558 lines
25 KiB
Python
558 lines
25 KiB
Python
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from flask import Flask, render_template, Response, request,jsonify,send_from_directory
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import cv2
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import imutils
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import numpy as np
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from ultralytics import YOLO
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from collections import defaultdict
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import os
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import pandas as pd
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app = Flask(__name__, static_folder='assets')
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video_list = []
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# color = (0, 255, 0)
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# color_red = (0, 0, 255)
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# thickness = 2
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# font = cv2.FONT_HERSHEY_SIMPLEX
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# font_scale = 0.5
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# # Background subtraction menggunakan MOG2
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# subtracao = cv2.createBackgroundSubtractorMOG2()
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jumlah_kenderaan = 0
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kenderaan_kiri = 0
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kenderaan_kanan = 0
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# Define the generate_frames function with parameters for video, threshold, and state
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# def generate_frames(video, threshold, stat):
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# model_path = "models/yolov8n.pt"
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# cap = cv2.VideoCapture(video)
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# model = YOLO(model_path)
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# vehicle_ids = [2, 3, 5, 7]
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# track_history = defaultdict(lambda: [])
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# up = {}
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# down = {}
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# global jumlah_kenderaan
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# global kenderaan_kiri
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# global kenderaan_kanan
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# jumlah_kenderaan = 0
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# kenderaan_kiri = 0
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# kenderaan_kanan = 0
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# while True:
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# ret, frame = cap.read()
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# if not ret:
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# break
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# try:
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# frame = imutils.resize(frame, width=1280, height=720)
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# # freame_original = frame.copy()
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# frame_color = frame.copy()
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# frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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# frame_gray = cv2.cvtColor(frame_gray, cv2.COLOR_GRAY2BGR)
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# frame_bw = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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# results = model.track(frame_color, persist=True, verbose=False)[0]
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# bboxes = np.array(results.boxes.data.tolist(), dtype="int")
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# # Gambar garis pembatas untuk menghitung jumlah kendaraan yang melewati garis
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# cv2.line(frame_color, (0, threshold), (1280, threshold), color, thickness)
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# text_position = (620, threshold - 5) # Adjust the Y coordinate to place the text just above the line
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# cv2.putText(frame_color, "Pembatas Jalan", text_position, font, 0.7, color_red, thickness)
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# for box in bboxes:
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# x1, y1, x2, y2, track_id, score, class_id = box
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# cx = int((x1 + x2) / 2)
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# cy = int((y1 + y2) / 2)
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# if class_id in vehicle_ids:
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# class_name = results.names[int(class_id)].upper()
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# track = track_history[track_id]
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# track.append((cx, cy))
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# if len(track) > 20:
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# track.pop(0)
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# points = np.hstack(track).astype("int32").reshape(-1, 1, 2)
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# cv2.polylines(frame_color, [points], isClosed=False, color=color, thickness=thickness)
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# cv2.rectangle(frame_color, (x1, y1), (x2, y2), color, thickness)
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# text = "ID: {} {}".format(track_id, class_name)
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# cv2.putText(frame_color, text, (x1, y1 - 5), font, font_scale, color, thickness)
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# if cy > threshold - 5 and cy < threshold + 5 and cx < 670:
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# down[track_id] = x1, y1, x2, y2
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# if cy > threshold - 5 and cy < threshold + 5 and cx > 670:
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# up[track_id] = x1, y1, x2, y2
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# up_text = "Kanan:{}".format(len(list(up.keys())))
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# down_text = "Kiri:{}".format(len(list(down.keys())))
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# kenderaan_kanan = len(list(up.keys()))
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# kenderaan_kiri = len(list(down.keys()))
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# cv2.putText(frame_color, up_text, (1150, threshold - 5), font, 0.8, color_red, thickness)
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# cv2.putText(frame_color, down_text, (0, threshold - 5), font, 0.8, color_red, thickness)
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# # Background subtraction dan deteksi kontur
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# grey = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Konversi frame ke citra grayscale
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# blur = cv2.GaussianBlur(grey, (3, 3), 5) # Reduksi noise menggunakan Gaussian Blur
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# img_sub = subtracao.apply(blur) # Background subtraction
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# dilat = cv2.dilate(img_sub, np.ones((5, 5))) # Dilasi untuk meningkatkan ketebalan objek
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# kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) # Kernel untuk operasi morfologi
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# dilatada = cv2.morphologyEx(dilat, cv2.MORPH_CLOSE, kernel) # Operasi closing untuk mengisi lubang kecil pada objek
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# dilatada = cv2.morphologyEx(dilatada, cv2.MORPH_CLOSE, kernel) # Operasi closing tambahan
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# contorno, h = cv2.findContours(dilatada, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # Deteksi kontur objek
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# frame_bw = cv2.cvtColor(dilatada, cv2.COLOR_GRAY2BGR) # Konversi frame grayscale ke BGR
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# if stat == 'color':
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# frame_to_encode = frame_color
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# elif stat == 'grayscale':
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# frame_to_encode = frame_gray
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# elif stat == 'original':
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# frame_to_encode = frame
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# else: # Assuming 'detectar' state
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# frame_to_encode = frame_bw
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# _, buffer = cv2.imencode('.jpg', frame_to_encode)
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# frame_bytes = buffer.tobytes()
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# yield (b'--frame\r\n'
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# b'Content-Type: image/jpeg\r\n\r\n' + frame_bytes + b'\r\n')
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# except Exception as e:
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# print("Terjadi kesalahan:", str(e))
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# continue
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# jumlah_kenderaan = kenderaan_kiri + kenderaan_kanan
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# cap.release()
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def generate_frames2(video, threshold,stat):
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global jumlah_kenderaan
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global kenderaan_kiri
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global kenderaan_kanan
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jumlah_kenderaan = 0
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kenderaan_kiri = 0
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kenderaan_kanan = 0
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cap = cv2.VideoCapture(video)
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frames_count, fps, width, height = cap.get(cv2.CAP_PROP_FRAME_COUNT), cap.get(cv2.CAP_PROP_FPS), cap.get(
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cv2.CAP_PROP_FRAME_WIDTH), cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
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width = int(width)
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height = int(height)
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print(frames_count, fps, width, height)
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# creates a pandas data frame with the number of rows the same length as frame count
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df = pd.DataFrame(index=range(int(frames_count)))
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df.index.name = "Frames"
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framenumber = 0 # keeps track of current frame
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carscrossedup = 0 # keeps track of cars that crossed up
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carscrosseddown = 0 # keeps track of cars that crossed down
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carids = [] # blank list to add car ids
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caridscrossed = [] # blank list to add car ids that have crossed
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totalcars = 0 # keeps track of total cars
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fgbg = cv2.createBackgroundSubtractorMOG2() # create background subtractor
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# information to start saving a video file
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ret, frame = cap.read() # import image
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ratio = .5 # resize ratio
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image = cv2.resize(frame, (0, 0), None, ratio, ratio) # resize image
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width2, height2, channels = image.shape
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# video = cv2.VideoWriter('traffic_counter.avi', cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), fps, (height2, width2), 1)
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while True:
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ret, frame = cap.read() # import image
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if ret: # if there is a frame continue with code
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image = cv2.resize(frame, (0, 0), None, ratio, ratio) # resize image
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # converts image to gray
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fgmask = fgbg.apply(gray) # uses the background subtraction
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# applies different thresholds to fgmask to try and isolate cars
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# just have to keep playing around with settings until cars are easily identifiable
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kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) # kernel to apply to the morphology
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closing = cv2.morphologyEx(fgmask, cv2.MORPH_CLOSE, kernel)
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opening = cv2.morphologyEx(closing, cv2.MORPH_OPEN, kernel)
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dilation = cv2.dilate(opening, kernel)
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retvalbin, bins = cv2.threshold(dilation, 220, 255, cv2.THRESH_BINARY) # removes the shadows
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# creates contours
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contours, hierarchy = cv2.findContours(bins, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)[-2:]
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# use convex hull to create polygon around contours
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hull = [cv2.convexHull(c) for c in contours]
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# draw contours
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# cv2.drawContours(image, hull, -1, (0, 255, 0), 3)
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# line created to stop counting contours, needed as cars in distance become one big contour
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lineypos = 125
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# cv2.line(image, (0, lineypos), (width, lineypos), (255, 0, 0), 5)
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# line y position created to count contours
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lineypos2 = 150
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cv2.line(image, (0, lineypos2), (width, lineypos2), (0, 255, 0), 5)
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# min area for contours in case a bunch of small noise contours are created
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minarea = 175
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# max area for contours, can be quite large for buses
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maxarea = 50000
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# vectors for the x and y locations of contour centroids in current frame
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cxx = np.zeros(len(contours))
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cyy = np.zeros(len(contours))
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for i in range(len(contours)): # cycles through all contours in current frame
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if hierarchy[0, i, 3] == -1: # using hierarchy to only count parent contours (contours not within others)
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area = cv2.contourArea(contours[i]) # area of contour
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if minarea < area < maxarea: # area threshold for contour
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# calculating centroids of contours
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cnt = contours[i]
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M = cv2.moments(cnt)
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cx = int(M['m10'] / M['m00'])
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cy = int(M['m01'] / M['m00'])
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if cy > lineypos: # filters out contours that are above line (y starts at top)
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# gets bounding points of contour to create rectangle
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# x,y is top left corner and w,h is width and height
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x, y, w, h = cv2.boundingRect(cnt)
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# creates a rectangle around contour
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cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 0), 2)
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# Prints centroid text in order to double check later on
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cv2.putText(image, str(cx) + "," + str(cy), (cx + 10, cy + 10), cv2.FONT_HERSHEY_SIMPLEX,
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.3, (0, 0, 255), 1)
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cv2.drawMarker(image, (cx, cy), (0, 0, 255), cv2.MARKER_STAR, markerSize=5, thickness=1,
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line_type=cv2.LINE_AA)
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# adds centroids that passed previous criteria to centroid list
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cxx[i] = cx
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cyy[i] = cy
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# eliminates zero entries (centroids that were not added)
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cxx = cxx[cxx != 0]
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cyy = cyy[cyy != 0]
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# empty list to later check which centroid indices were added to dataframe
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minx_index2 = []
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miny_index2 = []
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# maximum allowable radius for current frame centroid to be considered the same centroid from previous frame
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maxrad = 25
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# The section below keeps track of the centroids and assigns them to old carids or new carids
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if len(cxx): # if there are centroids in the specified area
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if not carids: # if carids is empty
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for i in range(len(cxx)): # loops through all centroids
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carids.append(i) # adds a car id to the empty list carids
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df[str(carids[i])] = "" # adds a column to the dataframe corresponding to a carid
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# assigns the centroid values to the current frame (row) and carid (column)
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df.at[int(framenumber), str(carids[i])] = [cxx[i], cyy[i]]
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totalcars = carids[i] + 1 # adds one count to total cars
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else: # if there are already car ids
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dx = np.zeros((len(cxx), len(carids))) # new arrays to calculate deltas
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dy = np.zeros((len(cyy), len(carids))) # new arrays to calculate deltas
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for i in range(len(cxx)): # loops through all centroids
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for j in range(len(carids)): # loops through all recorded car ids
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# acquires centroid from previous frame for specific carid
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oldcxcy = df.iloc[int(framenumber - 1)][str(carids[j])]
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# acquires current frame centroid that doesn't necessarily line up with previous frame centroid
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curcxcy = np.array([cxx[i], cyy[i]])
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if not oldcxcy: # checks if old centroid is empty in case car leaves screen and new car shows
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continue # continue to next carid
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else: # calculate centroid deltas to compare to current frame position later
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dx[i, j] = oldcxcy[0] - curcxcy[0]
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dy[i, j] = oldcxcy[1] - curcxcy[1]
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for j in range(len(carids)): # loops through all current car ids
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sumsum = np.abs(dx[:, j]) + np.abs(dy[:, j]) # sums the deltas wrt to car ids
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# finds which index carid had the min difference and this is true index
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correctindextrue = np.argmin(np.abs(sumsum))
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minx_index = correctindextrue
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miny_index = correctindextrue
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# acquires delta values of the minimum deltas in order to check if it is within radius later on
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mindx = dx[minx_index, j]
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mindy = dy[miny_index, j]
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if mindx == 0 and mindy == 0 and np.all(dx[:, j] == 0) and np.all(dy[:, j] == 0):
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# checks if minimum value is 0 and checks if all deltas are zero since this is empty set
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# delta could be zero if centroid didn't move
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continue # continue to next carid
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else:
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# if delta values are less than maximum radius then add that centroid to that specific carid
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if np.abs(mindx) < maxrad and np.abs(mindy) < maxrad:
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# adds centroid to corresponding previously existing carid
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||
|
|
df.at[int(framenumber), str(carids[j])] = [cxx[minx_index], cyy[miny_index]]
|
||
|
|
minx_index2.append(minx_index) # appends all the indices that were added to previous carids
|
||
|
|
miny_index2.append(miny_index)
|
||
|
|
|
||
|
|
for i in range(len(cxx)): # loops through all centroids
|
||
|
|
|
||
|
|
# if centroid is not in the minindex list then another car needs to be added
|
||
|
|
if i not in minx_index2 and miny_index2:
|
||
|
|
|
||
|
|
df[str(totalcars)] = "" # create another column with total cars
|
||
|
|
totalcars = totalcars + 1 # adds another total car the count
|
||
|
|
t = totalcars - 1 # t is a placeholder to total cars
|
||
|
|
carids.append(t) # append to list of car ids
|
||
|
|
df.at[int(framenumber), str(t)] = [cxx[i], cyy[i]] # add centroid to the new car id
|
||
|
|
|
||
|
|
elif curcxcy[0] and not oldcxcy and not minx_index2 and not miny_index2:
|
||
|
|
# checks if current centroid exists but previous centroid does not
|
||
|
|
# new car to be added in case minx_index2 is empty
|
||
|
|
|
||
|
|
df[str(totalcars)] = "" # create another column with total cars
|
||
|
|
totalcars = totalcars + 1 # adds another total car the count
|
||
|
|
t = totalcars - 1 # t is a placeholder to total cars
|
||
|
|
carids.append(t) # append to list of car ids
|
||
|
|
df.at[int(framenumber), str(t)] = [cxx[i], cyy[i]] # add centroid to the new car id
|
||
|
|
|
||
|
|
# The section below labels the centroids on screen
|
||
|
|
|
||
|
|
currentcars = 0 # current cars on screen
|
||
|
|
currentcarsindex = [] # current cars on screen carid index
|
||
|
|
|
||
|
|
for i in range(len(carids)): # loops through all carids
|
||
|
|
|
||
|
|
if df.at[int(framenumber), str(carids[i])] != '':
|
||
|
|
# checks the current frame to see which car ids are active
|
||
|
|
# by checking in centroid exists on current frame for certain car id
|
||
|
|
|
||
|
|
currentcars = currentcars + 1 # adds another to current cars on screen
|
||
|
|
currentcarsindex.append(i) # adds car ids to current cars on screen
|
||
|
|
|
||
|
|
for i in range(currentcars): # loops through all current car ids on screen
|
||
|
|
|
||
|
|
# grabs centroid of certain carid for current frame
|
||
|
|
curcent = df.iloc[int(framenumber)][str(carids[currentcarsindex[i]])]
|
||
|
|
|
||
|
|
# grabs centroid of certain carid for previous frame
|
||
|
|
oldcent = df.iloc[int(framenumber - 1)][str(carids[currentcarsindex[i]])]
|
||
|
|
|
||
|
|
if curcent: # if there is a current centroid
|
||
|
|
|
||
|
|
# On-screen text for current centroid
|
||
|
|
cv2.putText(image, "Centroid" + str(curcent[0]) + "," + str(curcent[1]),
|
||
|
|
(int(curcent[0]), int(curcent[1])), cv2.FONT_HERSHEY_SIMPLEX, .5, (0, 255, 255), 2)
|
||
|
|
|
||
|
|
# cv2.putText(image, "ID:" + str(carids[currentcarsindex[i]]), (int(curcent[0]), int(curcent[1] - 15)),
|
||
|
|
# cv2.FONT_HERSHEY_SIMPLEX, .5, (0, 255, 255), 2)
|
||
|
|
|
||
|
|
cv2.drawMarker(image, (int(curcent[0]), int(curcent[1])), (0, 0, 255), cv2.MARKER_STAR, markerSize=5,
|
||
|
|
thickness=1, line_type=cv2.LINE_AA)
|
||
|
|
|
||
|
|
if oldcent: # checks if old centroid exists
|
||
|
|
# adds radius box from previous centroid to current centroid for visualization
|
||
|
|
xstart = oldcent[0] - maxrad
|
||
|
|
ystart = oldcent[1] - maxrad
|
||
|
|
xwidth = oldcent[0] + maxrad
|
||
|
|
yheight = oldcent[1] + maxrad
|
||
|
|
cv2.rectangle(image, (int(xstart), int(ystart)), (int(xwidth), int(yheight)), (0, 125, 0), 1)
|
||
|
|
|
||
|
|
# checks if old centroid is on or below line and curcent is on or above line
|
||
|
|
# to count cars and that car hasn't been counted yet
|
||
|
|
if oldcent[1] >= lineypos2 and curcent[1] <= lineypos2 and carids[
|
||
|
|
currentcarsindex[i]] not in caridscrossed:
|
||
|
|
|
||
|
|
carscrossedup = carscrossedup + 1
|
||
|
|
kenderaan_kiri = carscrossedup
|
||
|
|
cv2.line(image, (0, lineypos2), (width, lineypos2), (0, 0, 255), 5)
|
||
|
|
caridscrossed.append(
|
||
|
|
currentcarsindex[i]) # adds car id to list of count cars to prevent double counting
|
||
|
|
|
||
|
|
# checks if old centroid is on or above line and curcent is on or below line
|
||
|
|
# to count cars and that car hasn't been counted yet
|
||
|
|
elif oldcent[1] <= lineypos2 and curcent[1] >= lineypos2 and carids[
|
||
|
|
currentcarsindex[i]] not in caridscrossed:
|
||
|
|
|
||
|
|
carscrosseddown = carscrosseddown + 1
|
||
|
|
kenderaan_kanan = carscrosseddown
|
||
|
|
cv2.line(image, (0, lineypos2), (width, lineypos2), (0, 0, 125), 5)
|
||
|
|
caridscrossed.append(currentcarsindex[i])
|
||
|
|
jumlah_kenderaan = carscrossedup + carscrosseddown
|
||
|
|
|
||
|
|
# Top left hand corner on-screen text
|
||
|
|
#cv2.rectangle(image, (0, 0), (250, 100), (255, 0, 0), -1) # background rectangle for on-screen text
|
||
|
|
|
||
|
|
cv2.putText(image, "Kenderaan Sebelah Kiri: " + str(carscrossedup), (0, 20), cv2.FONT_HERSHEY_SIMPLEX, .7, (255,255,255),
|
||
|
|
4)
|
||
|
|
|
||
|
|
cv2.putText(image, "Kenderaan Sebelah Kanan: " + str(carscrosseddown), (0, 45), cv2.FONT_HERSHEY_SIMPLEX, .7,
|
||
|
|
(255,255,255), 4)
|
||
|
|
|
||
|
|
# cv2.putText(image, "Total Cars Detected: " + str(len(carids)), (0, 60), cv2.FONT_HERSHEY_SIMPLEX, .5,
|
||
|
|
# (255,255,255), 1)
|
||
|
|
|
||
|
|
cv2.putText(image, "Frame: " + str(framenumber) + ' dari ' + str(frames_count), (0, 60), cv2.FONT_HERSHEY_SIMPLEX,
|
||
|
|
.5, (255,255,255), 1)
|
||
|
|
|
||
|
|
cv2.putText(image, 'Waktu: ' + str(round(framenumber / fps, 2)) + ' detik dari ' + str(round(frames_count / fps, 2))
|
||
|
|
+ ' detik', (0, 75), cv2.FONT_HERSHEY_SIMPLEX, .5, (255,255,255), 1)
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
# displays images and transformations and resize to 1280x720
|
||
|
|
# cv2.imshow("countours", image)
|
||
|
|
# cv2.moveWindow("countours", 0, 0)
|
||
|
|
if stat == 'color':
|
||
|
|
# frame_to_encode = frame
|
||
|
|
# resize to 1280x720
|
||
|
|
frame_to_encode = cv2.resize(image, (1280, 720))
|
||
|
|
|
||
|
|
|
||
|
|
# cv2.imshow("fgmask", fgmask)
|
||
|
|
# cv2.moveWindow("fgmask", int(width * ratio), 0)
|
||
|
|
elif stat == 'grayscale':
|
||
|
|
# frame_to_encode = gray
|
||
|
|
frame_to_encode = cv2.resize(gray, (1280, 720))
|
||
|
|
|
||
|
|
# cv2.imshow("closing", closing)
|
||
|
|
# cv2.moveWindow("closing", width, 0)
|
||
|
|
elif stat == 'detectar':
|
||
|
|
# frame_to_encode = closing
|
||
|
|
frame_to_encode = cv2.resize(bins, (1280, 720))
|
||
|
|
else :
|
||
|
|
# frame_to_encode = opening
|
||
|
|
frame_to_encode = cv2.resize(frame, (1280, 720))
|
||
|
|
|
||
|
|
_, buffer = cv2.imencode('.jpg', frame_to_encode)
|
||
|
|
frame_bytes = buffer.tobytes()
|
||
|
|
|
||
|
|
yield (b'--frame\r\n'
|
||
|
|
b'Content-Type: image/jpeg\r\n\r\n' + frame_bytes + b'\r\n')
|
||
|
|
|
||
|
|
# cv2.imshow("opening", opening)
|
||
|
|
# cv2.moveWindow("opening", 0, int(height * ratio))
|
||
|
|
|
||
|
|
# cv2.imshow("dilation", dilation)
|
||
|
|
# cv2.moveWindow("dilation", int(width * ratio), int(height * ratio))
|
||
|
|
|
||
|
|
# cv2.imshow("binary", bins)
|
||
|
|
# cv2.moveWindow("binary", width, int(height * ratio))
|
||
|
|
|
||
|
|
# video.write(image) # save the current image to video file from earlier
|
||
|
|
|
||
|
|
# adds to framecount
|
||
|
|
framenumber = framenumber + 1
|
||
|
|
|
||
|
|
k = cv2.waitKey(int(1000/fps)) & 0xff # int(1000/fps) is normal speed since waitkey is in ms
|
||
|
|
if k == 27:
|
||
|
|
break
|
||
|
|
|
||
|
|
else: # if video is finished then break loop
|
||
|
|
|
||
|
|
break
|
||
|
|
|
||
|
|
cap.release()
|
||
|
|
cv2.destroyAllWindows()
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
def update_video_list():
|
||
|
|
global video_list
|
||
|
|
# add "video/" to the video_list and only take video extensions
|
||
|
|
video_list = [f"video/{f}" for f in os.listdir("video") if f.endswith(".mp4")]
|
||
|
|
|
||
|
|
@app.route('/')
|
||
|
|
def index():
|
||
|
|
update_video_list()
|
||
|
|
print("video_list:", video_list)
|
||
|
|
video = request.args.get('video', 'video/video.mp4')
|
||
|
|
threshold = int(request.args.get('threshold', 450))
|
||
|
|
# Pass the video file path and threshold value to the template
|
||
|
|
return render_template('index.html', video=video, threshold=threshold, video_list=video_list)
|
||
|
|
|
||
|
|
def video_feed():
|
||
|
|
# Get the video file path, threshold value, and state from the URL parameters
|
||
|
|
video = request.args.get('video')
|
||
|
|
threshold = int(request.args.get('threshold', 450))
|
||
|
|
stat = request.args.get('stat', 'color') # Default to 'color' if state is not specified
|
||
|
|
# Return the response with the generator function
|
||
|
|
print("ini semua variable:", video, threshold, stat)
|
||
|
|
return Response(generate_frames2(video, threshold, stat), mimetype='multipart/x-mixed-replace; boundary=frame')
|
||
|
|
|
||
|
|
|
||
|
|
@app.route('/video_list')
|
||
|
|
def video_list():
|
||
|
|
update_video_list()
|
||
|
|
return render_template('video_list.html', video_list=video_list)
|
||
|
|
|
||
|
|
@app.route('/videos/<path:video>')
|
||
|
|
def video(video):
|
||
|
|
return send_from_directory('', video)
|
||
|
|
|
||
|
|
# Add route for the video feed
|
||
|
|
app.add_url_rule('/video_feed', 'video_feed', video_feed)
|
||
|
|
|
||
|
|
@app.route('/check_jumlah_kenderaan', methods=['GET'])
|
||
|
|
def check_jumlah_kenderaan():
|
||
|
|
global jumlah_kenderaan
|
||
|
|
global kenderaan_kiri
|
||
|
|
global kenderaan_kanan
|
||
|
|
return jsonify({'jumlah_kenderaan': jumlah_kenderaan, 'kenderaan_kiri': kenderaan_kiri, 'kenderaan_kanan': kenderaan_kanan})
|
||
|
|
|
||
|
|
UPLOAD_FOLDER = 'video'
|
||
|
|
@app.route('/upload', methods=['POST'])
|
||
|
|
def upload_file():
|
||
|
|
file = request.files['file']
|
||
|
|
|
||
|
|
if file.filename == '':
|
||
|
|
return jsonify({'status': False, 'message': 'No file selected'})
|
||
|
|
|
||
|
|
if file:
|
||
|
|
filename = file.filename
|
||
|
|
file.save(os.path.join(UPLOAD_FOLDER, filename))
|
||
|
|
return jsonify({'status': True, 'message': 'File uploaded successfully', 'filename': filename})
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
app.run(debug=True)
|