpg │ │ ├── H49.jpg │ │ ├── H5.jpg │ │ ├── H50.jpg │ │ ├── H6.jpg │ │ ├── H7.jpg │ │ ├── H8.jpg │ │ ├── H9.jpg │ │ └── new.jpg ├── lr_model ├── main.py ├── pre_process.py ├── rfc_model ├── ridge_model └── train_label.py └── requirements.txt /.gitignore: -------------------------------------------------------------------------------- 1 | __pycache__ -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2019 Anand Kumar 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Bone-Fracture-Detection- 2 | 3rd Year college Project 3 | 4 | ### Note: 5 | Manual folder contains temporary work which detects the location of fracture in a given x-ray image manually(not totally, tho;) 6 | 7 | ### How to run: 8 | * Clone the repository: `git clone https://github.com/anandnet/Bone-Fracture-Detection.git` 9 | * Head to the root folder: `cd Bone-Fracture-Detection` 10 | * Install requirements: `pip install -r requirements.txt` 11 | * Head to the `manual` folder: `cd manual` 12 | * Run `main.py`: `python main.py` 13 | 14 | ### 15 | If you want to test on different images, alter the corresponding variables inside `main.py` 16 | -------------------------------------------------------------------------------- /manual/images/01.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/anandnet/Bone-Fracture-Detection/7bd1233b2de24c8cf1a9752d47c0b076be95309b/manual/images/01.jpg -------------------------------------------------------------------------------- /manual/images/02.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/anandnet/Bone-Fracture-Detection/7bd1233b2de24c8cf1a9752d47c0b076be95309b/manual/images/02.jpg -------------------------------------------------------------------------------- /manual/images/03.png: -------------------------------------------------------------------------------- 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image file 10 | to be available in both the folders. 11 | 12 | All names starting with F{n} are available in both the folders. 1<= n <=100 13 | """ 14 | 15 | model_name= "ridge_model" 16 | 17 | 18 | 19 | img_file= 'images/resized/{}'.format(img_name) 20 | orig_img= 'images/Fractured Bone/{}'.format(img_name) 21 | 22 | #for image read 23 | try: 24 | img_t=cv2.imread(img_file+".jpg",cv2.IMREAD_COLOR) 25 | img=cv2.imread(orig_img+".jpg",cv2.IMREAD_COLOR) 26 | shape= img.shape 27 | except (AttributeError,FileNotFoundError): 28 | try: 29 | img_t=cv2.imread(img_file+".JPG",cv2.IMREAD_COLOR) 30 | img=cv2.imread(orig_img+".JPG",cv2.IMREAD_COLOR) 31 | shape=img.shape 32 | except (AttributeError,FileNotFoundError): 33 | img_t=cv2.imread(img_file+".png",cv2.IMREAD_COLOR) 34 | img=cv2.imread(orig_img+".png",cv2.IMREAD_COLOR) 35 | shape=img.shape 36 | 37 | #else: raise FileNotFoundError("No image file {img_file}.jpg or {img_file}.JPG".format(img_file=img_file)) 38 | #else: 39 | # raise FileNotFoundError("No image file {img_file}.jpg or {img_file}.JPG".format(img_file=img_file)) 40 | 41 | 42 | #details of Imge 43 | print("\nShape: ",shape) 44 | print("\nSize: ",img.size) 45 | print("\nDType: ",img.dtype) 46 | 47 | #==============Manual edge ditect===================== 48 | def segment_img(_img,limit): 49 | for i in range(0,_img.shape[0]-1): 50 | for j in range(0,_img.shape[1]-1): 51 | if int(_img[i,j+1])-int(_img[i,j])>=limit: 52 | _img[i,j]=0 53 | elif(int(_img[i,j-1])-int(_img[i,j])>=limit): 54 | _img[i,j]=0 55 | 56 | return _img 57 | #====================================================== 58 | 59 | gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) 60 | 61 | #for i in range(0,gray.shape[0]): 62 | # for j in range(0,gray.shape[1]): 63 | # if (int(gray[i,j]))<=100: 64 | # gray[i,j]=100 65 | 66 | #gray=segment_img(gray,15) 67 | cv2.imshow("GrayEdited",gray) 68 | median = cv2.medianBlur(gray,5) 69 | 70 | model= get_model(model_name) 71 | pred_thresh= model.predict([_reshape_img(img_t)]) 72 | bool,threshold_img=cv2.threshold(median,int(pred_thresh),255,cv2.THRESH_BINARY) 73 | #blur=cv2.GaussianBlur(threshold_img,(7,7),0) 74 | cv2.imshow("threshold",threshold_img) 75 | 76 | 77 | initial=[] 78 | final=[] 79 | line=[] 80 | #count=[] 81 | #for i in range(0,256): 82 | # count.append(0) 83 | 84 | for i in range(0,gray.shape[0]): 85 | tmp_initial=[] 86 | tmp_final=[] 87 | for j in range(0,gray.shape[1]-1): 88 | #count[gray[i,j]]+=1 89 | if threshold_img[i,j]==0 and (threshold_img[i,j+1])==255: 90 | tmp_initial.append((i,j)) 91 | #img[i,j]=[255,0,0] 92 | if threshold_img[i,j]==255 and (threshold_img[i,j+1])==0: 93 | tmp_final.append((i,j)) 94 | #img[i,j]=[255,0,0] 95 | 96 | x= [each for each in zip(tmp_initial,tmp_final)] 97 | x.sort(key= lambda each: each[1][1]-each[0][1]) 98 | try: 99 | line.append(x[len(x)-1]) 100 | except IndexError: pass 101 | 102 | #print(count) 103 | 104 | 105 | err= 15 106 | danger_points=[] 107 | 108 | #store distances 109 | dist_list=[] 110 | 111 | for i in range(1,len(line)-1): 112 | dist_list.append(line[i][1][1]-line[i][0][1]) 113 | try: 114 | prev_= line[i-3] 115 | next_= line[i+3] 116 | 117 | dist_prev= prev_[1][1]-prev_[0][1] 118 | dist_next= next_[1][1]-next_[0][1] 119 | diff= abs(dist_next-dist_prev) 120 | if diff>err: 121 | #print("Dist: {}".format(abs(dist_next-dist_prev))) 122 | #print(line[i]) 123 | data=(diff, line[i]) 124 | #print(data) 125 | if len(danger_points): 126 | prev_data=danger_points[len(danger_points)-1] 127 | #print(prev_data) 128 | #print("here1....") 129 | if abs(prev_data[0]-data[0])>2 or data[1][0]-prev_data[1][0]!=1: 130 | #print("here2....") 131 | print(data) 132 | danger_points.append(data) 133 | else: 134 | print(data) 135 | danger_points.append(data) 136 | except Exception as e: 137 | print(e) 138 | pass 139 | 140 | #print(each) 141 | start,end= line[i] 142 | #raise ZeroDivisionError 143 | mid=int((start[0]+end[0])/2),int((start[1]+end[1])/2) 144 | #img[mid[0],mid[1]]=[0,0,255] 145 | 146 | for i in range(0,len(danger_points)-1,2): 147 | try: 148 | start_rect=danger_points[i][1][0][::-1] 149 | start_rect=(start_rect[0]-40, start_rect[1]-40) 150 | 151 | end_rect= danger_points[i+1][1][1][::-1] 152 | end_rect= (end_rect[0]+40, end_rect[1]+40) 153 | 154 | cv2.rectangle(img,start_rect,end_rect,(0,255,0),2) 155 | except: 156 | print("Pair not found") 157 | 158 | #blur= cv2.GaussianBlur(img,(5,5),0) 159 | 160 | import matplotlib.pyplot as plt 161 | import numpy as np 162 | 163 | fig, (ax1, ax2)= plt.subplots(2,1) 164 | 165 | fig2, ax3= plt.subplots(1,1) 166 | 167 | x= np.arange(1,gray.shape[0]-1) 168 | y= dist_list 169 | 170 | #print(len(x),len(y)) 171 | 172 | cv2.calcHist(gray,[0],None,[256],[0,256]) 173 | 174 | try: 175 | ax1.plot(x,y) 176 | except: 177 | print("Could not plot") 178 | img= np.rot90(img) 179 | ax2.imshow(img) 180 | 181 | #count= range(256) 182 | #ax3.hist(count, 255, weights=count, range=[0,256]) 183 | ax3.hist(gray.ravel(),256,[0,256]) 184 | 185 | plt.show() 186 | 187 | 188 | 189 | #wait for key pressing 190 | cv2.waitKey(0) 191 | 192 | #Distroy all the cv windows 193 | cv2.destroyAllWindows() 194 | -------------------------------------------------------------------------------- /manual/pre_process.py: -------------------------------------------------------------------------------- 1 | from PIL import Image 2 | import cv2 3 | import numpy as np 4 | import sys 5 | from sklearn.linear_model import LinearRegression, LogisticRegression, Ridge 6 | from sklearn.neighbors import KNeighborsRegressor 7 | from sklearn.tree import DecisionTreeRegressor 8 | from sklearn.ensemble import RandomForestClassifier 9 | import pickle 10 | 11 | WIDTH = 310/2 12 | HEIGHT = 568/2 13 | 14 | size = (int(WIDTH), int(HEIGHT)) 15 | 16 | def resize_and_save(img_name): 17 | #try: 18 | main_image = Image.open("images/Fractured Bone/{}".format(img_name)) 19 | #except IOError: 20 | #sys.stderr.write("ERROR: Could not open file {}\n".format(img_name)) 21 | #return 22 | #exit(1) 23 | 24 | #main_image.thumbnail(size, Image.ANTIALIAS) 25 | x= main_image.resize(size, Image.NEAREST) 26 | x.save("images/resized/{}".format(img_name)) 27 | 28 | def _reshape_img(arr): 29 | #reshape a numpy image and returns a 1-D array 30 | flat_arr=[] 31 | for i in range(arr.shape[0]): 32 | for j in range(arr.shape[1]): 33 | for k in range(arr.shape[2]): 34 | flat_arr.append(arr[i][j][k]) 35 | 36 | return flat_arr 37 | 38 | def _create_data(train_img_list, label_list): 39 | """ 40 | param train_img_list: A list containg images to train model upon 41 | param label_list: A list containing labels corresponding to images 42 | in the train_img_list 43 | 44 | returns inp_arr: a ready-to-feed numpy array for training 45 | returns np.array(label_list): a numpy array of corresponding label_list 46 | """ 47 | inp_arr=[] 48 | 49 | for img in train_img_list: 50 | img= cv2.imread(img) 51 | inp_arr.append(_reshape_img(img)) 52 | 53 | inp_arr= np.array(inp_arr) 54 | 55 | return inp_arr,np.array(label_list) 56 | 57 | 58 | def train_and_save(train_img_list, label_list, model_name): 59 | try: 60 | with open(model_name,"rb") as file: 61 | model= pickle.load(file) 62 | except FileNotFoundError: 63 | in_arr, out_arr= _create_data(train_img_list, label_list) 64 | 65 | #model= LogisticRegression(random_state=43,tol=1e-5,max_iter=5000,verbose=1).fit(in_arr,out_arr) 66 | #model = RandomForestClassifier(n_estimators=10, random_state=42).fit(in_arr, out_arr) 67 | model= Ridge(alpha=0.01,tol=0.000001,max_iter=5000,random_state=43).fit(in_arr,out_arr) 68 | 69 | with open(model_name,"wb") as file: 70 | pickle.dump(model,file) 71 | 72 | return model 73 | 74 | def get_model(model_name): 75 | try: 76 | with open(model_name,"rb") as file: 77 | model= pickle.load(file) 78 | return model 79 | except FileNotFoundError: 80 | print("{} doesn't exist. Train and save a model first".format(model_name)) 81 | sys.exit(0) 82 | 83 | if __name__=="__main__": 84 | for each in range(1,101): 85 | try: 86 | resize_and_save("F{}.JPG".format(each)) 87 | except IOError: 88 | try: 89 | resize_and_save("F{}.jpg".format(each)) 90 | except IOError: 91 | resize_and_save("F{}.jpeg".format(each)) 92 | 93 | 94 | from train_label import train_label, test_label 95 | 96 | train_img_list=[] 97 | train_label_list=[] 98 | 99 | for key in train_label.keys(): 100 | train_img_list.append("images/resized/"+key+".jpg") 101 | train_label_list.append(train_label[key]) 102 | 103 | test_img_list=[] 104 | test_label_list=[] 105 | for key in test_label.keys(): 106 | test_img_list.append("images/resized/"+key+".jpg") 107 | test_label_list.append(test_label[key]) 108 | 109 | #in_arr, out_arr= _create_data(train_img_list,label_list) 110 | #print(in_arr.shape) 111 | print("Training started...") 112 | svm_model=train_and_save(train_img_list,train_label_list, "ridge_model") 113 | print("Training finished...") 114 | 115 | train_in_arr, train_out_arr= _create_data(train_img_list,train_label_list) 116 | test_in_arr, test_out_arr= _create_data(test_img_list,test_label_list) 117 | 118 | print("Training set score: {:.2f}".format(svm_model.score(train_in_arr, train_out_arr))) 119 | print("Test set score: {:.2f}".format(svm_model.score(test_in_arr, test_out_arr))) -------------------------------------------------------------------------------- /manual/rfc_model: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/anandnet/Bone-Fracture-Detection/7bd1233b2de24c8cf1a9752d47c0b076be95309b/manual/rfc_model -------------------------------------------------------------------------------- /manual/ridge_model: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/anandnet/Bone-Fracture-Detection/7bd1233b2de24c8cf1a9752d47c0b076be95309b/manual/ridge_model -------------------------------------------------------------------------------- /manual/train_label.py: -------------------------------------------------------------------------------- 1 | #contains labels 2 | 3 | train_label= { 4 | "H1":115, 5 | "H2":82, 6 | "H4":184, 7 | "H6":186, 8 | "H9":123, 9 | "H10":141, 10 | "H11":139, 11 | "H14":120, 12 | "H17":163, 13 | "H18":113, 14 | "H19":126, 15 | "H20":152, 16 | "H23":141, 17 | "H24":104, 18 | "H25":45, 19 | "H26":48, 20 | "H27":142, 21 | "H31":119, 22 | "H32":138, 23 | "H35":95, 24 | "H36":116, 25 | "H39":123, 26 | "H40":97, 27 | "H43":148, 28 | "H48":147, 29 | "H49":132, 30 | "H50":119, 31 | } 32 | test_label={ 33 | "1":135, 34 | "2":135, 35 | "4":150, 36 | "6":130, 37 | "7":112, 38 | "8":100, 39 | "9":160, 40 | "10":60, 41 | "11":132, 42 | "13":95 43 | } -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | opencv-python 2 | sklearn 3 | pillow --------------------------------------------------------------------------------