from flask import Flask, render_template, Response, request,jsonify,send_from_directory import cv2 import imutils import numpy as np from ultralytics import YOLO from collections import defaultdict import os import pandas as pd app = Flask(__name__, static_folder='assets') video_list = [] # color = (0, 255, 0) # color_red = (0, 0, 255) # thickness = 2 # font = cv2.FONT_HERSHEY_SIMPLEX # font_scale = 0.5 # # Background subtraction menggunakan MOG2 # subtracao = cv2.createBackgroundSubtractorMOG2() jumlah_kenderaan = 0 kenderaan_kiri = 0 kenderaan_kanan = 0 # Define the generate_frames function with parameters for video, threshold, and state # def generate_frames(video, threshold, stat): # model_path = "models/yolov8n.pt" # cap = cv2.VideoCapture(video) # model = YOLO(model_path) # vehicle_ids = [2, 3, 5, 7] # track_history = defaultdict(lambda: []) # up = {} # down = {} # global jumlah_kenderaan # global kenderaan_kiri # global kenderaan_kanan # jumlah_kenderaan = 0 # kenderaan_kiri = 0 # kenderaan_kanan = 0 # while True: # ret, frame = cap.read() # if not ret: # break # try: # frame = imutils.resize(frame, width=1280, height=720) # # freame_original = frame.copy() # frame_color = frame.copy() # frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # frame_gray = cv2.cvtColor(frame_gray, cv2.COLOR_GRAY2BGR) # frame_bw = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # results = model.track(frame_color, persist=True, verbose=False)[0] # bboxes = np.array(results.boxes.data.tolist(), dtype="int") # # Gambar garis pembatas untuk menghitung jumlah kendaraan yang melewati garis # cv2.line(frame_color, (0, threshold), (1280, threshold), color, thickness) # text_position = (620, threshold - 5) # Adjust the Y coordinate to place the text just above the line # cv2.putText(frame_color, "Pembatas Jalan", text_position, font, 0.7, color_red, thickness) # for box in bboxes: # x1, y1, x2, y2, track_id, score, class_id = box # cx = int((x1 + x2) / 2) # cy = int((y1 + y2) / 2) # if class_id in vehicle_ids: # class_name = results.names[int(class_id)].upper() # track = track_history[track_id] # track.append((cx, cy)) # if len(track) > 20: # track.pop(0) # points = np.hstack(track).astype("int32").reshape(-1, 1, 2) # cv2.polylines(frame_color, [points], isClosed=False, color=color, thickness=thickness) # cv2.rectangle(frame_color, (x1, y1), (x2, y2), color, thickness) # text = "ID: {} {}".format(track_id, class_name) # cv2.putText(frame_color, text, (x1, y1 - 5), font, font_scale, color, thickness) # if cy > threshold - 5 and cy < threshold + 5 and cx < 670: # down[track_id] = x1, y1, x2, y2 # if cy > threshold - 5 and cy < threshold + 5 and cx > 670: # up[track_id] = x1, y1, x2, y2 # up_text = "Kanan:{}".format(len(list(up.keys()))) # down_text = "Kiri:{}".format(len(list(down.keys()))) # kenderaan_kanan = len(list(up.keys())) # kenderaan_kiri = len(list(down.keys())) # cv2.putText(frame_color, up_text, (1150, threshold - 5), font, 0.8, color_red, thickness) # cv2.putText(frame_color, down_text, (0, threshold - 5), font, 0.8, color_red, thickness) # # Background subtraction dan deteksi kontur # grey = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Konversi frame ke citra grayscale # blur = cv2.GaussianBlur(grey, (3, 3), 5) # Reduksi noise menggunakan Gaussian Blur # img_sub = subtracao.apply(blur) # Background subtraction # dilat = cv2.dilate(img_sub, np.ones((5, 5))) # Dilasi untuk meningkatkan ketebalan objek # kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) # Kernel untuk operasi morfologi # dilatada = cv2.morphologyEx(dilat, cv2.MORPH_CLOSE, kernel) # Operasi closing untuk mengisi lubang kecil pada objek # dilatada = cv2.morphologyEx(dilatada, cv2.MORPH_CLOSE, kernel) # Operasi closing tambahan # contorno, h = cv2.findContours(dilatada, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # Deteksi kontur objek # frame_bw = cv2.cvtColor(dilatada, cv2.COLOR_GRAY2BGR) # Konversi frame grayscale ke BGR # if stat == 'color': # frame_to_encode = frame_color # elif stat == 'grayscale': # frame_to_encode = frame_gray # elif stat == 'original': # frame_to_encode = frame # else: # Assuming 'detectar' state # frame_to_encode = frame_bw # _, 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') # except Exception as e: # print("Terjadi kesalahan:", str(e)) # continue # jumlah_kenderaan = kenderaan_kiri + kenderaan_kanan # cap.release() def generate_frames2(video, threshold,stat): global jumlah_kenderaan global kenderaan_kiri global kenderaan_kanan jumlah_kenderaan = 0 kenderaan_kiri = 0 kenderaan_kanan = 0 cap = cv2.VideoCapture(video) frames_count, fps, width, height = cap.get(cv2.CAP_PROP_FRAME_COUNT), cap.get(cv2.CAP_PROP_FPS), cap.get( cv2.CAP_PROP_FRAME_WIDTH), cap.get(cv2.CAP_PROP_FRAME_HEIGHT) width = int(width) height = int(height) print(frames_count, fps, width, height) # creates a pandas data frame with the number of rows the same length as frame count df = pd.DataFrame(index=range(int(frames_count))) df.index.name = "Frames" framenumber = 0 # keeps track of current frame carscrossedup = 0 # keeps track of cars that crossed up carscrosseddown = 0 # keeps track of cars that crossed down carids = [] # blank list to add car ids caridscrossed = [] # blank list to add car ids that have crossed totalcars = 0 # keeps track of total cars fgbg = cv2.createBackgroundSubtractorMOG2() # create background subtractor # information to start saving a video file ret, frame = cap.read() # import image ratio = .5 # resize ratio image = cv2.resize(frame, (0, 0), None, ratio, ratio) # resize image width2, height2, channels = image.shape video = cv2.VideoWriter('traffic_counter.avi', cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), fps, (height2, width2), 1) while True: ret, frame = cap.read() # import image if ret: # if there is a frame continue with code image = cv2.resize(frame, (0, 0), None, ratio, ratio) # resize image gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # converts image to gray fgmask = fgbg.apply(gray) # uses the background subtraction # applies different thresholds to fgmask to try and isolate cars # just have to keep playing around with settings until cars are easily identifiable kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) # kernel to apply to the morphology closing = cv2.morphologyEx(fgmask, cv2.MORPH_CLOSE, kernel) opening = cv2.morphologyEx(closing, cv2.MORPH_OPEN, kernel) dilation = cv2.dilate(opening, kernel) retvalbin, bins = cv2.threshold(dilation, 220, 255, cv2.THRESH_BINARY) # removes the shadows # creates contours contours, hierarchy = cv2.findContours(bins, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)[-2:] # use convex hull to create polygon around contours hull = [cv2.convexHull(c) for c in contours] # draw contours cv2.drawContours(image, hull, -1, (0, 255, 0), 3) # line created to stop counting contours, needed as cars in distance become one big contour lineypos = 100 cv2.line(image, (0, lineypos), (width, lineypos), (255, 0, 0), 5) # line y position created to count contours lineypos2 = 125 cv2.line(image, (0, lineypos2), (width, lineypos2), (0, 255, 0), 5) # min area for contours in case a bunch of small noise contours are created minarea = 400 # max area for contours, can be quite large for buses maxarea = 50000 # vectors for the x and y locations of contour centroids in current frame cxx = np.zeros(len(contours)) cyy = np.zeros(len(contours)) for i in range(len(contours)): # cycles through all contours in current frame if hierarchy[0, i, 3] == -1: # using hierarchy to only count parent contours (contours not within others) area = cv2.contourArea(contours[i]) # area of contour if minarea < area < maxarea: # area threshold for contour # calculating centroids of contours cnt = contours[i] M = cv2.moments(cnt) cx = int(M['m10'] / M['m00']) cy = int(M['m01'] / M['m00']) if cy > lineypos: # filters out contours that are above line (y starts at top) # gets bounding points of contour to create rectangle # x,y is top left corner and w,h is width and height x, y, w, h = cv2.boundingRect(cnt) # creates a rectangle around contour cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 0), 2) # Prints centroid text in order to double check later on cv2.putText(image, str(cx) + "," + str(cy), (cx + 10, cy + 10), cv2.FONT_HERSHEY_SIMPLEX, .3, (0, 0, 255), 1) cv2.drawMarker(image, (cx, cy), (0, 0, 255), cv2.MARKER_STAR, markerSize=5, thickness=1, line_type=cv2.LINE_AA) # adds centroids that passed previous criteria to centroid list cxx[i] = cx cyy[i] = cy # eliminates zero entries (centroids that were not added) cxx = cxx[cxx != 0] cyy = cyy[cyy != 0] # empty list to later check which centroid indices were added to dataframe minx_index2 = [] miny_index2 = [] # maximum allowable radius for current frame centroid to be considered the same centroid from previous frame maxrad = 25 # The section below keeps track of the centroids and assigns them to old carids or new carids if len(cxx): # if there are centroids in the specified area if not carids: # if carids is empty for i in range(len(cxx)): # loops through all centroids carids.append(i) # adds a car id to the empty list carids df[str(carids[i])] = "" # adds a column to the dataframe corresponding to a carid # assigns the centroid values to the current frame (row) and carid (column) df.at[int(framenumber), str(carids[i])] = [cxx[i], cyy[i]] totalcars = carids[i] + 1 # adds one count to total cars else: # if there are already car ids dx = np.zeros((len(cxx), len(carids))) # new arrays to calculate deltas dy = np.zeros((len(cyy), len(carids))) # new arrays to calculate deltas for i in range(len(cxx)): # loops through all centroids for j in range(len(carids)): # loops through all recorded car ids # acquires centroid from previous frame for specific carid oldcxcy = df.iloc[int(framenumber - 1)][str(carids[j])] # acquires current frame centroid that doesn't necessarily line up with previous frame centroid curcxcy = np.array([cxx[i], cyy[i]]) if not oldcxcy: # checks if old centroid is empty in case car leaves screen and new car shows continue # continue to next carid else: # calculate centroid deltas to compare to current frame position later dx[i, j] = oldcxcy[0] - curcxcy[0] dy[i, j] = oldcxcy[1] - curcxcy[1] for j in range(len(carids)): # loops through all current car ids sumsum = np.abs(dx[:, j]) + np.abs(dy[:, j]) # sums the deltas wrt to car ids # finds which index carid had the min difference and this is true index correctindextrue = np.argmin(np.abs(sumsum)) minx_index = correctindextrue miny_index = correctindextrue # acquires delta values of the minimum deltas in order to check if it is within radius later on mindx = dx[minx_index, j] mindy = dy[miny_index, j] if mindx == 0 and mindy == 0 and np.all(dx[:, j] == 0) and np.all(dy[:, j] == 0): # checks if minimum value is 0 and checks if all deltas are zero since this is empty set # delta could be zero if centroid didn't move continue # continue to next carid else: # if delta values are less than maximum radius then add that centroid to that specific carid if np.abs(mindx) < maxrad and np.abs(mindy) < maxrad: # adds centroid to corresponding previously existing carid 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 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 cv2.line(image, (0, lineypos2), (width, lineypos2), (0, 0, 125), 5) caridscrossed.append(currentcarsindex[i]) # 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, 15), cv2.FONT_HERSHEY_SIMPLEX, .5, (0, 170, 0), 1) cv2.putText(image, "Kenderaan Sebelah Kanan: " + str(carscrosseddown), (0, 30), cv2.FONT_HERSHEY_SIMPLEX, .5, (0, 170, 0), 1) # cv2.putText(image, "Total Cars Detected: " + str(len(carids)), (0, 60), cv2.FONT_HERSHEY_SIMPLEX, .5, # (0, 170, 0), 1) cv2.putText(image, "Frame: " + str(framenumber) + ' dari ' + str(frames_count), (0, 45), cv2.FONT_HERSHEY_SIMPLEX, .5, (0, 170, 0), 1) cv2.putText(image, 'Waktu: ' + str(round(framenumber / fps, 2)) + ' detik dari ' + str(round(frames_count / fps, 2)) + ' detik', (0, 60), cv2.FONT_HERSHEY_SIMPLEX, .5, (0, 170, 0), 1) kenderaan_kanan = carscrosseddown kenderaan_kiri = carscrossedup jumlah_kenderaan = carscrossedup + carscrosseddown # 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(closing, (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() 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/') 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)