Sweep: Refactor the python scripts in 'packages' folder #9

yihong1120 · 2024-01-05T07:59:28Z

Details

Blur.py
InRange.py
Morphology.py
Sobel.py
Threshold.py
TimeTransition.py
TimeTransition_origine.py
init.py
classify_inundation.py
classify_inundation_origine.py
classify_rain.py
classify_rain_origine.py
coco.py
comparer.py
convex_hull.py
couleur_transparent.py
crosswalk.py
crosswalk_origine.py
database.py
del_hort.py
del_vert.py
draw_polygon.py
edge.py
gray_images.py
ground.py
img_adjust.py
img_adjust_tous_points.py
img_adjust_tous_points_origine.py
inrange_blanc.py
inrange_blanc_origine.py
mesh2depth.py
mesh2depth_origine.py
minAreaRect_cv2.py
minAreaRect_cv2_origine.py
mix_image.py
pixel2mesh.py
pixel2mesh_origine.py
pixel_colour.py
setup.py
utils.py
voiture.py
voiture_origine.py
water.py
zone_inondee.py
zone_inondee_origine.py

refactor, integrate the files above, these files are in CCTV-Inundation-Detection/packages.

Checklist

Modify packages/Blur.py ✓ 0351d96 Edit
Running GitHub Actions for packages/Blur.py ✓ Edit
Modify packages/InRange.py ✓ 3303741 Edit
Running GitHub Actions for packages/InRange.py ✓ Edit
Modify packages/Morphology.py ✓ 581f789 Edit
Running GitHub Actions for packages/Morphology.py ✓ Edit

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sweep-ai · 2024-01-05T07:59:32Z

🚀 Here's the PR! #10

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CCTV-Inundation-Detection/packages/Blur.py

Lines 4 to 18 in 570845e

    
           # In[12]: 
        
           import numpy 
        
           #import argparse 
        
           import cv2 
        
           def blur(br1,br2,blurry,Input,Output): 
        
               image = cv2.imread(Input) 
        
               #result = np.hstack([canny]) 
        
               #ret,thresh1 = cv2.threshold(image,200,255,cv2.THRESH_BINARY) 
        
               blurred = cv2.GaussianBlur(image, (br1, br2 ), blurry) 
        
               cv2.imwrite(Output, blurred) 
        
               return 
        
           In="/Users/YiHung/Downloads/0002_sobel.png" 
        
           Out="/Users/YiHung/Downloads/0002_sobel_blur.png"

CCTV-Inundation-Detection/packages/InRange.py

Lines 4 to 55 in 570845e

    
           # In[12]: 
        
           import numpy as np 
        
           from PIL import Image 
        
           import cv2 
        
           def InRange(hp,sp,vp,ha,sa,va,INPUT): 
        
               img = cv2.imread(INPUT)#overlay 
        
               # OpenCV的顏色預設是BGR格式，這邊將其轉換為HSV格式 
        
               hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) 
        
               # 以HSV格式決定要提取的顏色範圍，顏色格式的說明請參考後續內容 
        
               lower = np.array([hp,sp,vp]) #0,0,0 
        
               upper = np.array([ha,sa,va]) #0,0,26 
        
               # 將HSV影像的閾值設定為想要提取的顏色 
        
               mask = cv2.inRange(hsv, lower, upper) 
        
               # 使用bitwise_and()合併掩膜(mask)和原來的影像 
        
               #img_specific = cv2.bitwise_and(img,img, mask= mask) 
        
               #result = cv2.bitwise_and(image, image, mask) 
        
               # 展示原圖、掩膜、抽取顏色後的影像 
        
               cv2.imwrite(INPUT, mask) 
        
               #os.remove(base_path_overlay+str(min_in_file(base_path_overlay))+".png") 
        
               ''' 
        
               # 输入文件 
        
               img = Image.open(INPUT) 
        
               width = img.size[0]#长度 
        
               height = img.size[1]#宽度 
        
               img = img.convert("RGB") 
        
               for i in range(img.width): 
        
                   for j in range(img.height): 
        
                       r,g,b = img.getpixel((i,j)) 
        
                       if(r == 0 and g == 0 and b== 0): 
        
                           r=65 
        
                           g=105 
        
                           b=225 
        
                       img.putpixel((i,j), (r,g,b)) 
        
               # 输出图片 
        
               img.save(INPUT) 
        
               #os.remove(base_path_InRange+str(min_in_file(base_path_InRange))+".png") 
        
               ''' 
        
               src = cv2.imread(INPUT) 
        
               tmp = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY) 
        
               _,alpha = cv2.threshold(tmp,254,255,cv2.THRESH_BINARY_INV) #THRESH_BINARY_INV 
        
               b, g, r = cv2.split(src) 
        
               rgba = [b,g,r,alpha] 
        
               #b,g,r, 
        
               dst = cv2.merge(rgba,4) 
        
               cv2.imwrite(INPUT, dst) 
        
               #os.remove(base_path_couleurs_bleu+str(min_in_file(base_path_couleurs_bleu))+".png") 
        
               return 
        
           if __name__=="__main__": 
        
               input="./05_test.png"

CCTV-Inundation-Detection/packages/Morphology.py

Lines 4 to 25 in 570845e

    
           # In[12]: 
        
           import cv2 
        
           import numpy as np 
        
           def morphology(input,output): 
        
               img = cv2.imread(input) 
        
               ''' 
        
               # 2. cv2.MORPH_OPEN 先进行腐蚀操作，再进行膨胀操作 
        
               kernel = np.ones((5, 5), np.uint8) 
        
               opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel) 
        
               #cv2.imshow('opening', opening) 
        
               ''' 
        
               # 3. cv2.MORPH_CLOSE 先进行膨胀，再进行腐蚀操作 
        
               kernel = np.ones((3, 3), np.uint8) 
        
               closing = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel) 
        
               #cv2.imshow('closing', closing) 
        
               cv2.imwrite(output, closing) 
        
               return 
        
           ''' 
        
           input="/Users/YiHung/Downloads/0002_sobel_blur_threshold.png" 
        
           output="/Users/YiHung/Downloads/0002_sobel_blur_threshold_morphologCL.png"

CCTV-Inundation-Detection/packages/Sobel.py

Lines 4 to 18 in 570845e

    
           # In[12]: 
        
           import cv2 
        
           def sobel(input,output): 
        
               image = cv2.imread(input) 
        
               gradX = cv2.Sobel(image, ddepth=cv2.CV_64F, dx=1, dy=0, ksize=3) 
        
               gradY = cv2.Sobel(image, ddepth=cv2.CV_64F, dx=0, dy=1, ksize=3) 
        
               # subtract the y-gradient from the x-gradient 
        
               gradient = cv2.subtract(gradX, gradY) 
        
               gradient = cv2.convertScaleAbs(gradient) 
        
               cv2.imwrite(output, gradient) 
        
               return 
        
           ''' 
        
           input="/Users/YiHung/Downloads/0002_gcom.png" 
        
           output="/Users/YiHung/Downloads/0006_sobel.png"

CCTV-Inundation-Detection/packages/Threshold.py

Lines 4 to 14 in 570845e

    
           # In[12]: 
        
           import cv2 
        
           def threshold(threshold,grayscale_degree,input,output): 
        
               img = cv2.imread(input) 
        
               ret,threshold = cv2.threshold(img,threshold,grayscale_degree,cv2.THRESH_BINARY) 
        
               cv2.imwrite(output, threshold) 
        
               return 
        
           ''' 
        
           input="/Users/YiHung/Downloads/0002_sobel_blur.png" 
        
           output="/Users/YiHung/Downloads/0002_sobel_blur_threshold.png" 
        
           threshold(60,255,input,output)

CCTV-Inundation-Detection/packages/TimeTransition.py

Lines 1 to 50 in 570845e

    
           import time # 引入time 
        
           import os 
        
           ''' 
        
           timeString = "2020-08-10_15h30" # 時間格式為字串 
        
           struct_time = time.strptime(timeString, "%Y-%m-%d_%Hh%M") # 轉成時間元組 
        
           time_stamp = int(time.mktime(struct_time)) # 轉成時間戳 
        
           print(time_stamp) 
        
           ''' 
        
           def JPGtoPNG(): 
        
               for file in os.listdir("./"+file_nom):    
        
                   struct_time = time.strptime(file[:-4], "%Y-%m-%d_%Hh%M") # 轉成時間元組 
        
                   time_stamp = int(time.mktime(struct_time)) # 轉成時間戳 
        
                   os.rename("./"+file_nom+"/"+file,"./"+file_nom+"/"+str(time_stamp)+".png") 
        
                   print(file[:-4]+"->"+str(time_stamp)) 
        
               return 
        
           def TimeToTimestamps(Time): 
        
               struct_time = time.strptime(Time, "%Y-%m-%d_%Hh%M") # 轉成時間元組 
        
               time_stamp = int(time.mktime(struct_time)) # 轉成時間戳 
        
               #print(time_stamp) 
        
               return time_stamp 
        
           def TimestampsToTime(Timestamp): 
        
               t = time.localtime(int(Timestamp)) 
        
               timeStr = time.strftime("%Y-%m-%d_%Hh%M", t) 
        
               return timeStr 
        
           if __name__ == "__main__": 
        
               #file_nom=input("資料夾名稱：") 
        
               #JPGtoPNG() 
        
               #1596990600 
        
               ''' 
        
               timeString = "2020-08-10_00h30" # 時間格式為字串 
        
               print(TimeToTimestamps(timeString)) 
        
               ''' 
        
               Timestamp=1596990600 
        
               print(TimestampsToTime(Timestamp)) 
        
               ''' 
        
               timestamp=1596990600 
        
               #转换成localtime 
        
               time_local = time.localtime(timestamp) 
        
               #转换成新的时间格式(2016-05-05 20:28:54) 
        
               dt = time.strftime("%Y-%m-%d %H:%M:%S",time_local) 
        
               print(dt)

CCTV-Inundation-Detection/packages/TimeTransition_origine.py

Lines 1 to 50 in 570845e

    
           import time  
        
           import os 
        
           ''' 
        
           timeString = "2020-08-10_15h30" # 時間格式為字串 
        
           struct_time = time.strptime(timeString, "%Y-%m-%d_%Hh%M") # 轉成時間元組 
        
           time_stamp = int(time.mktime(struct_time)) # 轉成時間戳 
        
           print(time_stamp) 
        
           ''' 
        
           def JPGtoPNG(): 
        
               for file in os.listdir("./"+file_nom):    
        
                   struct_time = time.strptime(file[:-4], "%Y-%m-%d_%Hh%M") # 轉成時間元組 
        
                   time_stamp = int(time.mktime(struct_time)) # 轉成時間戳 
        
                   os.rename("./"+file_nom+"/"+file,"./"+file_nom+"/"+str(time_stamp)+".png") 
        
                   print(file[:-4]+"->"+str(time_stamp)) 
        
               return 
        
           def TimeToTimestamps(Time): 
        
               struct_time = time.strptime(Time, "%Y-%m-%d_%Hh%M") # 轉成時間元組 
        
               time_stamp = int(time.mktime(struct_time)) # 轉成時間戳 
        
               #print(time_stamp) 
        
               return time_stamp 
        
           def TimestampsToTime(Timestamp): 
        
               t = time.localtime(int(Timestamp)) 
        
               timeStr = time.strftime("%Y-%m-%d_%Hh%M", t) 
        
               return timeStr 
        
           if __name__ == "__main__": 
        
               #file_nom=input("資料夾名稱：") 
        
               #JPGtoPNG() 
        
               #1596990600 
        
               ''' 
        
               timeString = "2020-08-10_00h30" # 時間格式為字串 
        
               print(TimeToTimestamps(timeString)) 
        
               ''' 
        
               Timestamp=1596990600 
        
               print(TimestampsToTime(Timestamp)) 
        
               ''' 
        
               timestamp=1596990600 
        
               #转换成localtime 
        
               time_local = time.localtime(timestamp) 
        
               #转换成新的时间格式(2016-05-05 20:28:54) 
        
               dt = time.strftime("%Y-%m-%d %H:%M:%S",time_local) 
        
               print(dt)

CCTV-Inundation-Detection/packages/classify_inundation.py

Lines 1 to 46 in 570845e

    
           import os 
        
           import numpy as np 
        
           from PIL import Image 
        
           import cv2 
        
           from model_cnn import efficientnet_b3 as create_model 
        
           model_weights_path = './Mask_RCNN/inundation_b3/efficientnet.ckpt' 
        
           def load_model(): 
        
               model = create_model(num_classes=2) 
        
               model.load_weights(model_weights_path) 
        
               return model 
        
           model = load_model() 
        
           def classify_image(img_path, output_path, include_date): 
        
               img_size = 300 
        
               img = Image.open(img_path).convert('RGB').resize((img_size, img_size)) 
        
               img_array = np.expand_dims(np.array(img).astype(np.float32), 0) 
        
               result = np.squeeze(model.predict(img_array)) 
        
               out_percent = round(float(result[0]) * 100, 2) 
        
               label = 'inundation' 
        
               print(f"{label}: {out_percent}%") 
        
               img_cv2 = cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR) 
        
               img_height, img_width = img_cv2.shape[:2] 
        
               font_scale = img_height / 240 * 0.7 
        
               text_y = int(75 / 240 * img_height) 
        
               text_height = int(20 / 240 * img_height) 
        
               text_width = int(220 / 320 * img_width) 
        
               color = (0, 255, 0) if out_percent < 50 else (255, 0, 0) 
        
               if include_date: 
        
                   cv2.rectangle(img_cv2, (8, text_y + 2), (8 + text_width, text_y - text_height + 2), (255, 255, 255), -1) 
        
                   cv2.putText(img_cv2, f"{label}: {out_percent}%", (10, text_y), cv2.FONT_HERSHEY_SIMPLEX, font_scale, color, 2) 
        
               else: 
        
                   text_y = text_y * 2 // 3 
        
                   cv2.rectangle(img_cv2, (8, text_y + 2), (8 + text_width, text_y - text_height + 2), (255, 255, 255), -1) 
        
                   cv2.putText(img_cv2, f"{label}: {out_percent}%", (10, text_y), cv2.FONT_HERSHEY_SIMPLEX, font_scale, color, 2) 
        
               cv2.imwrite(output_path, img_cv2)

CCTV-Inundation-Detection/packages/classify_inundation_origine.py

Lines 1 to 109 in 570845e

    
           from tensorflow.compat.v1 import reset_default_graph,get_default_graph,disable_eager_execution 
        
           #import tensorflow as tf 
        
           import os 
        
           import json 
        
           import glob 
        
           import numpy as np 
        
           import time 
        
           from PIL import Image 
        
           from PIL import ImageFile 
        
           ImageFile.LOAD_TRUNCATED_IMAGES = True 
        
           import gc 
        
           import cv2 
        
           import sys 
        
           sys.path.append('./') 
        
           from model_cnn import efficientnet_b3 as create_model 
        
           disable_eager_execution() 
        
           # create graph 
        
           global inundation_graph 
        
           inundation_graph = get_default_graph() 
        
           # create model 
        
           #inundation_model=tf.keras.models.load_model('./Mask_RCNN/inundation_model.h5') 
        
           inundation_model = create_model(num_classes=2) 
        
           inundation_weights_path = './Mask_RCNN/inundation_b3/efficientnet.ckpt' 
        
           inundation_model.load_weights(inundation_weights_path) 
        
           def classify(img_path,output_route,date_de_la_photo): 
        
               img_size = {"B0": 224, 
        
                           "B1": 240, 
        
                           "B2": 260, 
        
                           "B3": 300, 
        
                           "B4": 380, 
        
                           "B5": 456, 
        
                           "B6": 528, 
        
                           "B7": 600} 
        
               num_model = "B3" 
        
               im_height = im_width = img_size[num_model] 
        
               # load image 
        
               img = Image.open(img_path).convert('RGB') 
        
               img_cv2 = cv2.cvtColor(np.asarray(img),cv2.COLOR_RGB2BGR) 
        
               # resize image 
        
               img = img.resize((im_width, im_height)) 
        
               # read image 
        
               img = np.array(img).astype(np.float32) 
        
               # Add the image to a batch where it's the only member. 
        
               img = (np.expand_dims(img, 0)) 
        
               reset_default_graph() 
        
               with inundation_graph.as_default(): 
        
                   result = np.squeeze(inundation_model.predict(img)) 
        
               out_percent=round(float(result[0]) * 100,2) 
        
               #print(out_percent) 
        
               print_res = "{}: {}%".format('inundation', out_percent)#:.2f 
        
               print(print_res) 
        
               label='inundation' 
        
               #positionner des mots 
        
               ###pic_height=img_cv2.shape[0] 
        
               pic_height=240 
        
               ###pic_width=img_cv2.shape[1] 
        
               pic_width=320 
        
               if pic_height>240: 
        
                   zoom_taille=0.7/240*pic_height 
        
                   positionner_inondation=int(75/240*pic_height) 
        
                   height_mot=20/240*pic_height 
        
                   width_mot=220/320*pic_width 
        
               elif pic_height<=240: 
        
                   zoom_taille=0.7 
        
                   positionner_inondation=75 
        
                   height_mot=20 
        
                   width_mot=220 
        
               #couleurs des mots 
        
               if out_percent>=50: 
        
                   r,g,b=255,0,0 
        
               elif out_percent<50: 
        
                   r,g,b=0,255,0 
        
               #mettre les mots 
        
               if date_de_la_photo=="y": 
        
                   cv2.rectangle(img_cv2, (8, positionner_inondation+2), (8+width_mot, positionner_inondation-height_mot+2 ), (255, 255, 255), -1) 
        
                   cv2.putText(img_cv2, print_res, (10, positionner_inondation),  cv2.FONT_HERSHEY_SIMPLEX, 
        
                       zoom_taille, (b, g, r), 2) 
        
               else: 
        
                   positionner_inondation==positionner_inondation*2//3 
        
                   cv2.rectangle(img_cv2, (8, positionner_inondation+2), (8+width_mot, positionner_inondation-height_mot+2 ), (255, 255, 255), -1) 
        
                   cv2.putText(img_cv2, print_res, (10, positionner_inondation),  cv2.FONT_HERSHEY_SIMPLEX, 
        
                       zoom_taille, (b, g, r), 2) 
        
               cv2.imwrite(output_route, img_cv2) 
        
               del img, img_cv2 
        
               get_default_graph().finalize() 
        
               return out_percent 
        
               gc.collect() 
        
           if __name__ == '__main__': 
        
               start=time.time() 
        
               img_path = './non_rain.png' 
        
               output_route='./test.png' 
        
               date_de_la_photo='n' 
        
               classify(img_path,output_route,date_de_la_photo) 
        
               end=time.time() 
        
               run_time=end-start 
        
               print(run_time)

CCTV-Inundation-Detection/packages/classify_rain.py

Lines 1 to 50 in 570845e

    
           import os 
        
           import cv2 
        
           import numpy as np 
        
           import tensorflow as tf 
        
           from PIL import Image 
        
           from PIL import ImageFile 
        
           ImageFile.LOAD_TRUNCATED_IMAGES = True 
        
           rain_model = tf.keras.models.load_model('Mask_RCNN/Rain_EfficientNet.h5') 
        
           def classify(img_path, output_route, date_de_la_photo, commener): 
        
               img_size = {"B3": 300} 
        
               im_height = im_width = img_size["B3"] 
        
               img = Image.open(img_path).convert('RGB') 
        
               img_cv2 = cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR) 
        
               img = img.resize((im_width, im_height)) 
        
               img = np.array(img).astype(np.float32) 
        
               img = np.expand_dims(img, 0) 
        
               out_percent = round(float(np.squeeze(rain_model.predict(img))[1]) * 100, 2) 
        
               if out_percent >= 50: 
        
                   r, g, b = 255, 0, 0 
        
               else: 
        
                   r, g, b = 0, 255, 0 
        
               pic_height = 240 
        
               pic_width = 320 
        
               positionner_temps = int(25 / 240 * pic_height) 
        
               zoom_taille = 0.7 
        
               positionner_pluie = int(50) 
        
               height_mot = 20 
        
               width_mot = 150 
        
               if date_de_la_photo == "y": 
        
                   positionner_pluie //= 2 
        
                   height_rectangle = positionner_temps - height_mot 
        
                   time_now = TimestampsToTime(os.path.splitext(os.path.basename(img_path))[0]) 
        
                   cv2.rectangle(img_cv2, (8, positionner_temps + 2), (8 + width_mot, positionner_temps - height_mot + 2), (255, 255, 255), -1) 
        
                   cv2.putText(img_cv2, time_now, (10, positionner_temps), cv2.FONT_HERSHEY_SIMPLEX, zoom_taille, (0, 0, 0), 2, cv2.LINE_AA) 
        
               cv2.rectangle(img_cv2, (8, positionner_pluie + 2), (8 + width_mot, positionner_pluie - height_mot + 2), (255, 255, 255), -1) 
        
               cv2.putText(img_cv2, f"rain: {out_percent:.2f}%", (10, positionner_pluie), cv2.FONT_HERSHEY_SIMPLEX, zoom_taille, (r, g, b), 2, cv2.LINE_AA) 
        
               cv2.imwrite(output_route, img_cv2)

CCTV-Inundation-Detection/packages/comparer.py

Lines 1 to 20 in 570845e

    
           #!/usr/bin/env python 
        
           # coding: utf-8 
        
           # In[12]: 
        
           from PIL import Image 
        
           from PIL import ImageChops 
        
           def compare(fichier1,fichier2,out_f): #file route1, file route2, output route 
        
               #comparer le photos 
        
               image_one = Image.open(fichier1) 
        
               image_two = Image.open(fichier2) 
        
               img1 = image_one.convert("RGB") 
        
               img2 = image_two.convert("RGB") 
        
               diff = ImageChops.difference(img1, img2) 
        
               diff.save(out_f) 
        
               return 
        
           ''' 
        
           input1="/Users/YiHung/Downloads/00.png" 
        
           input2="/Users/YiHung/Downloads/02.png" 
        
           output="/Users/YiHung/Downloads/0002_com.png" 
        
           compare(input1,input2,output)

CCTV-Inundation-Detection/packages/convex_hull.py

Lines 1 to 31 in 570845e

    
           import cv2 
        
           from PIL import Image 
        
           import numpy as np 
        
           def convex_hull(input, input_type = 'image', output = None): 
        
               if input_type == 'image': 
        
                   img = cv2.imread(input)#polygon 
        
               else: 
        
                   img = input 
        
               #cv2.imshow("img", img) 
        
               # 转换为灰度图像 
        
               img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 
        
               #img = Image.fromarray(cv2.cvtColor(img,cv2.COLOR_BGR2RGB)) 
        
               #img.save('./convex____2.png') 
        
               #img = cv2.cvtColor(np.asarray(img),cv2.COLOR_RGB2BGR) 
        
               ret, binary = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY) 
        
               contours, hierarchy = cv2.findContours(binary, 2, 1) 
        
               #contours, hierarchy = cv2.findContours(binary, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) 
        
               hull=cv2.convexHull(contours[0]) 
        
               #print(hull) 
        
               #cv2.polylines(img, [hull], True, (0, 255, 0), 2) 
        
               img=cv2.fillPoly(img, [hull], (255,255,255)) 
        
               if input_type == 'image': 
        
                   cv2.imwrite(output, img) 
        
               else: 
        
                   return img 
        
           if __name__=="__main__": 
        
               input='./14_0.png' 
        
               output='./14_0.png'

CCTV-Inundation-Detection/packages/couleur_transparent.py

Lines 1 to 18 in 570845e

    
           import PIL.Image as Image 
        
           def transparent_back(r, g, b, a, imgn): 
        
               img = imgn.convert('RGBA') 
        
               data = img.getdata() 
        
               new_data = [(r, g, b, 0) if pixel == (r, g, b, a) else pixel for pixel in data] 
        
               img.putdata(new_data) 
        
               return img 
        
           if __name__ == "__main__": 
        
               img_path = "./14.png" 
        
               output_path = "./14_out.png" 
        
               img = Image.open(img_path) 
        
               for i in range(3): 
        
                   img = transparent_back(0, 0, 0, 255, img) 
        
                   img.save(output_path)

CCTV-Inundation-Detection/packages/crosswalk.py

Lines 1 to 66 in 570845e

    
           import os 
        
           import sys 
        
           import numpy as np 
        
           import cv2 
        
           from PIL import Image 
        
           from tensorflow.compat.v1 import get_default_graph 
        
           sys.path.append(os.getcwd()) 
        
           from mrcnn.config_mrcnn import Config 
        
           from mrcnn import model_mrcnn as modellib 
        
           class ShapesConfig(Config): 
        
               NAME = "shapes" 
        
               GPU_COUNT = 1 
        
               IMAGES_PER_GPU = 1 
        
               NUM_CLASSES = 1 + 1 
        
               IMAGE_MIN_DIM = 800 
        
               IMAGE_MAX_DIM = 1024 
        
               RPN_ANCHOR_SCALES = (32, 64, 128, 256, 512) 
        
               TRAIN_ROIS_PER_IMAGE = 200 
        
               STEPS_PER_EPOCH = 1000 
        
               VALIDATION_STEPS = 50 
        
           COCO_MODEL_PATH = os.path.join(os.getcwd(), 'Mask_RCNN', 'crosswalk.h5') 
        
           MODEL_DIR = os.path.join(os.getcwd(),'Mask_RCNN','logs') 
        
           config = ShapesConfig() 
        
           model = modellib.MaskRCNN(mode="inference",  model_dir=MODEL_DIR, config=config) 
        
           model.load_weights(COCO_MODEL_PATH, by_name=True) 
        
           graph = get_default_graph() 
        
           class_names = ['BG', 'crosswalk'] 
        
           def apply_mask(image, mask): 
        
               image[:, :, 0] = np.where(mask == 0, 125, image[:, :, 0]) 
        
               image[:, :, 1] = np.where(mask == 0, 12, image[:, :, 1]) 
        
               image[:, :, 2] = np.where(mask == 0, 15, image[:, :, 2]) 
        
           def display_instances(image, boxes, masks, ids, names, scores): 
        
               max_area = 0 
        
               for i in range(len(boxes)): 
        
                   if names[ids[i]] == 'crosswalk': 
        
                       mask = masks[:, :, i] 
        
                       mask_area = np.sum(mask) / (mask.shape[0] * mask.shape[1]) 
        
                       if mask_area > max_area: 
        
                           max_area = mask_area 
        
                           box = boxes[i] 
        
                           mask = mask.astype(np.uint8) 
        
                           mask_image = apply_mask(image, mask) 
        
                           image = Image.fromarray(mask_image.astype(np.uint8)) 
        
                           image, _, _ = model.detect_with_image(image, verbose=0) 
        
                           image = np.asarray(image) 
        
               return image 
        
           def mrcnn(input_image): 
        
               results = model.detect([input_image], verbose=1) 
        
               r = results[0] 
        
               image = display_instances(input_image, r['rois'], r['masks'], r['class_ids'], class_names, r['scores']) 
        
               return image 
        
           if __name__ == '__main__': 
        
               test_image = cv2.imread('test_image.jpg') 
        
               output_image = mrcnn(test_image) 
        
               cv2.imshow('output', output_image) 
        
               cv2.waitKey(0)

CCTV-Inundation-Detection/packages/crosswalk_origine.py

Lines 1 to 196 in 570845e

    
           import os 
        
           import sys 
        
           import time 
        
           import numpy as np 
        
           import cv2 
        
           #import plaidml 
        
           from PIL import Image 
        
           import time 
        
           from tensorflow.compat.v1 import reset_default_graph,get_default_graph 
        
           sys.path.append(os.getcwd())  # To find local version of the library 
        
           from mrcnn.config_mrcnn import Config 
        
           from mrcnn import model_mrcnn as modellib 
        
           class ShapesConfig(Config): 
        
               """Configuration for training on the toy shapes dataset. 
        
               Derives from the base Config class and overrides values specific 
        
               to the toy shapes dataset. 
        
               """ 
        
               # Give the configuration a recognizable name 
        
               NAME = "shapes" 
        
               # Train on 1 GPU and 8 images per GPU. We can put multiple images on each 
        
               # GPU because the images are small. Batch size is 8 (GPUs * images/GPU). 
        
               GPU_COUNT = 1 
        
               IMAGES_PER_GPU = 1 
        
               # Number of classes (including background) 
        
               NUM_CLASSES = 1 + 1  # background + 1 class 
        
               # Use small images for faster training. Set the limits of the small side 
        
               # the large side, and that determines the image shape. 
        
               IMAGE_MIN_DIM = 800 
        
               IMAGE_MAX_DIM = 1024 
        
               # Use smaller anchors because our image and objects are small 
        
               RPN_ANCHOR_SCALES = (32, 64, 128, 256, 512)  # anchor side in pixels 
        
               # Reduce training ROIs per image because the images are small and have 
        
               # few objects. Aim to allow ROI sampling to pick 33% positive ROIs. 
        
               TRAIN_ROIS_PER_IMAGE = 200 
        
               # Use a small epoch since the data is simple 
        
               STEPS_PER_EPOCH = 1000 
        
               # use small validation steps since the epoch is small 
        
               VALIDATION_STEPS = 50 
        
           COCO_MODEL_PATH = os.path.join(os.getcwd(), 'Mask_RCNN', 'crosswalk.h5') 
        
           MODEL_DIR = os.path.join(os.getcwd(),'Mask_RCNN','logs') 
        
           config = ShapesConfig() 
        
           global mrcnn_model_crosswalk 
        
           mrcnn_model_crosswalk = modellib.MaskRCNN(mode="inference",  model_dir=MODEL_DIR,config=config) #MODEL_DIR=logs 
        
           # Load weights trained on ground 
        
           mrcnn_model_crosswalk.load_weights(COCO_MODEL_PATH, by_name=True) 
        
           global mrcnn_graph_crosswalk 
        
           mrcnn_graph_crosswalk = get_default_graph() 
        
           mrcnn_class_names = ['BG', 'crosswalk'] 
        
           def mrcnn(input): #output 
        
               def apply_mask(image, mask): 
        
                   image[:, :, 0] = np.where( 
        
                       mask == 0, 
        
                       125, 
        
                       image[:, :, 0] 
        
                   ) 
        
                   image[:, :, 1] = np.where( 
        
                       mask == 0, 
        
                       12, 
        
                       image[:, :, 1] 
        
                   ) 
        
                   image[:, :, 2] = np.where( 
        
                       mask == 0, 
        
                       15, 
        
                       image[:, :, 2] 
        
                   ) 
        
                   return image 
        
               # This function is used to show the object detection result in original image. 
        
               def display_instances(image_display_instances, boxes, masks, ids, names, scores,i): 
        
                   # max_area will save the largest object for all the detection results 
        
                   max_area = 0 
        
                   assert boxes.shape[0] == masks.shape[-1] == ids.shape[0] 
        
                   rang=0 
        
                   area_org=0 
        
                   objs=["crosswalk"] 
        
                   for i in range(len(r['rois'])): 
        
                       if names[ids[i]] in objs: 
        
                           mask = masks[:, :, i] 
        
                           ''' 
        
                           image_display_instances[mask] = 255 
        
                           image_display_instances[~mask] = 0 
        
                           ''' 
        
                           unique, counts = np.unique(image_display_instances, return_counts=True) 
        
                           mask_area = counts[1] / (counts[0] + counts[1]) 
        
                           if area_org==0: 
        
                               rang=i 
        
                               area_org=mask_area 
        
                               mask = masks[:, :, i] 
        
                           elif mask_area>area_org: 
        
                               area_org=mask_area 
        
                               rang=i 
        
                               mask = masks[:, :, i] 
        
                   #####print(i) 
        
                   # by mistake you put apply_mask inside for loop or you can write continue in if also 
        
                   image2 = apply_mask(image_display_instances, mask) 
        
                   image = image2 
        
                   return image 
        
               def transparent_back(image): 
        
                   image = image.convert('RGBA') 
        
                   L,H = image.size 
        
                   color_0 = image.getpixel((0, 0)) 
        
                   for h in range(H): 
        
                       for l in range(L): 
        
                           dot = (l, h) 
        
                           color_1 = image.getpixel(dot) 
        
                           if color_1 == color_0: 
        
                               color_1 = color_1[:-1] + (0,) 
        
                               image.putpixel(dot, color_1) 
        
                   return image 
        
               reset_default_graph() 
        
               image_CV2 = cv2.imread(input) #input 
        
               crosswalk_img=image_CV2.copy() 
        
               height, width, channels = image_CV2.shape 
        
               # Run detection 
        
               with mrcnn_graph_crosswalk.as_default(): 
        
                   results = mrcnn_model_crosswalk.detect([image_CV2], verbose=0) 
        
               # Visualize results 
        
               r = results[0] 
        
               if not len(r['rois']): 
        
                   # 输入文件 
        
                   img = Image.fromarray(cv2.cvtColor(image_CV2, cv2.COLOR_BGR2RGB)) #input 
        
                   width = img.size[0]#长度 
        
                   height = img.size[1]#宽度 
        
                   img = img.convert("RGBA") 
        
                   for i in range(img.width): 
        
                       for j in range(img.height): 
        
                           ''' 
        
                           r,g,b,a = img.getpixel((i,j)) 
        
                           #if(r !=None  and g != None and b!= None): 
        
                           r=0 
        
                           g=0 
        
                           b=0 
        
                           a=0 
        
                           ''' 
        
                           img.putpixel((i,j), (0,0,0,0)) 
        
                   # 输出图片 
        
                   #####img.save('./crosswalk/0.png') #mid file 
        
                   #os.remove(image) 
        
                   #transparent 
        
               else: 
        
                   print('crosswalks are detected') 
        
                   i=len(r['rois']) 
        
                   frame = display_instances( 
        
                                image_CV2, r['rois'], r['masks'], r['class_ids'], mrcnn_class_names, r['scores'],i 
        
                           ) 
        
                   #####cv2.imwrite(os.path.join(os.getcwd(),'mrcnn_bm','water.png'), image_CV2) #output[:-4]+"_pro.png" 
        
                   frame=Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) 
        
                   #####image_PIL = Image.open(os.path.join(os.getcwd(),'mrcnn_bm','water.png')) #output[:-4]+"_pro.png" 
        
                   image = transparent_back(frame) 
        
                   #image.save(os.path.join(os.getcwd(),'Mask_RCNN','crosswalk.png')) 
        
                   return image 
        
                   ''' 
        
                   width = image.size[0]#长度 
        
                   height = image.size[1]#宽度 
        
                   for ip in range(0,width):#遍历所有长度的点 
        
                       for jp in range(0,height):#遍历所有宽度的点 
        
                           data = (image.getpixel((ip,jp)))#打印该图片的所有 
        
                           if data[3]!=0:#RGBA的r值大于0，并且g值大于0,并且b值大于0 
        
                               frame.putpixel((ip,jp),(0,0,0,0)) 
        
                           else:#RGBA的r值大于0，并且g值大于0,并且b值大于0 
        
                               frame.putpixel((ip,jp),(0,0,0,255)) 
        
                   ''' 
        
           if __name__ == "__main__": 
        
               #train_model() 
        
               #file_handle() 
        
               start = time.time() 
        
               in1="./crosswalk_1.png" 
        
               out1="./crosswalk_1_passage_cloute.png" 
        
               mrcnn(in1, "1") 
        
               end = time.time() 
        
               print(end-start)

Step 2: ⌨️ Coding

Modify packages/Blur.py ✓ 0351d96 Edit

Modify packages/Blur.py with contents:
• Organize the imports at the beginning of the script.
• Remove the commented-out code in lines 6, 10, and 11.
• Improve the function name from 'blur' to 'apply_gaussian_blur' for better readability.
• Add a docstring to the 'apply_gaussian_blur' function explaining its purpose and the parameters it takes.
• Remove the hard-coded input and output file paths in lines 17 and 18. Instead, pass these as parameters to the function.

--- 
+++ 
@@ -2,18 +2,26 @@
 # coding: utf-8
 
 # In[12]:
-import numpy
-#import argparse
 import cv2
-def blur(br1,br2,blurry,Input,Output):
-    image = cv2.imread(Input)
-    #result = np.hstack([canny])
-    #ret,thresh1 = cv2.threshold(image,200,255,cv2.THRESH_BINARY)
+# Removed unused import argparse
+# numpy import not used, so it is also removed
+def apply_gaussian_blur(br1, br2, blurry, input_path, output_path):
+    """Applies a Gaussian blur to an image using OpenCV.
+
+    Args:
+        br1 (int): Width (in pixels) of the kernel.
+        br2 (int): Height (in pixels) of the kernel.
+        blurry (int): Standard deviation in the X and Y directions for the Gaussian kernel.
+        input_path (str): Path to the input image.
+        output_path (str): Path to save the blurred image.
+    """
+    image = cv2.imread(input_path)
+
     blurred = cv2.GaussianBlur(image, (br1, br2 ), blurry)
     
-    cv2.imwrite(Output, blurred)
+    cv2.imwrite(output_path, blurred)
     return
 
-In="/Users/YiHung/Downloads/0002_sobel.png"
-Out="/Users/YiHung/Downloads/0002_sobel_blur.png"
-blur(9,9,100,In,Out)
+input_path="/path/to/input_image.png"
+output_path="/path/to/output_blurred_image.png"
+apply_gaussian_blur(9, 9, 100, input_path, output_path)

Running GitHub Actions for packages/Blur.py ✓ Edit

Check packages/Blur.py with contents:
Ran GitHub Actions for 0351d96b05769fd9854df448a90a580a9476db0f:

Modify packages/InRange.py ✓ 3303741 Edit

Modify packages/InRange.py with contents:
• Organize the imports at the beginning of the script.
• Improve the function name from 'InRange' to 'apply_color_range' for better readability.
• Add a docstring to the 'apply_color_range' function explaining its purpose and the parameters it takes.
• Remove the commented-out code in lines 25-43.
• Remove the hard-coded input file path in line 55. Instead, pass this as a parameter to the function.

--- 
+++ 
@@ -1,27 +1,27 @@
 #!/usr/bin/env python
 # coding: utf-8
 
-# In[12]:
+import cv2
 import numpy as np
-from PIL import Image
-import cv2
-def InRange(hp,sp,vp,ha,sa,va,INPUT):
-    img = cv2.imread(INPUT)#overlay
 
-    # OpenCV的顏色預設是BGR格式，這邊將其轉換為HSV格式
+def apply_color_range(hp, sp, vp, ha, sa, va, input_path):
+    """Applies a color range to an image using OpenCV.
+
+    Args:
+        hp, sp, vp (int): Lower bound for the HSV color space.
+        ha, sa, va (int): Upper bound for the HSV color space.
+        input_path (str): Path to the input image.
+    """
+    img = cv2.imread(input_path)
+
     hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
 
-    # 以HSV格式決定要提取的顏色範圍，顏色格式的說明請參考後續內容
-    lower = np.array([hp,sp,vp]) #0,0,0
-    upper = np.array([ha,sa,va]) #0,0,26
-    # 將HSV影像的閾值設定為想要提取的顏色
+    lower = np.array([hp, sp, vp])
+    upper = np.array([ha, sa, va])
+
     mask = cv2.inRange(hsv, lower, upper)
-    # 使用bitwise_and()合併掩膜(mask)和原來的影像
-    #img_specific = cv2.bitwise_and(img,img, mask= mask)
-    #result = cv2.bitwise_and(image, image, mask)
-    # 展示原圖、掩膜、抽取顏色後的影像
-    cv2.imwrite(INPUT, mask)
-    #os.remove(base_path_overlay+str(min_in_file(base_path_overlay))+".png")
+
+    cv2.imwrite(input_path, mask)
     '''
     # 输入文件
     img = Image.open(INPUT)
@@ -51,6 +51,6 @@
     cv2.imwrite(INPUT, dst)
     #os.remove(base_path_couleurs_bleu+str(min_in_file(base_path_couleurs_bleu))+".png")
     return
-if __name__=="__main__":
-    input="./05_test.png"
-    InRange(0,43,46,10,255,255,input)
+if __name__ == "__main__":
+    input_path = "./05_test.png"
+    apply_color_range(0, 43, 46, 10, 255, 255, input_path)

Running GitHub Actions for packages/InRange.py ✓ Edit

Check packages/InRange.py with contents:
Ran GitHub Actions for 3303741743311b230bfcd16e5549dc6cb5276fd0:

Modify packages/Morphology.py ✓ 581f789 Edit

Modify packages/Morphology.py with contents:
• Organize the imports at the beginning of the script.
• Improve the function name from 'morphology' to 'apply_morphological_operations' for better readability.
• Add a docstring to the 'apply_morphological_operations' function explaining its purpose and the parameters it takes.
• Remove the commented-out code in lines 10-15 and 23-24.
• Remove the hard-coded input and output file paths in lines 24 and 25. Instead, pass these as parameters to the function.
--- 
+++ 
@@ -2,26 +2,25 @@
 # coding: utf-8
 
 # In[12]:
+import numpy as np
 import cv2
-import numpy as np
-def morphology(input,output):
+def apply_morphological_operations(input_path, output_path):
+    """Applies morphological operations to an image using OpenCV.
+
+    Args:
+        input_path (str): Path to the input image.
+        output_path (str): Path where the processed image should be saved.
+    """
     
     img = cv2.imread(input)
-    '''
-    # 2. cv2.MORPH_OPEN 先进行腐蚀操作，再进行膨胀操作
-    kernel = np.ones((5, 5), np.uint8)
-    opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)
-    #cv2.imshow('opening', opening)
-    '''
+
     # 3. cv2.MORPH_CLOSE 先进行膨胀，再进行腐蚀操作
     kernel = np.ones((3, 3), np.uint8)
     closing = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)
-    #cv2.imshow('closing', closing)
+
     
-    cv2.imwrite(output, closing)
+    cv2.imwrite(output_path, closing)
     return
 '''
-input="/Users/YiHung/Downloads/0002_sobel_blur_threshold.png"
-output="/Users/YiHung/Downloads/0002_sobel_blur_threshold_morphologCL.png"
-morphology(input,output)
+
 '''

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Ran GitHub Actions for 581f7892176b6328860ae7d278691f3a01532c92:

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codeautopilot · 2024-01-05T07:59:45Z

Potential solution

To refactor the scripts in the 'packages' folder, we will follow the proposed changes for each file, focusing on code quality, performance optimization, integration with the package, and additional refinements such as error handling, testing, and documentation. The reasoning behind this approach is to ensure that the code is maintainable, efficient, and consistent with the rest of the package. We will also ensure that the code adheres to PEP 8 style guidelines and includes proper documentation for ease of use and future maintenance.

Code

For each file, we will apply the proposed changes. Below are examples of how to refactor some of the scripts based on the provided proposals:

Refactor `water.py`

# Modularization example: Extracting the apply_mask function
def apply_mask(image, mask):
    # Code to apply the mask to the image
    pass

# Removing redundant imports and dead code
import time  # Remove additional imports of time

# Consistent naming conventions
def display_instances(image, boxes, masks, class_ids, class_names):
    # Function code
    pass

# Performance optimization: Avoid global variables
def predict(model, image):
    # Function code without using global variables
    pass

# Integration with the package: Consistent API
def load_model(model_path):
    # Function code to load the model
    pass

# Documentation: Update README and add docstrings
"""
This module contains functions for water detection and image processing.
"""

Refactor `zone_inondee.py`

# Consistent function signatures
def fill_edge(image, edge_width=10):
    # Function code
    pass

# Performance optimization: Vectorization
def white_area_size(image):
    return np.count_nonzero(image == 255)

# Integration with the package: Modular design
from utils import read_image  # Assuming read_image is a shared utility function

# Specific refactoring steps: Refactor delete_frame_lines
def delete_frame_lines(image, edge_width=10):
    image[:edge_width, :] = 0
    image[-edge_width:, :] = 0
    image[:, :edge_width] = 0
    image[:, -edge_width:] = 0
    return image

Refactor `TimeTransition.py`

# Code quality improvements: Add docstrings and comments
def jpg_to_png(directory):
    """
    Converts JPG files in the directory to PNG files with timestamps.
    """
    # Function code
    pass

# Performance optimization: Efficient file handling
def time_to_timestamp(time_string):
    # Function code
    pass

# Integration with the package: Consistent API
def timestamp_to_time(timestamp):
    # Function code
    pass

# Additional refinements: Error handling
try:
    # Code that might raise an exception
except Exception as e:
    print(f"Error: {e}")

For the other files (zone_inondee_origine.py, voiture.py, Sobel.py, Threshold.py, TimeTransition_origine.py, voiture_origine.py, utils.py), similar refactoring steps will be applied based on the proposals provided. This includes improving function names, adding error handling, optimizing performance, and ensuring integration with the package. Each script will be tested individually and in combination with the rest of the package to ensure that the refactoring does not introduce any regressions.

Click here to create a Pull Request with the proposed solution

Files used for this task:

Changes on packages/water.py

To refactor the water.py script, we should focus on improving code quality, optimizing performance, and ensuring it integrates well with the rest of the package. Here's a practical proposal to achieve these goals:

Code Quality Improvements:
- Modularization: Break down the code into smaller, reusable functions or classes. For example, the apply_mask, display_instances, and transparent_back functions can be separated out and potentially reused in other scripts.
- Remove Redundant Code: There are multiple imports of time and sys.path.append which can be cleaned up.
- Consistent Naming Conventions: Ensure that variable and function names follow a consistent naming convention (e.g., snake_case for variables and functions).
- Remove Dead Code: The min_in_file, max_in_file, min_fichier, and max_fichier functions do not seem to be used and can be removed if they are indeed not needed.
- Comments and Docstrings: Add comments and docstrings to functions to explain their purpose, parameters, and return values.
Performance Optimization:
- Avoid Global Variables: Refactor the code to pass variables as parameters to functions instead of using global variables.
- Efficient Image Processing: Check if image processing steps can be optimized, for example, by using vectorized operations with NumPy instead of looping over pixels.
- Model Loading: Ensure that the model is loaded only once if the script is called multiple times, to avoid unnecessary overhead.
Integration with the Package:
- Consistent API: Make sure that the function signatures are consistent with other scripts in the package.
- Error Handling: Add proper error handling to ensure that the script can gracefully handle unexpected inputs or failures.
- Unit Tests: Write unit tests for the functions to ensure they work as expected and to facilitate integration with the rest of the package.
Code Refactoring:
- Refactor Large Functions: The predict function is quite large and does multiple things. Break it down into smaller functions that each handle a specific task.
- Use Context Managers: Use context managers (the with statement) for managing resources like files or sessions, which ensures that resources are properly released after their use.
Code Style and Standards:
- PEP 8 Compliance: Ensure the code adheres to PEP 8 style guidelines for Python code.
- Type Annotations: Add type annotations to function signatures to improve readability and facilitate static type checking.
Documentation:
- Update README: Ensure that the README file or any relevant documentation is updated to reflect the changes made to the script.
Version Control:
- Commit Changes: Use version control to commit changes incrementally, with clear commit messages that explain the changes made.

By following this plan, we can refactor the water.py script to improve its maintainability, performance, and integration with the CCTV-Inundation-Detection package.

Changes on packages/zone_inondee.py

To refactor the zone_inondee.py script, we should focus on improving code quality, optimizing performance, and ensuring it integrates well with the rest of the package. Here is a practical proposal to achieve these goals:

Code Quality Improvements:
- Consistent Function Signatures: Ensure all functions have consistent input parameters. For example, fill_edge and max_min_area_rect both take an input_type parameter, but delete_frame_lines does not. We should standardize this across all functions.
- Remove Redundant Code: The delete_frame_lines function reads the image but then overwrites the image variable with the image data. This is unnecessary and can be simplified.
- Error Handling: Add error handling for file operations and image processing steps to ensure the script doesn't crash unexpectedly.
- Use of Comments: Improve comments to explain the purpose of functions and complex code blocks more clearly.
- Function Decomposition: Break down complex functions into smaller, more manageable pieces that are easier to understand and test.
Performance Optimization:
- Vectorization: Replace for-loops with vectorized operations where possible. For example, white_area_size can be optimized by using NumPy's vectorized operations to count white pixels.
- Avoid Repeated I/O: Minimize the number of times an image is read from disk by passing image data directly between functions instead of file paths.
- In-Place Operations: Use in-place operations where possible to reduce memory usage and improve performance.
Integration with the Package:
- Modular Design: Ensure that functions are modular and can be easily called from other scripts within the package.
- Consistent File Handling: Use a consistent method for reading and writing images across the package.
- Shared Utility Functions: If there are common operations used by multiple scripts, consider moving them to utils.py and importing them as needed.
Specific Refactoring Steps:
- delete_frame_lines: Simplify the function to avoid unnecessary image read and write operations. Use slicing to set the edge pixels to black.
- fill_edge: Remove the input_type parameter and always assume the input is an image array. This simplifies the function signature and usage.
- white_area_size: Use NumPy's np.count_nonzero to count white pixels instead of a double for-loop.
- max_min_area_rect: Ensure that the function only processes image data and does not read from disk.
- sort_contours: This function is fine as is, but ensure it is used consistently with other contour-related functions.
- count_lines: Optimize the Hough Line Transform parameters for better performance.
- line_distance: Remove the input_type parameter and correct the call to count_lines to pass the correct data type.

After making these changes, thoroughly test the script to ensure that all functions work as expected and that the script integrates well with the rest of the package. Additionally, consider adding unit tests to validate the functionality of each function.

Changes on packages/TimeTransition.py

To refactor the TimeTransition.py script, we should focus on improving the code quality, optimizing performance, and ensuring it integrates well with the rest of the package. Here are some practical steps to achieve this:

Code Quality Improvements:
- Add a docstring at the beginning of the script to explain the purpose and usage of the script.
- Add comments to functions to explain what they do, their parameters, and their return values.
- Ensure consistent naming conventions (e.g., snake_case for functions and variables).
- Remove commented-out code that is not needed to reduce clutter.
- Use more descriptive variable names instead of generic ones like file_nom.
Performance Optimization:
- Check if there are any redundant operations that can be eliminated.
- If the script is dealing with a large number of files, consider using more efficient file handling or libraries that can handle batch operations.
Integration with the Package:
- Ensure that the script uses relative imports from the package if it needs to import other modules from the same package.
- Make sure that the script does not have any side effects that could affect other modules in the package.
- If the script is meant to be used as a module, ensure that it does not execute code when imported (only under if __name__ == "__main__":).
Additional Refinements:
- Replace the hard-coded file extension in JPGtoPNG() with a variable or constant, and allow the function to handle different file types.
- Consider adding error handling for file operations and date conversions to make the script more robust.
- If the script is intended to be used as a command-line tool, implement argument parsing to handle input parameters more gracefully.

Here's an example of how the refactored TimeTransition.py might start:

"""
This module provides functions to convert between human-readable time strings and UNIX timestamps,
and to rename image files based on their timestamp.
"""

import time
import os

def jpg_to_png(directory):
    """
    Converts all JPG files in the specified directory to PNG files, renaming them with their UNIX timestamp.
    
    Parameters:
    - directory: The directory containing JPG files to be converted.
    """
    for file in os.listdir(directory):   
        if file.lower().endswith('.jpg'):
            timestamp = time_to_timestamp(file[:-4])
            new_filename = f"{timestamp}.png"
            os.rename(os.path.join(directory, file), os.path.join(directory, new_filename))
            print(f"{file[:-4]} -> {new_filename}")

def time_to_timestamp(time_string):
    """
    Converts a human-readable time string to a UNIX timestamp.
    
    Parameters:
    - time_string: A string representing time in the format "%Y-%m-%d_%Hh%M".
    
    Returns:
    - A UNIX timestamp corresponding to the given time string.
    """
    struct_time = time.strptime(time_string, "%Y-%m-%d_%Hh%M")
    return int(time.mktime(struct_time))
    
def timestamp_to_time(timestamp):
    """
    Converts a UNIX timestamp to a human-readable time string.
    
    Parameters:
    - timestamp: A UNIX timestamp.
    
    Returns:
    - A string representing the time in the format "%Y-%m-%d_%Hh%M".
    """
    t = time.localtime(int(timestamp))
    return time.strftime("%Y-%m-%d_%Hh%M", t)

if __name__ == "__main__":
    # Example usage:
    directory_name = input("Enter the directory name: ")
    jpg_to_png(directory_name)
    # Example timestamp conversion:
    example_timestamp = 1596990600
    print(timestamp_to_time(example_timestamp))

This refactored code is cleaner, has better documentation, and is more maintainable. It also provides a clear interface for other scripts to use the functions provided.

Changes on packages/zone_inondee_origine.py

To refactor the zone_inondee_origine.py script, we should focus on improving code quality, optimizing performance, and ensuring it integrates well with the rest of the package. Here are some practical steps to achieve this:

Code Quality Improvements:
- Remove Unused Imports: Clean up the import statements by removing any modules that are not used in the script.
- Consistent Commenting: Ensure that comments are written in English and provide meaningful information about the code. Remove any unnecessary comments.
- Function Documentation: Add docstrings to each function to explain what it does, its parameters, and its return value.
- Variable Naming: Use descriptive variable names that follow the PEP 8 naming conventions.
- Error Handling: Add error handling where appropriate to manage exceptions and provide useful feedback to the user.
Performance Optimization:
- Avoid Repetitive I/O Operations: Minimize the number of times the script reads or writes to disk by keeping data in memory when possible.
- Vectorization: Use NumPy's vectorized operations instead of loops where possible to improve performance.
- Use Efficient Algorithms: Review the algorithms used and replace them with more efficient ones if available.
Integration with the Package:
- Modularize Code: Break down the script into smaller, reusable modules that can be easily integrated with other parts of the package.
- Consistent API: Ensure that the functions have a consistent API with the rest of the package. This includes parameter names, default values, and return types.
- Remove Redundant Code: If there are functions that duplicate functionality found in other parts of the package, remove them and use the existing functions instead.
Specific Refactoring Steps:
- Refactor del_frame_ligne Function: This function can be optimized by using slicing instead of nested loops to black out the edges of the image.
- Refactor fill_edge Function: This function can be simplified by removing unused code and ensuring that the mask is correctly applied.
- Refactor white_area_size Function: This function can be optimized by using NumPy's array operations instead of a double loop.
- Refactor max_minAreaRect Function: This function can be optimized by using built-in functions from OpenCV to find the maximum area rectangle.
- Refactor compute_size_minAreaRect and compute_area Functions: These functions should be reviewed for efficiency and clarity.
- Refactor crosswalk_count Function: This function is quite complex and should be broken down into smaller, more manageable pieces.
- Refactor File Handling Functions: The functions min_in_file, max_in_file, min_fichier, and max_fichier can be simplified and optimized to handle file operations more efficiently.
Testing and Validation:
- Unit Tests: Write unit tests for each function to ensure they work as expected.
- Integration Tests: Test the script as a whole to ensure it integrates well with the rest of the package.
Code Formatting:
- PEP 8 Compliance: Ensure the entire script follows PEP 8 style guidelines for Python code.
Version Control:
- Commit Changes: Use version control to commit changes incrementally, with clear and descriptive commit messages.

By following these steps, the zone_inondee_origine.py script can be refactored to improve its maintainability, performance, and integration with the CCTV-Inundation-Detection package.

Changes on packages/voiture.py

To refactor the voiture.py script, we should focus on improving code quality, optimizing performance, and ensuring good integration with the rest of the package. Here's a practical proposal to achieve these goals:

Code Quality Improvements:
- Remove redundant imports and duplicate code.
- Use meaningful variable names and remove French comments or variables to maintain consistency in English.
- Remove unnecessary comments and commented-out code.
- Ensure PEP 8 compliance for better readability.
- Remove del statements at the end of functions, as they are unnecessary and do not execute (since return precedes them).
Performance Optimization:
- Avoid loading the model in every function call. Load it once and reuse it.
- Minimize file I/O operations by caching results where possible.
- Use vectorized operations with NumPy instead of loops where applicable.
- Consider using a context manager for file operations to ensure proper resource management.
Integration with the Package:
- Ensure that the script uses relative imports from the package instead of modifying the system path.
- Make sure that the script's functions can be easily imported and used in other parts of the package.
- If the script is meant to be used as a command-line tool, consider using argparse or similar to handle arguments.
Refactoring Specific Code Blocks:
- The mrcnn function is too long and does multiple things. Break it down into smaller functions with single responsibilities (e.g., apply_mask, display_instances, transparent_back).
- The min_in_file and max_in_file functions have similar code; consider creating a single function that can return both min and max using a single iteration.
- The transparent_back function can be optimized by using array operations instead of looping over each pixel.
Testing and Documentation:
- Write unit tests for each function to ensure they work as expected after refactoring.
- Add docstrings to functions to explain their purpose, parameters, and return values.
Version Control:
- Use version control (e.g., Git) to track changes and allow for easy rollback if needed.
- Commit changes incrementally with clear messages describing what was changed.
Continuous Integration:
- Set up continuous integration to run tests automatically when changes are made to the script.

Here's an example of how you might refactor the min_in_file and max_in_file functions into a single function:

def min_max_in_file(router, file_extension='*.png'):
    min_file, max_file = None, None
    for file in glob.iglob(os.path.join(os.getcwd(), router, file_extension)):
        file_name = os.path.split(file)[1]
        if file_name.split(".")[0].isdigit():
            file_number = int(file_name.split(".")[0])
            if min_file is None or file_number < min_file:
                min_file = file_number
            if max_file is None or file_number > max_file:
                max_file = file_number
    return min_file, max_file

By following these steps, you should be able to refactor voiture.py to be cleaner, more efficient, and better integrated with the rest of the package.

Changes on packages/Sobel.py

To refactor the Sobel.py script, we should focus on improving the code quality, optimizing performance, and ensuring it integrates well with the rest of the package. Here's a practical proposal for refactoring this script:

Improve Code Quality:
- Add a docstring to the sobel function to explain what it does, its parameters, and its return value.
- Remove the hardcoded file paths in the commented-out section at the bottom of the script.
- Use more descriptive variable names to improve readability.
Optimize Performance:
- Check if the input image exists before attempting to read it to avoid unnecessary errors.
- Consider adding error handling for file reading and writing operations.
Integration:
- Ensure that the function parameters are consistent with other scripts in the package.
- If the package has a common utility module for file operations, use that for reading and writing images.
Additional Enhancements:
- Allow the function to work with different kernel sizes by adding an optional parameter.
- Add an option to return the gradient image in memory instead of writing it to disk, which can be useful if this function is part of a larger image processing pipeline.

Here's the refactored Sobel.py script:

#!/usr/bin/env python
# coding: utf-8

import cv2
import os

def sobel(input_path, output_path, kernel_size=3):
    """
    Applies the Sobel operator to an input image and saves the result.

    Parameters:
    - input_path: str, path to the input image.
    - output_path: str, path to save the output image.
    - kernel_size: int, size of the Sobel kernel (default is 3).

    Returns:
    - None
    """
    if not os.path.exists(input_path):
        raise FileNotFoundError(f"Input file {input_path} does not exist.")

    image = cv2.imread(input_path)
    if image is None:
        raise IOError(f"Could not read the image {input_path}.")

    grad_x = cv2.Sobel(image, ddepth=cv2.CV_64F, dx=1, dy=0, ksize=kernel_size)
    grad_y = cv2.Sobel(image, ddepth=cv2.CV_64F, dx=0, dy=1, ksize=kernel_size)

    # Subtract the y-gradient from the x-gradient
    gradient = cv2.subtract(grad_x, grad_y)
    gradient = cv2.convertScaleAbs(gradient)

    if not cv2.imwrite(output_path, gradient):
        raise IOError(f"Could not write the image {output_path}.")

# Example usage:
# sobel("path/to/input.png", "path/to/output.png")

This refactored script now includes error handling, improved readability, and additional functionality for kernel size customization. It also removes hardcoded paths, making it more flexible for use as a module within the package.

Changes on packages/Threshold.py

To refactor the Threshold.py script, we should focus on improving code quality, optimizing performance, and ensuring it integrates well with the rest of the package. Here are some practical steps to achieve this:

Code Quality Improvements:
- Add a docstring to the threshold function to explain its purpose, parameters, and return value.
- Use more descriptive variable names instead of ret, threshold to improve readability.
- Handle exceptions where appropriate, for example, when reading or writing files.
- Remove the commented-out code at the bottom or move it to a separate test function.
Performance Optimization:
- Check if the image is read correctly after cv2.imread and handle any errors that may occur.
- Consider checking if the input image is already in grayscale before applying the threshold to avoid unnecessary conversions.
Integration with the Package:
- Ensure that the function parameters are consistent with other scripts in the package.
- If the package has a common utility module for file operations, use that for reading and writing images.
- If there are any common thresholds or grayscale degrees used across the package, consider defining them as constants in a shared module.
Additional Refinements:
- If this script is meant to be used as both a module and a standalone script, add an if __name__ == "__main__": block to handle command-line arguments.
- Add type hints to function parameters and return types for better code clarity and to assist with type checking.

Here's an example of how the refactored Threshold.py might look:

#!/usr/bin/env python
# coding: utf-8

import cv2
from typing import Optional

def apply_threshold(image_path: str, threshold_value: int, max_value: int, output_path: str) -> Optional[bool]:
    """
    Apply a binary threshold to an image and save the result.

    Parameters:
    - image_path: str, path to the input image
    - threshold_value: int, threshold value to apply
    - max_value: int, maximum value to use with the THRESH_BINARY thresholding
    - output_path: str, path to save the thresholded image

    Returns:
    - bool, True if the operation was successful, None otherwise
    """
    try:
        img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
        if img is None:
            raise FileNotFoundError(f"Image not found at {image_path}")
        
        _, binary_image = cv2.threshold(img, threshold_value, max_value, cv2.THRESH_BINARY)
        cv2.imwrite(output_path, binary_image)
        return True
    except Exception as e:
        print(f"An error occurred: {e}")
        return None

# If this script is run as a standalone program, process command-line arguments.
if __name__ == "__main__":
    import sys
    if len(sys.argv) != 5:
        print("Usage: python Threshold.py <image_path> <threshold_value> <max_value> <output_path>")
        sys.exit(1)
    
    image_path, threshold_value, max_value, output_path = sys.argv[1:]
    apply_threshold(image_path, int(threshold_value), int(max_value), output_path)

This refactored version includes error handling, command-line argument processing, type hints, and improved variable names. It also assumes that the input image is in grayscale; if this is not the case, additional logic may be needed to convert the image to grayscale before thresholding.

Changes on packages/TimeTransition_origine.py

To refactor the TimeTransition_origine.py script, we should focus on improving code quality, optimizing performance, and ensuring it integrates well with the rest of the package. Here's a practical proposal for refactoring this script:

Code Quality Improvements:
- Remove commented-out code that is not needed for understanding the script's functionality.
- Ensure consistent use of quotes for strings (choose either single or double quotes and stick with it throughout the script).
- Add docstrings to functions to explain their purpose, parameters, and return values.
- Use more descriptive variable names instead of generic ones like Time and Timestamp.
- Handle potential exceptions that may occur during file operations or time conversions.
Performance Optimization:
- If the script is used to process a large number of files or timestamps, consider using more efficient data structures or algorithms.
- Avoid redundant conversions by caching results if the same conversion is performed multiple times.
Integration with the Package:
- Ensure that the script's functions can be easily imported and used in other scripts within the package.
- If the script shares functionality with other scripts in the package, consider creating a shared utility module to avoid code duplication.
- Make sure that the script adheres to any established coding standards or conventions used in the package.
Additional Refactoring Steps:
- Replace the os.listdir and os.rename operations with safer and more modern pathlib operations.
- Ensure that the script is compatible with both Python 2 and Python 3 if necessary, or update it to use Python 3 syntax exclusively.
- Add unit tests to verify the correctness of the time conversion functions.

Here's an example of how the refactored TimeTransition_origine.py might look after applying some of these suggestions:

import time
from pathlib import Path

def convert_jpg_to_png(directory):
    """
    Converts all JPG files in the specified directory to PNG files, renaming them with a timestamp.
    
    :param directory: The directory containing JPG files to convert.
    """
    for file_path in Path(directory).glob("*.jpg"):
        timestamp = time_to_timestamp(file_path.stem)
        new_file_path = file_path.with_name(f"{timestamp}.png")
        file_path.rename(new_file_path)
        print(f"{file_path.stem} -> {timestamp}")

def time_to_timestamp(time_string):
    """
    Converts a time string to a Unix timestamp.
    
    :param time_string: The time string to convert.
    :return: The Unix timestamp.
    """
    struct_time = time.strptime(time_string, "%Y-%m-%d_%Hh%M")
    return int(time.mktime(struct_time))
    
def timestamp_to_time(timestamp):
    """
    Converts a Unix timestamp to a time string.
    
    :param timestamp: The Unix timestamp to convert.
    :return: The time string.
    """
    t = time.localtime(int(timestamp))
    return time.strftime("%Y-%m-%d_%Hh%M", t)

if __name__ == "__main__":
    # Example usage:
    directory_name = "path_to_directory"
    convert_jpg_to_png(directory_name)
    
    example_timestamp = 1596990600
    print(timestamp_to_time(example_timestamp))

This refactored version includes improved function names, the removal of commented-out code, and the use of pathlib for file operations. It also provides a clear example of how to use the functions in the script.

Changes on packages/voiture_origine.py

To refactor the voiture_origine.py script, we should focus on improving code quality, optimizing performance, and ensuring it integrates well with the rest of the package. Here's a practical proposal for refactoring this script:

Code Cleanup and Organization:
- Remove redundant imports and duplicated code.
- Organize imports according to PEP 8 guidelines (standard library imports, third-party imports, local application/library specific imports).
- Remove commented-out code that is not needed.
- Delete the del statements at the end of functions, as they are unnecessary; Python's garbage collector will handle it.
Function Decomposition:
- Break down large functions into smaller, more manageable ones. For example, apply_mask, display_instances, and transparent_back can be separate functions outside of mrcnn.
- Ensure that each function has a single responsibility and is named appropriately.
Performance Optimization:
- Avoid using loops for image processing when vectorized operations can be used with NumPy for better performance.
- Minimize the use of os.path.join(os.getcwd(), ...) by setting a base directory at the start of the script and using it throughout.
- Use list comprehensions and generator expressions where appropriate for better performance and readability.
Code Readability:
- Add docstrings to functions to explain their purpose, parameters, and return values.
- Use meaningful variable names that make the code self-explanatory.
- Follow PEP 8 style guidelines for consistent code formatting.
Error Handling:
- Add proper error handling and input validation to make the script robust.
- Use try-except blocks where necessary to catch potential exceptions.
Integration with Other Packages:
- Ensure that the script's functions and classes are easily importable and usable by other scripts in the package.
- If the script shares common functionality with other scripts, consider creating a shared utility module.
Testing:
- Write unit tests for the functions to ensure they work as expected and to facilitate future refactoring.
- Use continuous integration tools to run tests automatically.
Remove Global Variables:
- Avoid using global variables. Instead, pass necessary data as parameters to functions.
Resource Management:
- Use context managers (with statement) for managing resources like files to ensure they are properly closed after use.
Comments and Documentation:
- Update comments to reflect changes in the code.
- Provide a module-level docstring explaining the purpose of the script and how to use it.
Version Control:
- Commit changes incrementally with clear, descriptive commit messages.
- Consider using feature branches if working in a team.

By applying these refactoring steps, the voiture_origine.py script will be cleaner, more maintainable, and better integrated within the CCTV-Inundation-Detection package.

Changes on packages/utils.py

To refactor the utils.py script, we can follow these steps to improve code quality, optimize performance, and ensure it integrates well with the rest of the package:

Code Organization and Comments: Ensure that the code is well-organized with functions logically grouped together. Add or update comments to clarify the purpose of each function and important blocks of code.
Use of Constants: Replace magic numbers and strings with named constants. This makes the code more readable and easier to maintain.
Error Handling: Improve error handling by using more specific exceptions and providing helpful error messages.
Function Refactoring: Break down long functions into smaller, more manageable ones. This improves readability and testability.
Type Annotations: Add type annotations to function signatures to clarify the expected types of arguments and return values.
Performance Optimization:
- Use list comprehensions where appropriate for more concise and faster code.
- Consider using os.scandir() instead of os.listdir() for potentially better performance when dealing with a large number of files.
Code Duplication: Identify and eliminate any duplicated code. For example, the process_train_info and process_val_info functions are very similar and could be combined into a single function with a parameter to control whether to apply data augmentation.
Testing: Add unit tests for each function to ensure they work as expected and to facilitate future refactoring.
Integration: Ensure that the refactored code integrates well with other scripts in the package. This may involve updating function calls and imports in other scripts.
Documentation: Update or add docstrings to each function to explain what it does, its parameters, and its return value.
Configuration File: Consider using a configuration file or environment variables for settings that might change, such as the val_rate or supported file extensions.
Use of Global Variables: Avoid the use of global variables within functions. If a global variable is necessary, ensure it is declared as such.
Plotting Function: The plotting code within read_split_data could be extracted into a separate function for better modularity.
File Handling: Ensure that files are properly closed after use. For example, use the with statement when opening files to ensure they are automatically closed.
Code Style: Follow PEP 8 style guidelines for Python code to improve readability and consistency.

Here's an example of how you might refactor the read_split_data function with some of the above suggestions:

import os
import json
import random
from typing import Tuple, List

# Constants
SUPPORTED_EXTENSIONS = [".jpg", ".JPG", ".jpeg", ".JPEG"]
CLASS_INDICES_FILE = 'class_indices.json'

def read_split_data(root: str, val_rate: float = 0.2) -> Tuple[List[str], List[int], List[str], List[int]]:
    """
    Reads and splits the data into training and validation sets.
    
    Parameters:
    - root: The root directory of the dataset.
    - val_rate: The proportion of data to be used for validation.
    
    Returns:
    - A tuple containing lists of image paths and labels for training and validation.
    """
    random.seed(0)
    assert os.path.exists(root), f"dataset root: {root} does not exist."

    flower_class = [cla for cla in os.listdir(root) if os.path.isdir(os.path.join(root, cla))]
    flower_class.sort()
    class_indices = {k: v for v, k in enumerate(flower_class)}

    with open(CLASS_INDICES_FILE, 'w') as json_file:
        json.dump(class_indices, json_file, indent=4)

    train_images_path, train_images_label, val_images_path, val_images_label = [], [], [], []
    every_class_num = []

    for cla in flower_class:
        images = [os.path.join(root, cla, i) for i in os.listdir(os.path.join(root, cla))
                  if os.path.splitext(i)[-1] in SUPPORTED_EXTENSIONS]
        image_class = class_indices[cla]
        every_class_num.append(len(images))
        val_path = random.sample(images, k=int(len(images) * val_rate))

        for img_path in images:
            if img_path in val_path:
                val_images_path.append(img_path)
                val_images_label.append(image_class)
            else:
                train_images_path.append(img_path)
                train_images_label.append(image_class)

    print(f"{sum(every_class_num)} images were found in the dataset.\n"
          f"{len(train_images_path)} for training, {len(val_images_path)} for validation")

    return train_images_path, train_images_label, val_images_path, val_images_label

This refactoring includes type annotations, use of constants, and improved error handling. Similar refactoring can be applied to other parts of the script.

Disclaimer: This comment was entirely generated using AI. Be aware that the information provided may be incorrect.

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yihong1120 added the sweep Assigns Sweep to an issue or pull request. label Jan 5, 2024

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Sweep: Refactor the python scripts in 'packages' folder (✓ Sandbox Passed) #10

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Refactor scripts in 'packages' for better quality and performance #12

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Refactor scripts in 'packages' folder for improved code quality and performance #16

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Sweep: Refactor the python scripts in 'packages' folder #9

Sweep: Refactor the python scripts in 'packages' folder #9

yihong1120 commented Jan 5, 2024 •

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🚀 Here's the PR! #10

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Sweep: Refactor the python scripts in 'packages' folder #9

Sweep: Refactor the python scripts in 'packages' folder #9

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🚀 Here's the PR! #10

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Refactor water.py

Refactor zone_inondee.py

Refactor TimeTransition.py

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Refactor `water.py`

Refactor `zone_inondee.py`

Refactor `TimeTransition.py`