pd.DataFrame 正在覆盖列，而不是将数据保存到 pd.DataFrame

Question

Here, I am attempting to create a dataframe to compare the location of an object between frames:

Pcount = []
Pcountdb = []
framenumber = 0
frames_count = 0
frames_count = self.vdo.get(cv2.CAP_PROP_FRAME_COUNT)
df = pd.DataFrame(index=range(int(frames_count))) 
if len(outputs) > 0:
    for i in range(len(outputs):
        bbox_xyxy = outputs[:,:4]
        identities = outputs[:,-1]
        sx = outputs[:,0]
        sy = outputs[:,1]
        ex = outputs[:,2]
        ey = outputs[:,3]
        cx = ((sx + ex) /2)
        cy = ((sy + ey) /2)
        ct = (cx, cy)
        cx2 = (cx.tolist())
        cy2 = (cy.tolist())
        P = identities[i]
        df[str(P.astype(int))] = ""                              
        #creates new column with an id number obtained through deepsort

        df.at[int(framenumber), str(P.astype(int))] = [cx2[i], cy2[i]]  
        #the i function from a for loop is necessary for multiple objects in the same frame

        print(df)

        if not P in Pcountdb:
            global PcountT
            Pcountdb.append(P)
            PcountT = PcountT + 1

framenumber = framenumber + 1

Edited: The script above starts with placeholders

df = pd.DataFrame... creates my dataframe with a row for each image/frame in my video

bbox_xyxy is created once my object detector has been looped over by deepsort, and deepsort has identified each detected object and identified it as an object with a location.

I then, break apart the np.arrays and calculate the center points of these objects so they can be seen as an individual point, instead of a bounding box rectangle.

Pandas takes my inputs and creates a DataFrame with the object id (in this case, 1), the center xy coordinates and places them in the row corresponding with each frame

next, we print the dataframe and view the results

print(df) returns:

                     1
Frames                
3       [614.5, 632.0]

                     1
Frames                
3                     
4       [610.5, 624.0]

                     1
Frames                
3                     
4                     
5       [603.0, 618.0]

                     1
Frames                
3                     
4                     
5                     
6       [574.0, 615.5]

                     1
Frames                
3                     
4                     
5                     
6                     
7       [564.0, 610.0]

                     1
Frames                
3                     
4                     
5                     
6                     
7                     
8       [559.0, 597.0]

The DataFrame keeps track of ONLY the most recent set of coordinates per column. If I were to produce two columns, only the last sighting of each object will be present in my dataframe (as shown above with one object, identified as 1)

I need to save the output to my pd.DataFrame = df, instead of being overwritten.

                     1
Frames                
3       [614.5, 632.0]
4       [610.5, 624.0]
5       [603.0, 618.0]
6       [574.0, 615.5]
7       [564.0, 610.0]
8       [559.0, 597.0]

So I can compare locations of these objects between frames, giving me an object count that counts objects and stores them in 2 databases, "UP and "DOWN"

Answer 1

Your DataFrame is just adding last raw because every time for loop runs you're resetting column to null. so all previous values are erased. by looking at your code I can see that as your code doesn't require to be in for loop.

solution :

Pcount = []
Pcountdb = []
framenumber = 0
frames_count = 0
frames_count = self.vdo.get(cv2.CAP_PROP_FRAME_COUNT)
df = pd.DataFrame(index=range(int(frames_count))) 
if len(outputs) > 0:
    bbox_xyxy = outputs[:,:4]
    identities = outputs[:,-1]
    sx = outputs[:,0]
    sy = outputs[:,1]
    ex = outputs[:,2]
    ey = outputs[:,3]
    cx = ((sx + ex) /2)
    cy = ((sy + ey) /2)
    ct = (cx, cy)
    cx2 = (cx.tolist())
    cy2 = (cy.tolist())
    P = identities[i]
    df[str(P.astype(int))] = ""
    for i in range(len(outputs):
        df.at[int(framenumber), str(P.astype(int))] = [cx2[i], cy2[i]]
        print(df)

Hope this works.

Answer 2

for i in range(len(outputs)):
                    P = identities[i]

                    if not P in Pcountdb:
                        df[str(P.astype(int))] = ""
                        global PcountT
                        Pcountdb.append(P)
                        PcountT = PcountT + 1
                    else:
                        if P in Pcountdb:
                            df.at[int(framenumber), str(P.astype(int))] = [cx2[i], cy2[i]]


[222 rows x 1 columns]
                     1
 Frames                
 4       [610.5, 624.0]
 5       [603.0, 618.0]
 6       [574.0, 615.5]
 7       [564.0, 610.0]
 8       [559.0, 597.0]
 ...                ...
 226     [640.5, 518.5]
 227     [643.0, 525.0]
 228     [646.0, 529.5]
 229     [647.5, 529.5]
 230     [650.5, 531.5]

Thank you @Adarsh for your response, you were right, my columns were being overwritten because i was creating them from a loop.

I took the line df[str(P.astype(int))] = "", where columns are created and ran it with an exclusive circumstance.