import cv2 as cv from imageai.Detection import VideoObjectDetection import os execution_path = os.getcwd() fourcc = cv.VideoWriter_fourcc(*'XVID') out = cv.VideoWriter('output.avi', fourcc, 29.97, (1280, 720)) def forFrame(frame_number, output_array, output_count, detected_frame): frame = detected_frame frame = cv.cvtColor(frame, cv.COLOR_BGR2RGB) out.write(frame) detector = VideoObjectDetection() detector.setModelTypeAsYOLOv3() detector.setModelPath(os.path.join(execution_path, "yolo.h5")) detector.loadModel() detector.detectObjectsFromVideo(input_file_path=os.path.join( execution_path, "piter2.mp4"), save_detected_video=False, frames_per_second=29.97, log_progress=True, per_frame_function=forFrame, return_detected_frame=True) out.release()
import os from imageai.Detection import VideoObjectDetection import cv2 camera = cv2.VideoCapture(0) execution_path = os.getcwd() detector = VideoObjectDetection() detector.setModelTypeAsYOLOv3() detector.setModelPath(os.path.join(execution_path, "yolo.h5")) detector.loadModel() video_path = detector.detectObjectsFromVideo(camera_input=camera, output_file_path=os.path.join( execution_path + "videos/", "camera_detected_1"), frames_per_second=29, log_progress=True) print(video_path)
from imageai.Detection import VideoObjectDetection import os execution_path = os.getcwd() detector = VideoObjectDetection() detector.setModelTypeAsRetinaNet() detector.setModelPath( os.path.join(execution_path , "resnet50_coco_best_v2.0.1.h5")) detector.loadModel(detection_speed="flash") custom_objects = detector.CustomObjects(person=True, bicycle=True, motorcycle=True) video_path = detector.detectCustomObjectsFromVideo(custom_objects=custom_objects, input_file_path=os.path.join(execution_path, "traffic-small.mp4"), output_file_path=os.path.join(execution_path, "traffic_small_custom_flash_detected") , frames_per_second=20, log_progress=True) print(video_path)
import os from imageai.Detection import VideoObjectDetection execution_path = os.getcwd() detector = VideoObjectDetection() detector.setModelTypeAsRetinaNet() detector.setModelPath( os.path.join(execution_path, "resnet50_coco_best_v2.0.1.h5")) detector.loadModel() video_path = detector.detectObjectsFromVideo( input_file_path=os.path.join(execution_path, "../../videos/traffic.mp4"), output_file_path=os.path.join(execution_path, "traffic_detected"), frames_per_second=20, log_progress=True) print(video_path)
from imageai.Detection import VideoObjectDetection import os execution_path = os.getcwd() detector = VideoObjectDetection() detector.setModelTypeAsRetinaNet() detector.setModelPath( os.path.join(execution_path , "Resnet.h5")) detector.loadModel("fast") video_path = detector.detectObjectsFromVideo(input_file_path=os.path.join(execution_path, "./video/NEED.mp4"), output_file_path=os.path.join(execution_path, "traffic_detected") , frames_per_second=1, log_progress=True) print(video_path)
from imageai.Detection import VideoObjectDetection import os import cv2 execution_path = os.getcwd() camera = cv2.VideoCapture(0) detector = VideoObjectDetection() detector.setModelTypeAsYOLOv3() detector.setModelPath(os.path.join(execution_path , "yolo.h5")) detector.loadModel() video_path = detector.detectObjectsFromVideo(camera_input=camera, output_file_path=os.path.join(execution_path, "camera_detected_video") , frames_per_second=15, log_progress=True, minimum_percentage_probability=30) print(video_path)
def __init__(self,setModelPath): self.execution_path = os.getcwd() self.detetor = VideoObjectDetection() self.detetor.setModelTypeAsRetinaNet() self.detetor.setModelPath(os.path.join(self.execution_path,setModelPath)) self.detetor.loadModel()
# -*- coding: utf-8 -*- """ Created on Fri Jun 5 23:05:28 2020 @author: oguzkaya """ from imageai.Detection import VideoObjectDetection import os base_path_train = 'dataset/training_set' base_path_test = 'dataset/test_set' base_path_video = 'dataset' execution_path = os.getcwd() detector = VideoObjectDetection() detector.setModelTypeAsYOLOv3() detector.setModelPath(os.path.join(base_path_video, "yolo.h5")) detector.loadModel() video_path = detector.detectObjectsFromVideo( input_file_path=os.path.join(base_path_video, "test_video.mp4"), output_file_path=os.path.join(base_path_video, "test_result_1"), frames_per_second=29, log_progress=True) print(video_path)
parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee, skis, snowboard, sports ball, kite, baseball bat, baseball glove, skateboard, surfboard, tennis racket, bottle, wine glass, cup, fork, knife, spoon, bowl, banana, apple, sandwich, orange, broccoli, carrot, hot dog, pizza, donot, cake, chair, couch, potted plant, bed, dining table, toilet, tv, laptop, mouse, remote, keyboard, cell phone, microwave, oven, toaster, sink, refrigerator, book, clock, vase, scissors, teddy bear, hair dryer, toothbrush. """ #import tensorflow.compat.v1 as tf #tf.disable_v2_behavior() from imageai.Detection import VideoObjectDetection import os #execution_path = os.getcwd() detector = VideoObjectDetection() detector.setModelTypeAsYOLOv3() detector.setModelPath( "/content/drive/My Drive/Colab Notebooks/yolo.h5") detector.loadModel() video_path = detector.detectObjectsFromVideo(input_file_path="/content/Crow fly (slow motion).mp4",output_file_path="/content/bird1.mp4" , frames_per_second=25, log_progress=True) print(video_path)
# print("Array for the outputs of each frame ", output_arrays) # print("Array for output count for unique objects in each frame : ", count_arrays) print("Output average count for unique objects in the last minute: ", average_output_count) print("\n\n\n") def forFull(output_arrays, count_arrays, average_output_count): # print("Array for the outputs of each frame ", output_arrays) # print("Array for output count for unique objects in each frame : ", count_arrays) print("Output average count for unique objects in the entire video: ", average_output_count) print("------------END OF THE VIDEO --------------") detector = VideoObjectDetection() detector.setModelTypeAsYOLOv3() detector.setModelPath(os.path.join(execution_path, "yolo.h5")) detector.loadModel(detection_speed="faster") video_path = detector.detectObjectsFromVideo( input_file_path=os.path.join(execution_path, "input.mp4"), output_file_path=os.path.join(execution_path, "upload/output_detected_1"), frames_per_second=29, per_frame_function=forFrame, per_second_function=forSeconds, per_minute_function=forMinute, video_complete_function=forFull, minimum_percentage_probability=20, log_progress=True) print(video_path)
plt.subplot(1, 2, 1) plt.title("Frame : " + str(frame_number)) plt.axis("off") plt.imshow(returned_frame, interpolation="none") plt.subplot(1, 2, 2) plt.title("Analysis: " + str(frame_number)) plt.pie(sizes, labels=labels, colors=this_colors, shadow=True, startangle=140, autopct="%1.1f%%") plt.pause(0.01) video_detector = VideoObjectDetection() video_detector.setModelTypeAsYOLOv3() video_detector.setModelPath(os.path.join(execution_path, "yolo.h5")) video_detector.loadModel() plt.show() video_detector.detectObjectsFromVideo( input_file_path=os.path.join(execution_path, "airport_security.mp4"), output_file_path=os.path.join(execution_path, "video_frame_analysis"), frames_per_second=20, per_frame_function=forFrame, minimum_percentage_probability=30, return_detected_frame=True)
fgbg = cv.bgsegm.createBackgroundSubtractorGMG() while(1): ret, frame = cap.read() fgmask = fgbg.apply(frame) fgmask = cv.morphologyEx(fgmask, cv.MORPH_OPEN, kernel) cv.imshow('frame',fgmask) k = cv.waitKey(30) & 0xff if k == 27: break cap.release() cv.destroyAllWindows() # Object Detection and Tracking from imageai.Detection import VideoObjectDetection import os execution_path = os.getcwd() detector = VideoObjectDetection() detector.setModelTypeAsRetinaNet() detector.setModelPath( os.path.join(execution_path , "resnet50_coco_best_v2.0.1.h5")) detector.loadModel() detections = detector.detectObjectsFromVideo(input_file_path=os.path.join(execution_path , "View_001_S2_L1.mp4"), output_file_path=os.path.join(execution_path , "brandnew.mp4")) for eachObject in detections: print(eachObject["name"] , " : " , eachObject["percentage_probability"] )
# plt.pause(0.01) screen.fill([0, 0, 0]) frame = cv2.cvtColor(returned_frame, cv2.COLOR_BGR2RGB) frame = np.rot90(frame) frame = np.flip(frame, 0) frame = pygame.surfarray.make_surface(frame) screen.blit(frame, (0, 0)) pygame.display.update() for event in pygame.event.get(): if event.type == KEYDOWN: sys.exit(0) video_detector = VideoObjectDetection() video_detector.setModelTypeAsYOLOv3() video_detector.setModelPath(os.path.join(execution_path, "yolo.h5")) video_detector.loadModel() #plt.show() custom = video_detector.CustomObjects(person=True, bicycle=True, car=True, motorcycle=True, airplane=True, bus=True, train=True, truck=True, boat=True, traffic_light=True,
from imageai.Detection import VideoObjectDetection import os import cv2 camera = cv2.VideoCapture(0) detector = VideoObjectDetection() detector.setModelTypeAsRetinaNet() detector.setModelPath("resnet50_coco_best_v2.0.1.h5") detector.loadModel() video_path = detector.detectObjectsFromVideo( camera_input=camera, output_file_path="camera_detected_video", frames_per_second=20, log_progress=True, minimum_percentage_probability=40)
print("Output average count for unique objects in the last second: ", average_output_count) print("------------END OF A SECOND --------------") def forMinute(minute_number, output_arrays, count_arrays, average_output_count): print("MINUTE : ", minute_number) print("Array for the outputs of each frame ", output_arrays) print("Array for output count for unique objects in each frame : ", count_arrays) print("Output average count for unique objects in the last minute: ", average_output_count) print("------------END OF A MINUTE --------------") camera = cv2.VideoCapture(0) video_detector = VideoObjectDetection() video_detector.setModelTypeAsYOLOv3() video_detector.setModelPath(os.path.join(execution_path, "yolo.h5")) video_detector.loadModel() video_detector.detectObjectsFromVideo(camera_input=camera, output_file_path=os.path.join( execution_path, "traffic_detected"), frames_per_second=10, per_second_function=forSeconds, per_frame_function=forFrame, per_minute_function=forMinute, minimum_percentage_probability=30)
from imageai.Detection import VideoObjectDetection import os execution_path = os.getcwd() detector = VideoObjectDetection() detector.setModelTypeAsYOLOv3() detector.setModelPath(os.path.join(execution_path, "yolo.h5")) detector.loadModel() video_path = detector.detectObjectsFromVideo( input_file_path=os.path.join(execution_path, "video_0.mp4"), output_file_path=os.path.join(execution_path, "moderate"), frames_per_second=30, log_progress=True) print(video_path)
this_colors.append(color_index[eachItem]) global resized if (resized == False): manager = plt.get_current_fig_manager() manager.resize(width=1000, height=500) resized = True plt.subplot(1, 2, 1) plt.title("Frame : " + str(frame_number)) plt.axis("off") plt.imshow(returned_frame, interpolation="none") plt.subplot(1, 2, 2) plt.title("Analysis: " + str(frame_number)) plt.pie(sizes, labels=labels, colors=this_colors, shadow=True, startangle=140, autopct="%1.1f%%") plt.pause(0.01) video_detector = VideoObjectDetection() video_detector.setModelTypeAsYOLOv3() video_detector.setModelPath(os.path.join(execution_path, "yolo.h5")) video_detector.loadModel() plt.show() video_detector.detectObjectsFromVideo(input_file_path=os.path.join(execution_path, "traffic.mp4"), output_file_path=os.path.join(execution_path, "video_frame_analysis") , frames_per_second=20, per_frame_function=forFrame, minimum_percentage_probability=30, return_detected_frame=True)
from imageai.Detection import VideoObjectDetection import os import time execution_path = os.getcwd() s = time.time() detector = VideoObjectDetection() detector.setModelTypeAsYOLOv3() detector.setModelPath(os.path.join(execution_path, "yolo.h5")) detector.loadModel(detection_speed="faster") custom_objects = detector.CustomObjects(person=True) video_path = detector.detectCustomObjectsFromVideo( custom_objects=custom_objects, input_file_path=os.path.join(execution_path, "1080p_test.mp4"), output_file_path=os.path.join(execution_path, "custom_detected1080"), frames_per_second=30, log_progress=True) print(video_path) e = time.time() timetaken = e - s print(timetaken)
#The needed libraries are imported from imageai.Detection import VideoObjectDetection import cv2 from IPython.display import clear_output #Start the video capture of the camera camera = cv2.VideoCapture('http://lopezrui.ddns.net/video/mjpg.cgi') #Initialise the detector, very similar to the still image detector detector = VideoObjectDetection() detector.setModelTypeAsTinyYOLOv3() detector.setModelPath('/content/drive/My Drive/yolo-tiny.h5') #The argument within loadModel increases the speed of detection, #but sacrifices a little accuracy detector.loadModel(detection_speed="flash") custom_objects = detector.CustomObjects(person=True) #Create the function that is run every second the camera is recording. def forSeconds(second_number, output_arrays, count_arrays, average_output_count): number = "" people_in_frame = str(average_output_count) #If the system doesn't detect any people in the frame, it will print 0. if people_in_frame == "{}": number = "0" else: #The number of people in the frame is prepared. #This for loop analyses every character in the output and gathers
exec_path = os.getcwd() detector = ObjectDetection() detector.setModelTypeAsRetinaNet() detector.setModelPath(os.path.join(exec_path, "resnet50_coco_best_v2.0.1.h5")) detector.loadModel() list = detector.detectCustomObjectsFromImage( input_image=os.path.join(exec_path, "objects.jpg"), output_image_path=os.path.join(exec_path, "new_objects.jpg"), minimum_percentage_probability=70, display_percentage_probability=True, display_object_name=False) from imageai.Detection import VideoObjectDetection import os execution_path = os.getcwd() detector = VideoObjectDetection() detector.setModelTypeAsYOLOv3() detector.setModelPath(os.path.join(execution_path, "yolo.h5")) detector.loadModel() video_path = detector.detectObjectsFromVideo( input_file_path=os.path.join(execution_path, "traffic.mp4"), output_file_path=os.path.join(execution_path, "traffic_detected"), frames_per_second=20, log_progress=True) print(video_path)
#This is the library that we're using for the object detection. It was created by deepquest AI. from imageai.Detection import VideoObjectDetection import os #The following line of code returns a working directory for the actual folder of the file. execution_path = os.getcwd() #Initialize the detector. detector = VideoObjectDetection() #This sets the initial object detection model instance to the pre trained "RetinaNet" model. detector.setModelTypeAsRetinaNet() #Set the model path of the model file we downloaded (the resnet model that uses the COCO database). detector.setModelPath( os.path.join(execution_path, "resnet50_coco_best_v2.0.1.h5")) #Load the model to begin processing. detector.loadModel() # This takes each frame from the video and detects each object inside of the frame. #After doing so, it parses the images together in to an output video at 20 frames per second. The output file is in AVI. #Note: It takes the input file and stores the output file in the same folder. path = detector.detectObjectsFromVideo( input_file_path=os.path.join(execution_path, "waste.mp4"), output_file_path=os.path.join(execution_path, "Detected_Output"), frames_per_second=20, log_progress=False) #For me to make sure the model goes into the right folder. print(path)
from imageai.Detection import VideoObjectDetection import os execution_path = os.getcwd() detector = VideoObjectDetection() detector.setModelTypeAsYOLOv3() detector.setModelPath(os.path.join(execution_path, "yolo.h5")) detector.loadModel() video_path = detector.detectObjectsFromVideo( input_file_path=os.path.join(execution_path, "traffic-mini.mp4"), output_file_path=os.path.join(execution_path, "traffic_mini_detected_1"), frames_per_second=29, log_progress=True) print(video_path)
plt.subplot(1, 2, 1) plt.title("Frame : " + str(frame_number)) plt.axis("off") plt.imshow(returned_frame, interpolation="none") plt.subplot(1, 2, 2) plt.title("Analysis: " + str(frame_number)) plt.pie(sizes, labels=labels, colors=this_colors, shadow=True, startangle=140, autopct="%1.1f%%") plt.pause(0.01) video_detector = VideoObjectDetection() video_detector.setModelTypeAsYOLOv3() video_detector.setModelPath(os.path.join(execution_path, "yolo.h5")) video_detector.loadModel() plt.show() video_detector.detectObjectsFromVideo(camera_input=camera, save_detected_video=False, frames_per_second=10, per_frame_function=forFrame, minimum_percentage_probability=30, return_detected_frame=True)
def detection_of_vehicles_from_video(folder1, folder2, findex): ''' Detects and saves the arrays containing bounding boxes of detected vehicles from videos of a given folder Parameters: folder1 : path of the folder containing videos folder2 : path of the folder in which arrays are required to be stored findex : index number of the first video in folder1 ''' #modifying forFrame function of ImageAI to make a list #of bounding box coordinates for vehichles detected in a #particular frame def forFrame(frame_number, output_array, output_count): bboxes = [] for i in range(len(output_array)): bboxes.append(list(output_array[i]['box_points'])) B.append(bboxes) #reading and sorting the filenames of folder1 videos = glob.glob(folder1 + '/video*.MOV') videos = natsort.natsorted(videos) #set and load ResNet Model for detection of vehicles execution_path = os.getcwd() detector = VideoObjectDetection() detector.setModelTypeAsRetinaNet() #use detector.setModelTypeAsYOLOv3() to use YOLOv3 instead of RetinaNet detector.setModelPath( os.path.join( execution_path, "/home/siddhi/Desktop/RoadCrossingAssistant_FY_Project_Data/resnet50_coco_best_v2.0.1.h5" )) #use model path of yolo.h5 if to use YOLOv3 instead of RetinaNet detector.loadModel() custom_objects = detector.CustomObjects(bicycle=True, motorcycle=True, car=True, truck=True) for video in videos: print('processing' + video) B = [] detector.detectCustomObjectsFromVideo( save_detected_video=False, custom_objects=custom_objects, input_file_path=os.path.join(execution_path, video), frames_per_second=30, per_frame_function=forFrame, minimum_percentage_probability=40) B = np.array(B) print('saving array for video' + video + '\n shape of array: ' + str(B.shape)) np.save(folder2 + '/array' + str(findex), B) findex = findex + 1
from imageai.Detection import VideoObjectDetection import os import tensorflow execution_path = os.getcwd() detector = VideoObjectDetection() detector.setModelTypeAsYOLOv3() detector.setModelPath(os.path.join(execution_path, "yolo.h5")) detector.loadModel() video_path = detector.detectObjectsFromVideo( input_file_path=os.path.join(execution_path, "ingredients.jpg"), output_file_path=os.path.join(execution_path, "vegetable_detected"), frames_per_second=29, log_progress=True) print(video_path)
from imageai.Detection import VideoObjectDetection import os execution_path = os.getcwd() detector = VideoObjectDetection() detector.setModelTypeAsRetinaNet() detector.setModelPath( os.path.join(execution_path , "../models/resnet50_coco_best_v2.0.1.h5")) detector.loadModel() custom = detector.CustomObjects(person=True, motorcycle=True, bus=True) video_path = detector.detectCustomObjectsFromVideo(custom_objects=custom, input_file_path=os.path.join(execution_path, "traffic-mini.mp4"), output_file_path=os.path.join(execution_path, "traffic-mini_detected_custom") , frames_per_second=20, log_progress=True) print(video_path)
from imageai.Detection import VideoObjectDetection import os, logging logging.basicConfig(level=logging.DEBUG, filename='app.log', filemode='w', format='%(name)s - %(levelname)s - %(message)s') execution_path = os.getcwd() detector = VideoObjectDetection() detector.setModelTypeAsTinyYOLOv3() detector.setModelPath(execution_path + "\\yolo-tiny.h5") detector.loadModel() custom_objects = detector.CustomObjects(person=True) total_seconds = 0 object_visible = 0 frame_count = 0 def forFrame(frame_number, output_array, output_count): global frame_count if "person" in output_count: frame_count += 1 logging.info("Frames with visible objects: %s", frame_count) else: logging.info("No objects visible")
pass #nos asegguramos que elvalor de cueta sea 0 iCount = 0 print("------------END OF A FRAME --------------") if (0xFF == ord('q')): sys.exit() execution_path = os.getcwd() #le decimos a opencv que usaremos la camara de cierto index camera = cv2.VideoCapture(1) #iniciamos el detector detector = VideoObjectDetection() detector.setModelTypeAsYOLOv3() detector.setModelPath(os.path.join(execution_path, "yolo.h5")) detector.loadModel() #selecionamos cuales objectos queremos detectar custom_objects = detector.CustomObjects(person=True, chair=True) #variables para desplegar el video en tiempo real fourcc = cv2.VideoWriter_fourcc(*'XVID') out = cv2.VideoWriter('output-count2.avi', fourcc, 29.0, (640, 480)) #funcion de la libreria imageai para detectar los objectos video_path = detector.detectCustomObjectsFromVideo( save_detected_video=False, return_detected_frame=True,
# -*- coding: utf-8 -*- """ Created on Wed Nov 20 00:07:55 2019 @author: Kim """ from imageai.Detection import VideoObjectDetection import imageai import os execution_path = os.getcwd() # detector = VideoObjectDetection() # detector.setModelTypeAsRetinaNet() # detector.setModelPath( os.path.join(execution_path , "models/resnet50_coco_best_v2.0.1.h5")) detector = VideoObjectDetection() detector.setModelTypeAsYOLOv3() detector.setModelPath(os.path.join(execution_path, "models/yolo.h5")) detector.loadModel(detection_speed="fast") #fast, faster, fastest, flash video_path = detector.detectObjectsFromVideo( input_file_path=os.path.join(execution_path, "videos/seoul_02_0.mp4"), output_file_path=os.path.join(execution_path, "video_out2"), frames_per_second=20, log_progress=True) print(video_path)
from imageai.Detection import VideoObjectDetection import os execution_path = os.getcwd() detector = VideoObjectDetection() detector.setModelTypeAsRetinaNet() detector.setModelPath( os.path.join(execution_path , "resnet50_coco_best_v2.1.0.h5")) detector.loadModel() custom_objects = detector.CustomObjects(person=True, bicycle=True, motorcycle=True) video_path = detector.detectObjectsFromVideo( input_file_path=os.path.join(execution_path, "Animal.mp4"), output_file_path=os.path.join(execution_path, "Animal_output"), frames_per_second=1, log_progress=True) print(video_path)
plt.axis("off") plt.imshow(returned_frame, interpolation="none") plt.subplot(1, 2, 2) plt.title("Analysis: " + str(frame_number)) plt.pie(sizes, labels=labels, colors=this_colors, shadow=True, startangle=140, autopct="%1.1f%%") plt.pause(0.01) video_detector = VideoObjectDetection() video_detector.setModelTypeAsRetinaNet() video_detector.setModelPath( os.path.join(execution_path, "../models/resnet50_coco_best_v2.0.1.h5")) video_detector.loadModel() plt.show() video_detector.detectObjectsFromVideo( input_file_path=os.path.join(execution_path, "traffic.mp4"), output_file_path=os.path.join(execution_path, "video_frame_analysis"), frames_per_second=20, per_frame_function=forFrame, minimum_percentage_probability=30, return_detected_frame=True)
def forFull(output_arrays, count_arrays, average_output_count): print("Array for the outputs of each frame ", output_arrays) print("Array for output count for unique objects in each frame : ", count_arrays) print("Output average count for unique objects in the entire video: ", average_output_count) print("------------END OF THE VIDEO --------------") """ def forSeconds(second_number, output_arrays, count_arrays, average_output_count): print("SECOND : ", second_number) print("Array for the outputs of each frame ", output_arrays) print("Array for output count for unique objects in each frame : ", count_arrays) print("Output average count for unique objects in the last second: ", average_output_count) print("------------END OF A SECOND --------------") #-------------------------------------------------------------------------------# detector = VideoObjectDetection() detector.setModelTypeAsYOLOv3() detector.setModelPath(os.path.join(execution_path,"yolo.h5")) detector.loadModel(detection_speed="fastest") plt.show() custom_objects = detector.CustomObjects(car=True,truck=True,motorcycle=True) #--------------------------------Features---------------------------------------# video_path = detector.detectCustomObjectsFromVideo( minimum_percentage_probability=50, custom_objects=custom_objects, per_second_function=forSeconds, display_percentage_probability=True, display_object_name=True, log_progress=True,
from imageai.Detection import VideoObjectDetection import os execution_path = os.getcwd() detector = VideoObjectDetection() detector.setModelTypeAsYOLOv3() detector.setModelPath( os.path.join(execution_path , "yolo.h5")) detector.loadModel() video_path = detector.detectObjectsFromVideo(input_file_path=os.path.join(execution_path, "traffic.mp4"), output_file_path=os.path.join(execution_path, "traffic_detected") , frames_per_second=20, log_progress=True) print(video_path)
from imageai.Detection import VideoObjectDetection import os execution_path = os.getcwd() detector = VideoObjectDetection() detector.setModelTypeAsRetinaNet() detector.setModelPath( os.path.join(execution_path , "resnet50_coco_best_v2.0.1.h5")) detector.loadModel() video_path = detector.detectObjectsFromVideo(input_file_path=os.path.join(execution_path, "traffic.mp4"), output_file_path=os.path.join(execution_path, "traffic_detected") , frames_per_second=20, log_progress=True) print(video_path)