# Check Pytorch installation import torch, torchvision print("torch version:",torch.__version__, "cuda:",torch.cuda.is_available()) # Check MMDetection installation import mmdet import os import mmcv import mmengine from mmdet.apis import init_detector, inference_detector from mmdet.utils import register_all_modules from mmdet.registry import VISUALIZERS from huggingface_hub import hf_hub_download from huggingface_hub import snapshot_download from time import time classes = ['Beach', 'Sea', 'Wave', 'Rock', 'Breaking wave', 'Reflection of the sea', 'Foam', 'Algae', 'Vegetation', 'Watermark', 'Bird', 'Ship', 'Boat', 'Car', 'Kayak', "Shark's line", 'Dock', 'Dog', 'Unidentifiable shade', 'Bird shadow', 'Boat shadow', 'Kayal shade', 'Surfer shadow', 'Shark shadow', 'Surfboard shadow', 'Crocodile', 'Sea cow', 'Stingray', 'Person', 'Surfer', 'Surfer', 'Surfer', 'Fish', 'Killer whale', 'Whale', 'Dolphin', 'Miscellaneous', 'Unidentifiable shark', 'C Shark', 'Dusty shark', 'Blue shark', 'Great white shark', 'Shark', 'N shark', 'S shark', 'Leopard shark', 'Shortfin mako shark', 'Hammerhead shark', 'Oceanic whitetip shark', 'Blacktip shark', 'Tiger shark', 'Bull shark']*3 class_sizes = {'Beach': None, 'Sea': None, 'Wave': None, 'Rock': None, 'Breaking wave': None, 'Reflection of the sea': None, 'Foam': None, 'Algae': None, 'Vegetation': None, 'Watermark': None, 'Bird': {'feet':[1, 3], 'meter': [0.3, 0.9], 'kg': [0.5, 1.5], 'pounds': [1, 3]}, 'Ship': {'feet':[10, 100], 'meter': [3, 30], 'kg': [1000, 100000], 'pounds': [2200, 220000]}, 'Boat': {'feet':[10, 45], 'meter': [3, 15], 'kg': [750, 80000], 'pounds': [1500, 160000]}, 'Car': {'feet':[10, 20], 'meter': [3, 6], 'kg': [1000, 2000], 'pounds': [2200, 4400]}, 'Kayak': {'feet':[10, 20], 'meter': [3, 6], 'kg': [50, 300], 'pounds': [100, 600]}, "Shark's line": None, 'Dock': None, 'Dog': {'feet':[1, 3], 'meter': [0.3, 0.9], 'kg': [10, 50], 'pounds': [20, 100]}, 'Unidentifiable shade': None, 'Bird shadow': None, 'Boat shadow': None, 'Kayal shade': None, 'Surfer shadow': None, 'Shark shadow': None, 'Surfboard shadow': None, 'Crocodile': {'feet':[10, 20], 'meter': [3, 6], 'kg': [410, 1000], 'pounds': [900, 2200]}, 'Sea cow': {'feet':[9,12], 'meter': [3, 4], 'kg': [400, 590], 'pounds': [900, 1300]}, 'Stingray': {'feet':[2, 7.5], 'meter': [0.6, 2.5], 'kg': [100, 300], 'pounds': [220, 770]}, 'Person': {'feet':[5, 7], 'meter': [1.5, 2.1], 'kg': [50, 150], 'pounds': [110, 300]}, 'Ocean': None, 'Surfer': {'feet':[5, 7], 'meter': [1.5, 2.1], 'kg': [50, 150], 'pounds': [110, 300]}, 'Surfer': {'feet':[5, 7], 'meter': [1.5, 2.1], 'kg': [50, 150], 'pounds': [110, 300]}, 'Fish': {'feet':[1, 3], 'meter': [0.3, 0.9], 'kg': [20, 150], 'pounds': [40, 300]}, 'Killer whale': {'feet':[10, 20], 'meter': [3, 6], 'kg': [3600, 5400], 'pounds': [8000, 12000]}, 'Whale': {'feet':[15, 30], 'meter': [4.5, 10], 'kg': [2500, 80000], 'pounds': [55000, 176000]}, 'Dolphin': {'feet':[6.6, 13.1], 'meter': [2, 4], 'kg': [150, 650], 'pounds': [330, 1430]}, 'Miscellaneous': None, 'Unidentifiable shark': {'feet': [2, 15], 'meter': [0.6, 4.5], 'kg': [50, 1000], 'pounds': [110, 800]}, 'C Shark': {'feet': [4, 10], 'meter': [1.25, 3], 'kg': [50, 1000], 'pounds': [110, 800]}, # Prob incorrect 'Dusty shark': {'feet': [9, 14], 'meter': [3, 4.25], 'kg': [160, 180], 'pounds': [350, 400]}, 'Blue shark': {'feet': [7.9, 12.5], 'meter': [2.4, 3], 'kg': [60, 120], 'pounds': [130, 260]}, 'Great white shark': {'feet': [13.1, 20], 'meter': [4, 6], 'kg': [680, 1800], 'pounds': [1500, 4000]}, 'Shark':{'feet': [4, 10], 'meter': [1.25, 3], 'kg': [50, 1000], 'pounds': [110, 800]},# {'feet': [7.2, 10.8], 'meter': [2.2, 3.3], 'kg': [130, 300], 'pounds': [290, 660]}, 'N shark': {'feet': [4, 10], 'meter': [1.25, 3], 'kg': [50, 1000], 'pounds': [110, 800]},#{'feet': [7.9, 9.8], 'meter': [2.4, 3], 'kg': [90, 115], 'pounds': [200, 250]}, 'S shark': {'feet': [6.6, 8.2], 'meter': [2, 2.5], 'kg': [300, 380], 'pounds': [660, 840]}, 'Leopard shark': {'feet': [3.9, 4.9], 'meter': [1.2, 1.5], 'kg': [11, 20], 'pounds': [22, 44]}, 'Shortfin mako shark': {'feet': [10.5, 12], 'meter': [3.2, 3.6], 'kg': [60, 135], 'pounds': [130, 300]}, 'Hammerhead shark': {'feet': [4.9, 20], 'meter': [1.5, 6.1], 'kg': [230, 450], 'pounds': [500, 1000]}, 'Oceanic whitetip shark': {'feet': [5.9, 9.8], 'meter': [1.8, 3], 'kg': [36, 170], 'pounds': [80, 375]}, 'Blacktip shark': {'feet': [4.9, 6.6], 'meter': [1.5, 2], 'kg': [40, 100], 'pounds': [90, 220]}, 'Tiger shark': {'feet': [9.8, 18], 'meter': [3, 5.5], 'kg': [385, 635], 'pounds': [850, 1400]}, 'Bull shark': {'feet': [7.9, 11.2], 'meter': [2.4, 3.4], 'kg': [200, 315], 'pounds': [440, 690]}, } class_sizes_lower = {k.lower(): v for k, v in class_sizes.items()} classes_is_shark = [1 if 'shark' in x.lower() else 0 for x in classes] classes_is_human = [1 if 'person' or 'surfer' in x.lower() else 0 for x in classes] classes_is_unknown = [1 if 'unidentifiable' in x.lower() else 0 for x in classes] classes_is_shark_id = [i for i, x in enumerate(classes_is_shark) if x == 1] classes_is_human_id = [i for i, x in enumerate(classes_is_human) if x == 1] classes_is_unknown_id = [i for i, x in enumerate(classes_is_unknown) if x == 1] #if not os.path.exists('model'): REPO_ID = "SharkSpace/maskformer_model" FILENAME = "mask2former" snapshot_download(repo_id=REPO_ID, token= os.environ.get('SHARK_MODEL'),local_dir='model/') # Choose to use a config and initialize the detectorN config_file ='model/mask2former_swin-t-p4-w7-224_8xb2-lsj-50e_coco-panoptic/mask2former_swin-t-p4-w7-224_8xb2-lsj-50e_coco-panoptic.py' #'/content/mmdetection/configs/panoptic_fpn/panoptic-fpn_r50_fpn_ms-3x_coco.py' # Setup a checkpoint file to load checkpoint_file ='model/mask2former_swin-t-p4-w7-224_8xb2-lsj-50e_coco-panoptic/checkpoint_v2.pth' # '/content/drive/MyDrive/Algorithms/weights/shark_panoptic_weights_16_4_23/panoptic-fpn_r50_fpn_ms-3x_coco/epoch_36.pth' # register all modules in mmdet into the registries register_all_modules() # build the model from a config file and a checkpoint file model = init_detector(config_file, checkpoint_file, device='cuda:0') # or device='cuda:0' model.dataset_meta['palette'] = model.dataset_meta['palette'] + model.dataset_meta['palette'][-23:] model.dataset_meta['classes'] = classes print(model.cfg.visualizer) # init visualizer(run the block only once in jupyter notebook) visualizer = VISUALIZERS.build(model.cfg.visualizer) visualizer.img_save_dir ='temp' print(dir(visualizer)) # the dataset_meta is loaded from the checkpoint and # then pass to the model in init_detector visualizer.dataset_meta = model.dataset_meta classes = visualizer.dataset_meta.get('classes', None) palette = visualizer.dataset_meta.get('palette', None) print(len(classes)) print(len(palette)) def inference_frame_serial(image, visualize = True): #start = time() result = inference_detector(model, image) #print(f'inference time: {time()-start}') # show the results if visualize: visualizer.add_datasample( 'result', image, data_sample=result, draw_gt = None, show=False ) frame = visualizer.get_image() else: frame = None return frame, result def inference_frame(image): result = inference_detector(model, image) # show the results frames = [] cnt=0 for res in result: visualizer.add_datasample( 'result', image[cnt], data_sample=res.numpy(), draw_gt = None, show=False, ) frame = visualizer.get_image() frames.append(frame) cnt+=1 #frames = process_frames(result, image, visualizer) return frames def inference_frame_par_ready(image): result = inference_detector(model, image) return [result[i].numpy() for i in range(len(result))] def process_frame(in_tuple = (None, None, None)): visualizer.add_datasample( 'result', in_tuple[1], #image, data_sample=in_tuple[0], #result draw_gt = None, show=False ) #frame = visualizer.get_image() #print(in_tuple[2]) return visualizer.get_image() #def process_frame(frame): # def process_frames(result, image, visualizer): # frames = [] # lock = threading.Lock() # def process_data(cnt, res, img): # visualizer.add_datasample('result', img, data_sample=res, draw_gt=None, show=False) # frame = visualizer.get_image() # with lock: # frames.append(frame) # threads = [] # for cnt, res in enumerate(result): # t = threading.Thread(target=process_data, args=(cnt, res, image[cnt])) # threads.append(t) # t.start() # for t in threads: # t.join() # return frames