Car Counting with python

Author: celalaygar

README.md

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+### car-counting-with-python
+
+``` 
+Car counting process made with  Numpy, opencv on python.
+``` 
+if you wanna to watch the video as result of this project you can click and watch on youtube
+- for	counting_car.py_1 project you can watch this video : [![Watch the video](https://youtu.be/Oq7JGBhgvl4)](https://youtu.be/Oq7JGBhgvl4)
+- for	counting_car.py_2 project you can watch this video : [![Watch the video](https://www.youtube.com/watch?v=qm-Ha_ZrGrw)](https://www.youtube.com/watch?v=qm-Ha_ZrGrw)
+
+#### requirements
+- pycharm
+- python 3+
+- numpy
+- cv2 (opencv)
+#### İmportant
+There is a few problem in these projects. These problems will be solved soon.

counting_car_1.py

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+import cv2
+import numpy as np
+
+class Kordinat:
+    def __init__(self,x,y):
+        self.x=x
+        self.y=y
+
+class Sensor:
+    def __init__(self,kordinat1,kordinat2,frame_weight,frame_lenght):
+        self.kordinat1=kordinat1
+        self.kordinat2=kordinat2
+        self.frame_weight=frame_weight
+        self.frame_lenght =frame_lenght
+        self.mask=np.zeros((frame_weight,frame_lenght,1),np.uint8)*abs(self.kordinat2.y-self.kordinat1.y)
+        self.full_mask_area=abs(self.kordinat2.x-self.kordinat1.x)
+        cv2.rectangle(self.mask,(self.kordinat1.x,self.kordinat1.y),(self.kordinat2.x,self.kordinat2.y),(255),thickness=cv2.FILLED)
+        self.stuation=False
+        self.car_number_detected=0
+
+
+video=cv2.VideoCapture("video1.mp4")
+ret,frame=video.read()
+cropped_image= frame[0:450, 0:450]
+fgbg=cv2.createBackgroundSubtractorMOG2()
+Sensor1 = Sensor(
+    Kordinat(1, cropped_image.shape[1] - 35),
+    Kordinat(340, cropped_image.shape[1] - 30),
+    cropped_image.shape[0],
+    cropped_image.shape[1])
+
+kernel=np.ones((5,5),np.uint8)
+font=cv2.FONT_HERSHEY_TRIPLEX
+while (1):
+    ret,frame=video.read()
+    # resize frame
+    cropped_image= frame[0:450, 0:450]
+    # make morphology for frame
+    deleted_background=fgbg.apply(cropped_image)
+    opening_image=cv2.morphologyEx(deleted_background,cv2.MORPH_OPEN,kernel)
+    ret,opening_image=cv2.threshold(opening_image,125,255,cv2.THRESH_BINARY)
+
+    # detect moving anything
+    _, cnts,_=cv2.findContours(opening_image,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
+    result=cropped_image.copy()
+
+    zeros_image=np.zeros((cropped_image.shape[0], cropped_image.shape[1], 1), np.uint8)
+
+    # detect moving anything with loop
+    for cnt in cnts:
+        x,y,w,h=cv2.boundingRect(cnt)
+        if (w>75 and h>75  and w<160 and h<160 ):
+            cv2.rectangle(result,(x,y),(x+w,y+h),(255,0,0),thickness=2)
+            cv2.rectangle(zeros_image,(x,y),(x+w,y+h),(255),thickness=cv2.FILLED)
+
+    # detect whether there is car via bitwise_and
+    mask1=np.zeros((zeros_image.shape[0],zeros_image.shape[1],1),np.uint8)
+    mask_result=cv2.bitwise_or(zeros_image,zeros_image,mask=Sensor1.mask)
+    white_cell_number=np.sum(mask_result==255)
+
+    # detect to control whether car is passing under the red line sensor
+    sensor_rate=white_cell_number/Sensor1.full_mask_area
+    if sensor_rate>0:
+        print("result : ",sensor_rate)
+    # if car is passing under the red line sensor . red line sensor is yellow color.
+
+    if (sensor_rate>=0.9 and  sensor_rate<3.1 and Sensor1.stuation==False):
+        # draw the red line
+        cv2.rectangle(result, (Sensor1.kordinat1.x, Sensor1.kordinat1.y), (Sensor1.kordinat2.x, Sensor1.kordinat2.y),
+                      (0,255, 0,), thickness=cv2.FILLED)
+        Sensor1.stuation = True
+    elif (sensor_rate<0.9 and Sensor1.stuation==True) :
+        # draw the red line
+        cv2.rectangle(result, (Sensor1.kordinat1.x, Sensor1.kordinat1.y), (Sensor1.kordinat2.x, Sensor1.kordinat2.y),
+                      (0, 0,255), thickness=cv2.FILLED)
+        Sensor1.stuation = False
+        Sensor1.car_number_detected+=1
+    else :
+        # draw the red line
+        cv2.rectangle(result, (Sensor1.kordinat1.x, Sensor1.kordinat1.y), (Sensor1.kordinat2.x, Sensor1.kordinat2.y),
+                      (0, 0, 255), thickness=cv2.FILLED)
+
+
+
+    cv2.putText(result,str(Sensor1.car_number_detected),(Sensor1.kordinat1.x,150),font,2,(255,255,255))
+
+
+    cv2.imshow("video", result)
+    #cv2.imshow("mask_result", mask_result)
+    #cv2.imshow("zeros_image", zeros_image)
+    #cv2.imshow("opening_image", opening_image)
+
+    k=cv2.waitKey(30) & 0xff
+    if k == 27 :
+        break
+
+video.release()
+cv2.destroyAllWindows()

counting_car_2.py

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+import cv2
+import numpy as np
+
+class Kordinat:
+    def __init__(self,x,y):
+        self.x=x
+        self.y=y
+
+class Sensor:
+    def __init__(self,kordinat1,kordinat2,frame_weight,frame_lenght):
+        self.kordinat1=kordinat1
+        self.kordinat2=kordinat2
+        self.frame_weight=frame_weight
+        self.frame_lenght =frame_lenght
+        self.mask=np.zeros((frame_weight,frame_lenght,1),np.uint8)*abs(self.kordinat2.y-self.kordinat1.y)
+        self.full_mask_area=abs(self.kordinat2.x-self.kordinat1.x)
+        cv2.rectangle(self.mask,(self.kordinat1.x,self.kordinat1.y),(self.kordinat2.x,self.kordinat2.y),(255),thickness=cv2.FILLED)
+        self.stuation=False
+        self.car_number_detected=0
+
+
+Sensor1 = Sensor(Kordinat(1, 425), Kordinat(1080, 430), 500, 1080)
+video=cv2.VideoCapture("video2.mp4")
+fgbg=cv2.createBackgroundSubtractorMOG2()
+#fgbg=cv2.createBackgroundSubtractorMOG2()
+kernel=np.ones((5,5),np.uint8)
+font=cv2.FONT_HERSHEY_TRIPLEX
+while (1):
+    ret,frame=video.read()
+    # resize frame
+    cut_image=frame[100:600,100:1180]
+    # make morphology for frame
+    deleted_background=fgbg.apply(cut_image)
+    opening_image=cv2.morphologyEx(deleted_background,cv2.MORPH_OPEN,kernel)
+    ret,opening_image=cv2.threshold(opening_image,125,255,cv2.THRESH_BINARY)
+
+    # detect moving anything
+    _, cnts,_=cv2.findContours(opening_image,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
+    result=cut_image.copy()
+
+    zeros_image=np.zeros((cut_image.shape[0],cut_image.shape[1],1),np.uint8)
+
+    # detect moving anything with loop
+    for cnt in cnts:
+        x,y,w,h=cv2.boundingRect(cnt)
+        if (w>100 and h>100 ):
+            cv2.rectangle(result,(x,y),(x+w,y+h),(255,0,0),thickness=2)
+            cv2.rectangle(zeros_image,(x,y),(x+w,y+h),(255),thickness=cv2.FILLED)
+
+    # detect whether there is car via bitwise_and
+    mask1=np.zeros((zeros_image.shape[0],zeros_image.shape[1],1),np.uint8)
+    mask_result=cv2.bitwise_or(zeros_image,zeros_image,mask=Sensor1.mask)
+    white_cell_number=np.sum(mask_result==255)
+
+    # detect to control whether car is passing under the red line sensor
+    sensor_rate=white_cell_number/Sensor1.full_mask_area
+    if sensor_rate>0:
+        print(sensor_rate)
+
+    # if car is passing under the red line sensor . red line sensor is yellow color.
+    if (sensor_rate >= 1.8 and sensor_rate<2.9 and Sensor1.stuation == False):
+        # draw the red line
+        cv2.rectangle(result, (Sensor1.kordinat1.x, Sensor1.kordinat1.y), (Sensor1.kordinat2.x, Sensor1.kordinat2.y),
+                      (0, 0, 255), thickness=cv2.FILLED)
+        Sensor1.stuation = False
+        Sensor1.car_number_detected += 2
+    if (sensor_rate>=0.6 and  sensor_rate<1.8 and Sensor1.stuation==False):
+        # draw the red line
+        cv2.rectangle(result, (Sensor1.kordinat1.x, Sensor1.kordinat1.y), (Sensor1.kordinat2.x, Sensor1.kordinat2.y),
+                      (0,255, 0,), thickness=cv2.FILLED)
+        Sensor1.stuation = True
+    elif (sensor_rate<0.6 and Sensor1.stuation==True) :
+        # draw the red line
+        cv2.rectangle(result, (Sensor1.kordinat1.x, Sensor1.kordinat1.y), (Sensor1.kordinat2.x, Sensor1.kordinat2.y),
+                      (0, 0,255), thickness=cv2.FILLED)
+        Sensor1.stuation = False
+        Sensor1.car_number_detected+=1
+    else :
+        # draw the red line
+        cv2.rectangle(result, (Sensor1.kordinat1.x, Sensor1.kordinat1.y), (Sensor1.kordinat2.x, Sensor1.kordinat2.y),
+                      (0, 0, 255), thickness=cv2.FILLED)
+
+
+    cv2.putText(result,str(Sensor1.car_number_detected),(Sensor1.kordinat1.x,Sensor1.kordinat1.y+60),font,2,(255,255,255))
+
+
+    cv2.imshow("video", result)
+    #cv2.imshow("mask_result", mask_result)
+    #cv2.imshow("zeros_image", zeros_image)
+    #cv2.imshow("opening_image", opening_image)
+
+    k=cv2.waitKey(30) & 0xff
+    if k == 27 :
+        break
+
+video.release()
+cv2.destroyAllWindows()