def train_project(subscription_key): trainer = CustomVisionTrainingClient(subscription_key, endpoint=ENDPOINT) # Create a new project print ("Creating project...") project = trainer.create_project(SAMPLE_PROJECT_NAME) # Make two tags in the new project hemlock_tag = trainer.create_tag(project.id, "Hemlock") cherry_tag = trainer.create_tag(project.id, "Japanese Cherry") print ("Adding images...") hemlock_dir = os.path.join(IMAGES_FOLDER, "Hemlock") for image in os.listdir(hemlock_dir): with open(os.path.join(hemlock_dir, image), mode="rb") as img_data: trainer.create_images_from_data(project.id, img_data.read(), [ hemlock_tag.id ]) cherry_dir = os.path.join(IMAGES_FOLDER, "Japanese Cherry") for image in os.listdir(cherry_dir): with open(os.path.join(cherry_dir, image), mode="rb") as img_data: trainer.create_images_from_data(project.id, img_data.read(), [ cherry_tag.id ]) print ("Training...") iteration = trainer.train_project(project.id) while (iteration.status == "Training"): iteration = trainer.get_iteration(project.id, iteration.id) print ("Training status: " + iteration.status) time.sleep(1) # The iteration is now trained. Make it the default project endpoint trainer.update_iteration(project.id, iteration.id, is_default=True) print ("Done!") return project
with open(os.path.join(elephant_dir, image), mode="rb") as img_data: trainer.create_images_from_data(project.id, img_data.read(), [elephant_tag.id]) print("added elephants") # Add all images in Giraffe folder to your project with the tag "giraffe" IMAGES_FOLDER = os.path.join(os.path.dirname(os.path.realpath(__file__)), "ElephantGiraffeTrainingImages") giraffe_dir = os.path.join(IMAGES_FOLDER, "Giraffe") for image in os.listdir(giraffe_dir): with open(os.path.join(giraffe_dir, image), mode="rb") as img_data: trainer.create_images_from_data(project.id, img_data.read(), [giraffe_tag.id]) print("added giraffes") # Train the model print("Training...") iteration = trainer.train_project(project.id) while (iteration.status != "Completed"): iteration = trainer.get_iteration(project.id, iteration.id) print("Training status: " + iteration.status) time.sleep(1) # The iteration is now trained. Make it the default project endpoint trainer.update_iteration(project.id, iteration.id, is_default=True) print("Done!") input() # Now there is a trained endpoint that can be used to make a prediction # look at the code in MakePrediction.py to see how you send a new image to the trained model to get a prediction
def train_project(training_key): trainer = CustomVisionTrainingClient(training_key, endpoint=ENDPOINT) # Find the object detection domain obj_detection_domain = next(domain for domain in trainer.get_domains() if domain.type == "ObjectDetection") # Create a new project print("Creating project...") project = trainer.create_project("My Detection Project", domain_id=obj_detection_domain.id) # Make two tags in the new project fork_tag = trainer.create_tag(project.id, "fork") scissors_tag = trainer.create_tag(project.id, "scissors") fork_image_regions = { "fork_1": [0.145833328, 0.3509314, 0.5894608, 0.238562092], "fork_2": [0.294117659, 0.216944471, 0.534313738, 0.5980392], "fork_3": [0.09191177, 0.0682516545, 0.757352948, 0.6143791], "fork_4": [0.254901975, 0.185898721, 0.5232843, 0.594771266], "fork_5": [0.2365196, 0.128709182, 0.5845588, 0.71405226], "fork_6": [0.115196079, 0.133611143, 0.676470637, 0.6993464], "fork_7": [0.164215669, 0.31008172, 0.767156839, 0.410130739], "fork_8": [0.118872553, 0.318251669, 0.817401946, 0.225490168], "fork_9": [0.18259804, 0.2136765, 0.6335784, 0.643790841], "fork_10": [0.05269608, 0.282303959, 0.8088235, 0.452614367], "fork_11": [0.05759804, 0.0894935, 0.9007353, 0.3251634], "fork_12": [0.3345588, 0.07315363, 0.375, 0.9150327], "fork_13": [0.269607842, 0.194068655, 0.4093137, 0.6732026], "fork_14": [0.143382356, 0.218578458, 0.7977941, 0.295751631], "fork_15": [0.19240196, 0.0633497, 0.5710784, 0.8398692], "fork_16": [0.140931368, 0.480016381, 0.6838235, 0.240196079], "fork_17": [0.305147052, 0.2512582, 0.4791667, 0.5408496], "fork_18": [0.234068632, 0.445702642, 0.6127451, 0.344771236], "fork_19": [0.219362751, 0.141781077, 0.5919118, 0.6683006], "fork_20": [0.180147052, 0.239820287, 0.6887255, 0.235294119] } scissors_image_regions = { "scissors_1": [0.4007353, 0.194068655, 0.259803921, 0.6617647], "scissors_2": [0.426470578, 0.185898721, 0.172794119, 0.5539216], "scissors_3": [0.289215684, 0.259428144, 0.403186262, 0.421568632], "scissors_4": [0.343137264, 0.105833367, 0.332107842, 0.8055556], "scissors_5": [0.3125, 0.09766343, 0.435049027, 0.71405226], "scissors_6": [0.379901975, 0.24308826, 0.32107842, 0.5718954], "scissors_7": [0.341911763, 0.20714055, 0.3137255, 0.6356209], "scissors_8": [0.231617644, 0.08459154, 0.504901946, 0.8480392], "scissors_9": [0.170343131, 0.332957536, 0.767156839, 0.403594762], "scissors_10": [0.204656869, 0.120539248, 0.5245098, 0.743464053], "scissors_11": [0.05514706, 0.159754932, 0.799019635, 0.730392158], "scissors_12": [0.265931368, 0.169558853, 0.5061275, 0.606209159], "scissors_13": [0.241421565, 0.184264734, 0.448529422, 0.6830065], "scissors_14": [0.05759804, 0.05027781, 0.75, 0.882352948], "scissors_15": [0.191176474, 0.169558853, 0.6936275, 0.6748366], "scissors_16": [0.1004902, 0.279036, 0.6911765, 0.477124184], "scissors_17": [0.2720588, 0.131977156, 0.4987745, 0.6911765], "scissors_18": [0.180147052, 0.112369314, 0.6262255, 0.6666667], "scissors_19": [0.333333343, 0.0274019931, 0.443627447, 0.852941155], "scissors_20": [0.158088237, 0.04047389, 0.6691176, 0.843137264] } # Go through the data table above and create the images print("Adding images...") tagged_images_with_regions = [] for file_name in fork_image_regions.keys(): x, y, w, h = fork_image_regions[file_name] regions = [ Region(tag_id=fork_tag.id, left=x, top=y, width=w, height=h) ] with open(os.path.join(IMAGES_FOLDER, "fork", file_name + ".jpg"), mode="rb") as image_contents: tagged_images_with_regions.append( ImageFileCreateEntry(name=file_name, contents=image_contents.read(), regions=regions)) for file_name in scissors_image_regions.keys(): x, y, w, h = scissors_image_regions[file_name] regions = [ Region(tag_id=scissors_tag.id, left=x, top=y, width=w, height=h) ] with open(os.path.join(IMAGES_FOLDER, "scissors", file_name + ".jpg"), mode="rb") as image_contents: tagged_images_with_regions.append( ImageFileCreateEntry(name=file_name, contents=image_contents.read(), regions=regions)) trainer.create_images_from_files(project.id, images=tagged_images_with_regions) print("Training...") iteration = trainer.train_project(project.id) while (iteration.status != "Completed"): iteration = trainer.get_iteration(project.id, iteration.id) print("Training status: " + iteration.status) time.sleep(1) # The iteration is now trained. Make it the default project endpoint trainer.update_iteration(project.id, iteration.id, is_default=True) print("Done!") return project, iteration
with open(file_name, mode="rb") as image_contents: coach_Rives.create_images_from_files(legoProject.id, [ ImageFileCreateEntry( name=file_name, contents=image_contents.read(), tag_ids=[city.id]) ]) file_name = "Images/city/racecar.jpg" with open(file_name, mode="rb") as image_contents: coach_Rives.create_images_from_files(legoProject.id, [ ImageFileCreateEntry( name=file_name, contents=image_contents.read(), tag_ids=[city.id]) ]) file_name = "Images/city/snowmobile.jpg" with open(file_name, mode="rb") as image_contents: coach_Rives.create_images_from_files(legoProject.id, [ ImageFileCreateEntry( name=file_name, contents=image_contents.read(), tag_ids=[city.id]) ]) # Fotoğrafları çeşitli tag'ler ile ilişkilendirdiğimize göre öğretimi başlatabiliriz print("lego fotoğraflarım için eğitim başlıyor") iteration = coach_Rives.train_project(legoProject.id) while (iteration.status != "Completed"): iteration = coach_Rives.get_iteration(legoProject.id, iteration.id) print("Durum..." + iteration.status) coach_Rives.update_iteration(legoProject.id, iteration.id, is_default=True) print("Eğitim tamamlandı...")