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| import cv2 as cv | |
| import numpy as np | |
| from PIL import Image | |
| import glob | |
| import pathlib | |
| import sys | |
| import u2net_utils | |
| import os | |
| from skimage import io, transform | |
| import torch | |
| import torchvision | |
| from torch.autograd import Variable | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from torch.utils.data import Dataset, DataLoader | |
| from torchvision import transforms#, utils | |
| # import torch.optim as optim | |
| from u2net_utils.data_loader import RescaleT | |
| from u2net_utils.data_loader import ToTensor | |
| from u2net_utils.data_loader import ToTensorLab | |
| from u2net_utils.data_loader import SalObjDataset | |
| from u2net_utils.model import U2NET # full size version 173.6 MB | |
| from u2net_utils.model import U2NETP # small version u2net 4.7 MB | |
| from torchvision import models | |
| import onnxruntime as ort | |
| import cv2 as cv | |
| import numpy as np | |
| from torchvision.transforms import v2 as transforms | |
| # MODEL_PATH = r"\\CATALOGUE.CGIARAD.ORG\AcceleratedBreedingInitiative\4.Scripts\AndresRuiz\local_mydata_gpu\models\u2net.pth" | |
| # MODEL_PATH = r"D:\CIAT\catalogue\AcceleratedBreedingInitiative\1.Data\16. Spidermites_AdrianK\best_models" | |
| # MODEL_PATH = r"D:\local_mydata\models\spidermites\best_models" | |
| MODEL_PATH = "./models" | |
| #************************ | |
| # from loguru import logger | |
| # from segment_anything import build_sam, SamPredictor, SamAutomaticMaskGenerator | |
| # import subprocess | |
| # # Grounding DINO | |
| # import GroundingDINO.groundingdino.datasets.transforms as T | |
| # from GroundingDINO.groundingdino.models import build_model | |
| # from GroundingDINO.groundingdino.util import box_ops | |
| # from GroundingDINO.groundingdino.util.slconfig import SLConfig | |
| # from GroundingDINO.groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap | |
| # from huggingface_hub import hf_hub_download | |
| import gc | |
| def clear(): | |
| gc.collect() | |
| torch.cuda.empty_cache() | |
| # normalize the predicted SOD probability map | |
| def normPRED(d): | |
| ma = torch.max(d) | |
| mi = torch.min(d) | |
| dn = (d-mi)/(ma-mi) | |
| return dn | |
| class BackgroundRemover(): | |
| def __init__(self): | |
| #Load model | |
| #model_dir = "/workspace/u2net.pth" | |
| #model_dir = "D:/local_mydata/models/u2net.pth" | |
| model_dir = r"\\CATALOGUE.CGIARAD.ORG\AcceleratedBreedingInitiative\4.Scripts\AndresRuiz\local_mydata_gpu\models\u2net.pth" | |
| model_dir = os.path.join(MODEL_PATH, "u2net.pth") | |
| ## Load model | |
| net = U2NET(3,1) | |
| if torch.cuda.is_available(): | |
| net.load_state_dict(torch.load(model_dir)) | |
| net.cuda() | |
| else: | |
| net.load_state_dict(torch.load(model_dir, map_location='cpu')) | |
| net.eval() | |
| self.net = net | |
| def remove_background(self, filepath_image): | |
| img_name_list = [filepath_image] | |
| test_salobj_dataset = SalObjDataset(img_name_list = img_name_list, | |
| lbl_name_list = [], | |
| transform=transforms.Compose([RescaleT(320), | |
| ToTensorLab(flag=0)]) | |
| ) | |
| test_salobj_dataloader = DataLoader(test_salobj_dataset, | |
| batch_size=1, | |
| shuffle=False, | |
| num_workers=1) | |
| net = self.net | |
| for i_test, data_test in enumerate(test_salobj_dataloader): | |
| print("inferencing:",img_name_list[i_test].split(os.sep)[-1]) | |
| inputs_test = data_test['image'] | |
| inputs_test = inputs_test.type(torch.FloatTensor) | |
| if torch.cuda.is_available(): | |
| inputs_test = Variable(inputs_test.cuda()) | |
| else: | |
| inputs_test = Variable(inputs_test) | |
| d1,d2,d3,d4,d5,d6,d7= net(inputs_test) | |
| # normalization | |
| pred = d1[:,0,:,:] | |
| pred = normPRED(pred) | |
| # save results to test_results folder | |
| #if not os.path.exists(prediction_dir): | |
| # os.makedirs(prediction_dir, exist_ok=True) | |
| #save_output(img_name_list[i_test],pred,prediction_dir) | |
| predict = pred | |
| predict = predict.squeeze() | |
| #mask_torch.permute(1, 2, 0).detach().cpu().numpy() | |
| predict_np = predict.cpu().data.numpy() | |
| img = cv.imread(filepath_image) | |
| w = img.shape[1] | |
| h = img.shape[0] | |
| #im = Image.fromarray(predict_np*255).convert('RGB') | |
| #image = io.imread(filepath_image) | |
| #imo = im.resize((image.shape[1],image.shape[0]),resample=Image.BILINEAR) | |
| imo = cv.resize(predict_np, (w,h), cv.INTER_LINEAR ) | |
| #del d1,d2,d3,d4,d5,d6,d7 | |
| return imo | |
| def remove_background_save(self, path_in, path_out, path_out_mask = None): | |
| print("remove_background_save") | |
| mask_torch = self.remove_background(path_in) | |
| mask = mask_torch*255 | |
| mask = mask.astype(np.uint8) | |
| img = cv.imread(path_in) | |
| mask0 = mask#cv.UMat(cv.imread(mask,0)) | |
| #127 | |
| #200 | |
| ret,binary_mask = cv.threshold(mask0,80,255,cv.THRESH_BINARY) | |
| binary_mask = np.uint8(binary_mask) | |
| res = cv.bitwise_and(img,img, mask = binary_mask) | |
| cv.imwrite(path_out, res) | |
| if not (path_out_mask == None): | |
| cv.imwrite(path_out_mask, mask) | |
| def remove_background_dir(self, path_in, path_out): | |
| img_name_list = glob.glob(os.path.join(path_in, "*.jpg")) | |
| for img_name in img_name_list: | |
| img_name_output = img_name.replace(path_in, path_out) | |
| if not os.path.exists(img_name_output): | |
| self.remove_background_save(img_name, img_name_output) | |
| print(img_name.replace(path_in, path_out)) | |
| def remove_background_gradio(self, np_image): | |
| w = np_image.shape[1] | |
| h = np_image.shape[0] | |
| #image = torch.tensor(np_image) | |
| #image = image.permute(2,0,1) | |
| image = np_image#Image.fromarray(np_image) | |
| imidx = np.array([0]) | |
| #label = "test" | |
| #*** | |
| label_3 = np.zeros(image.shape) | |
| label = np.zeros(label_3.shape[0:2]) | |
| if(3==len(label_3.shape)): | |
| label = label_3[:,:,0] | |
| elif(2==len(label_3.shape)): | |
| label = label_3 | |
| if(3==len(image.shape) and 2==len(label.shape)): | |
| label = label[:,:,np.newaxis] | |
| elif(2==len(image.shape) and 2==len(label.shape)): | |
| image = image[:,:,np.newaxis] | |
| label = label[:,:,np.newaxis] | |
| #*** | |
| sample = {'imidx':imidx, 'image':image, 'label':label} | |
| print(image.shape) | |
| print(label.shape) | |
| eval_transform = transforms.Compose([RescaleT(320),ToTensorLab(flag=0)]) | |
| #eval_transform = transforms.Compose([RescaleT(320)]) | |
| #eval_transform = transforms.Compose([RescaleT(320)]) | |
| #eval_transform = transforms.Compose([ToTensorLab(flag=0)]) | |
| #eval_transform = transforms.Compose([transforms.Resize(320) | |
| # , transforms.ToTensor()]) | |
| #eval_transform = transforms.Compose([transforms.Resize(320)]) | |
| test_salobj_dataloader = DataLoader(sample, | |
| batch_size=1, | |
| shuffle=False, | |
| num_workers=1) | |
| sample = eval_transform(sample) | |
| net = self.net | |
| #for i_test, data_test in enumerate(test_salobj_dataloader): | |
| #device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') | |
| #x = eval_transform(sample) | |
| #x = x[:3, ...].to(device) | |
| inputs_test = sample['image'] | |
| inputs_test = inputs_test.type(torch.FloatTensor) | |
| inputs_test = inputs_test.unsqueeze(0) | |
| print(inputs_test.shape) | |
| if torch.cuda.is_available(): | |
| inputs_test = Variable(inputs_test.cuda()) | |
| else: | |
| inputs_test = Variable(inputs_test) | |
| d1,d2,d3,d4,d5,d6,d7= net(inputs_test) | |
| # normalization | |
| pred = d1[:,0,:,:] | |
| pred = normPRED(pred) | |
| predict = pred | |
| predict = predict.squeeze() | |
| #mask_torch.permute(1, 2, 0).detach().cpu().numpy() | |
| predict_np = predict.cpu().data.numpy() | |
| imo = cv.resize(predict_np, (w,h), cv.INTER_LINEAR ) | |
| mask = imo*255 | |
| mask = mask.astype(np.uint8) | |
| mask0 = mask#cv.UMat(cv.imread(mask,0)) | |
| #127 | |
| #200 | |
| ret,binary_mask = cv.threshold(mask0,80,255,cv.THRESH_BINARY) | |
| #ret,binary_mask = cv.threshold(mask0,233,255,cv.THRESH_BINARY) | |
| binary_mask = np.uint8(binary_mask) | |
| res = cv.bitwise_and(np_image,np_image, mask = binary_mask) | |
| return mask, res | |
| def apply_mask(self, input, mask, threshold): | |
| mask = cv.cvtColor(mask, cv.COLOR_BGR2GRAY) | |
| ret,binary_mask = cv.threshold(mask,threshold,255,cv.THRESH_BINARY) | |
| #binary_mask = np.uint8(binary_mask) | |
| #binary_mask = mask | |
| print("apply mask") | |
| print(input.shape) | |
| print(input.dtype) | |
| print(binary_mask.shape) | |
| print(binary_mask.dtype) | |
| res = cv.bitwise_and(input,input, mask = binary_mask) | |
| # foreground_alpha = mask.astype(np.float32) / 255.0 | |
| # # Create a new image to store the result with same size and type as foreground | |
| # blended_image = np.zeros_like(input) | |
| # # Loop through each pixel and apply alpha based on mask value | |
| # for channel in range(3): # Loop through BGR channels | |
| # blended_image[:, :, channel] = input[:, :, channel] * foreground_alpha | |
| return res, binary_mask | |
| def get_transform(train = True): | |
| transforms_list = [] | |
| #if train: | |
| # transforms.append(T.RandomHorizontalFlip(0.5)) | |
| transforms_list.append(transforms.Resize(256)) | |
| transforms_list.append(transforms.CenterCrop(256)) | |
| #transforms_list.append(transforms.ToDtype(torch.float, scale=True)) | |
| transforms_list.append(transforms.ToTensor()) | |
| #transforms_list.append(transforms.ToDtype(torch.float32, scale=True)) | |
| transforms_list.append(transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])) | |
| return transforms.Compose(transforms_list) | |
| class DamageClassifier(): | |
| def __init__(self): | |
| self.device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') | |
| self.model_name ="" | |
| def initialize(self, model_name): | |
| #Load model | |
| if model_name == "Resnet18": | |
| model_filepath = r"\\CATALOGUE.CGIARAD.ORG\AcceleratedBreedingInitiative\1.Data\16. Spidermites_AdrianK\best_models\resnet18_SpidermitesModel.pth" | |
| model_filepath = os.path.join(MODEL_PATH, "resnet18_SpidermitesModel.pth") | |
| model = models.resnet18(weights='IMAGENET1K_V1') | |
| if model_name == "Resnet152": | |
| model_filepath = r"\\CATALOGUE.CGIARAD.ORG\AcceleratedBreedingInitiative\1.Data\16. Spidermites_AdrianK\best_models\short_resnet152_SpidermitesModel_44_44.pth" | |
| model_filepath = os.path.join(MODEL_PATH, "short_resnet152_SpidermitesModel_44_44.pth") | |
| model = models.resnet152(weights='IMAGENET1K_V1') | |
| if model_name == "Googlenet": | |
| model_filepath = r"\\catalogue.cgiarad.org\AcceleratedBreedingInitiative\1.Data\16. Spidermites_AdrianK\best_models\regnet_x_32gf_SpidermitesModel.pth" | |
| model_filepath = model_filepath = os.path.join(MODEL_PATH, "regnet_x_32gf_SpidermitesModel.pth") | |
| model = models.regnet_x_32gf(weights='IMAGENET1K_V1') | |
| if model_name == "Regnet32": | |
| model_filepath = r"\\CATALOGUE.CGIARAD.ORG\AcceleratedBreedingInitiative\1.Data\16. Spidermites_AdrianK\best_models\short_resnet18_SpidermitesModel.pth" | |
| model_filepath = model_filepath = os.path.join(MODEL_PATH, "short_resnet18_SpidermitesModel.pth") | |
| model = models.resnet18(weights='IMAGENET1K_V1') | |
| #Add fully connected layer at the end with num_classes as output | |
| num_ftrs = model.fc.in_features | |
| model.fc = nn.Linear(num_ftrs, 4) | |
| if torch.cuda.is_available(): | |
| model.load_state_dict(torch.load(model_filepath)) | |
| model.cuda() | |
| else: | |
| model.load_state_dict(torch.load(model_filepath, map_location='cpu')) | |
| model.eval() | |
| self.model = model | |
| self.model_name = model_name | |
| return | |
| def inference(self, np_image, model_name): | |
| if model_name == "Regnet": | |
| model_filepath = r"\\CATALOGUE.CGIARAD.ORG\AcceleratedBreedingInitiative\1.Data\16. Spidermites_AdrianK\best_models\regnet_x_32gf_SpidermitesModel.onnx" | |
| model_filepath = model_filepath = os.path.join(MODEL_PATH, "regnet_x_32gf_SpidermitesModel.onnx") | |
| ort_sess = ort.InferenceSession(model_filepath | |
| ,providers=ort.get_available_providers() | |
| ) | |
| transforms_list = [] | |
| transforms_list.append(transforms.ToTensor()) | |
| transforms_list.append(transforms.Resize(512)) | |
| transforms_list.append(transforms.CenterCrop(512)) | |
| transforms_list.append(transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])) | |
| apply_t = transforms.Compose(transforms_list) | |
| img = apply_t(np_image) | |
| imgs = np.array([img.numpy()]) | |
| outputs = ort_sess.run(None, {'input': [img.numpy()]}) | |
| np_res = outputs[0][0] | |
| final_res = {'0-(No damage)': np_res[0] | |
| ,'1-3-(Moderately damaged)': np_res[1] | |
| ,'4-7-(Damaged)': np_res[2] | |
| ,'8-10-(Severely damaged)': np_res[3]} | |
| return final_res | |
| else: | |
| if self.model_name != model_name: | |
| self.initialize(model_name) | |
| with torch.no_grad(): | |
| print("inference") | |
| print(np_image.shape) | |
| pil_image = Image.fromarray(np_image.astype('uint8')) | |
| data_transforms = get_transform(train = False) | |
| img = data_transforms(pil_image) | |
| inputs = img.to(self.device) | |
| outputs = self.model(inputs.unsqueeze(0)) | |
| #_, preds = torch.max(outputs, 1) | |
| print(outputs) | |
| _, preds = torch.max(outputs, 1) | |
| print(preds) | |
| m = nn.Softmax(dim=1) | |
| res = m(outputs) | |
| print(res) | |
| np_res = res[0].cpu().numpy() | |
| print(np_res) | |
| final_res = {'0-(No damage)': np_res[0] | |
| ,'1-3-(Moderately damaged)': np_res[1] | |
| ,'4-7-(Damaged)': np_res[2] | |
| ,'8-10-(Severely damaged)': np_res[3]} | |
| return final_res | |
| class ColorCheckerDetector(): | |
| def __init__(self): | |
| return | |
| def process(self, np_image_mask, np_image): | |
| ret,binary_mask = cv.threshold(np_image_mask,80,255,cv.THRESH_BINARY) | |
| binary_mask_C = cv.cvtColor(binary_mask, cv.COLOR_BGR2GRAY) #change to single channel | |
| (contours, hierarchy) = cv.findContours(binary_mask_C, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE) | |
| main_contour = contours[0] | |
| # compute the center of the contour | |
| moments = cv.moments(main_contour) | |
| cx = int(moments["m10"] / moments["m00"]) | |
| cy = int(moments["m01"] / moments["m00"]) | |
| # Bounding rect | |
| bb_x,bb_y,bb_w,bb_h = cv.boundingRect(binary_mask_C) | |
| # Min Bounding rect | |
| rect = cv.minAreaRect(main_contour) | |
| box = cv.boxPoints(rect) | |
| box = np.int64(box) | |
| # Fitting line | |
| rows,cols = binary_mask_C.shape[:2] | |
| #[vx,vy,x,y] = cv.fitLine(main_contour, cv.DIST_L2,0,0.01,0.01) | |
| [vx,vy,x,y] = cv.fitLine(box, cv.DIST_L2,0,0.01,0.01) | |
| lefty = int((-x*vy/vx) + y) | |
| righty = int(((cols-x)*vy/vx)+y) | |
| point1 = (cols-1,righty) | |
| point2 = (0,lefty) | |
| angle = np.arctan2(np.abs(righty-lefty),cols) | |
| # rotation matrix | |
| M_rot = cv.getRotationMatrix2D((cx, cy), -angle*180.0/np.pi, 1.0) | |
| rotated = cv.warpAffine(np_image, M_rot, (binary_mask.shape[1], binary_mask.shape[0])) | |
| #perspective transform | |
| input_pts = box.astype(np.float32) | |
| maxHeight = 200 | |
| maxWidth = 290 | |
| output_pts = np.float32([[0, 0], | |
| [maxWidth - 1, 0], | |
| [maxWidth - 1, maxHeight - 1] , | |
| [0, maxHeight - 1]] | |
| ) | |
| M_per = cv.getPerspectiveTransform(input_pts,output_pts) | |
| corrected = cv.warpPerspective(np_image,M_per,(maxWidth, maxHeight),flags=cv.INTER_LINEAR) | |
| res = cv.drawContours(np_image, main_contour, -1, (255,255,0), 5) | |
| res = cv.rectangle(res,(bb_x,bb_y),(bb_x+bb_w,bb_y+bb_h),(0,255,0),5) | |
| res = cv.drawContours(res,[box],0,(0,0,255),5) | |
| res = cv.line(res,(cols-1,righty),(0,lefty),(0,0,255),5) | |
| return [res, rotated, corrected] | |
| class BatchProcessor(): | |
| def __init__(self): | |
| return | |
| def batch_process(self, input_dir, output_dir, output_suffixes = ["output"], format="jpg", pattern='**/*.tiff', processing_fc=None, output_format = None): | |
| if processing_fc == None: | |
| print("Processing function is None") | |
| return | |
| else: | |
| if output_format == None: | |
| output_format = format | |
| # Get list of files in folder and subfolders | |
| pattern = '**/*.' + format | |
| files = glob.glob(pattern, root_dir=input_dir, recursive=True) | |
| for file in files: | |
| filepath = os.path.join(input_dir, file) | |
| basename = os.path.basename(filepath) | |
| parent_dir = os.path.dirname(filepath) | |
| extra_path = file.replace(basename,"") | |
| output_dir = os.path.join(output_dir, extra_path) | |
| # Create output filepath list | |
| output_filepaths = [] | |
| for suffix in output_suffixes: | |
| output_filepaths.append(os.path.join(output_dir, basename.replace("." + format, "_" + suffix + "." + output_format))) | |
| if not os.path.exists(output_filepaths[0]):# Process only if first output file does not exist | |
| if not os.path.exists(output_dir): # Create subfolders if necessary | |
| pathlib.Path(output_dir).mkdir(parents=True, exist_ok=True) | |
| processing_fc(filepath, output_filepaths) # Process and save file | |
| print(file) | |
| print(output_filepaths[0]) | |
| print("****") | |
| class Segmentor(): | |
| def __init__(self): | |
| self.sam_predictor = None | |
| self.groundingdino_model = None | |
| #self.sam_checkpoint = './sam_vit_h_4b8939.pth' | |
| #self.sam_checkpoint = r"\\CATALOGUE.CGIARAD.ORG\AcceleratedBreedingInitiative\4.Scripts\AndresRuiz\local_mydata_backup\model\sam_vit_h_4b8939.pth" | |
| self.sam_checkpoint = r"D:\local_mydev\Grounded-Segment-Anything\sam_vit_h_4b8939.pth" | |
| # self.config_file = 'GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py' | |
| # self.ckpt_repo_id = "ShilongLiu/GroundingDINO" | |
| # self.ckpt_filename = "groundingdino_swint_ogc.pth" | |
| self.config_file = r"D:\local_mydev\gsam\GroundingDINO\groundingdino\config\GroundingDINO_SwinT_OGC.py" | |
| self.ckpt_repo_id = "ShilongLiu/GroundingDINO" | |
| self.ckpt_filename = "groundingdino_swint_ogc.pth" | |
| self.device ='cpu' | |
| self.load_sam_model(self.device) | |
| self.load_groundingdino_model(self.device) | |
| return | |
| def get_sam_vit_h_4b8939(self): | |
| return | |
| # if not os.path.exists('./sam_vit_h_4b8939.pth'): | |
| # logger.info(f"get sam_vit_h_4b8939.pth...") | |
| # result = subprocess.run(['wget', '-nv', 'https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth'], check=True) | |
| # print(f'wget sam_vit_h_4b8939.pth result = {result}') | |
| def load_sam_model(self, device): | |
| sam_checkpoint = self.sam_checkpoint | |
| # initialize SAM | |
| self.get_sam_vit_h_4b8939() | |
| logger.info(f"initialize SAM model...") | |
| sam_device = device | |
| sam_model = build_sam(checkpoint=sam_checkpoint).to(sam_device) | |
| self.sam_predictor = SamPredictor(sam_model) | |
| self.sam_mask_generator = SamAutomaticMaskGenerator(sam_model) | |
| def get_grounding_output(self, model, image, caption, box_threshold, text_threshold, with_logits=True, device="cpu"): | |
| caption = caption.lower() | |
| caption = caption.strip() | |
| if not caption.endswith("."): | |
| caption = caption + "." | |
| model = model.to(device) | |
| image = image.to(device) | |
| with torch.no_grad(): | |
| outputs = model(image[None], captions=[caption]) | |
| logits = outputs["pred_logits"].cpu().sigmoid()[0] # (nq, 256) | |
| boxes = outputs["pred_boxes"].cpu()[0] # (nq, 4) | |
| logits.shape[0] | |
| # filter output | |
| logits_filt = logits.clone() | |
| boxes_filt = boxes.clone() | |
| filt_mask = logits_filt.max(dim=1)[0] > box_threshold | |
| logits_filt = logits_filt[filt_mask] # num_filt, 256 | |
| boxes_filt = boxes_filt[filt_mask] # num_filt, 4 | |
| logits_filt.shape[0] | |
| # get phrase | |
| tokenlizer = model.tokenizer | |
| tokenized = tokenlizer(caption) | |
| # build pred | |
| pred_phrases = [] | |
| for logit, box in zip(logits_filt, boxes_filt): | |
| pred_phrase = get_phrases_from_posmap(logit > text_threshold, tokenized, tokenlizer) | |
| if with_logits: | |
| pred_phrases.append(pred_phrase + f"({str(logit.max().item())[:4]})") | |
| else: | |
| pred_phrases.append(pred_phrase) | |
| return boxes_filt, pred_phrases | |
| def load_model_hf(self, model_config_path, repo_id, filename, device='cpu'): | |
| args = SLConfig.fromfile(model_config_path) | |
| model = build_model(args) | |
| args.device = device | |
| cache_file = hf_hub_download(repo_id=repo_id, filename=filename) | |
| checkpoint = torch.load(cache_file, map_location=device) | |
| print(checkpoint['model']) | |
| log = model.load_state_dict(clean_state_dict(checkpoint['model']), strict=False) | |
| print("Model loaded from {} \n => {}".format(cache_file, log)) | |
| _ = model.eval() | |
| return model | |
| def load_groundingdino_model(self, device): | |
| config_file = self.config_file | |
| ckpt_repo_id = self.ckpt_repo_id | |
| ckpt_filename = self.ckpt_filename | |
| # initialize groundingdino model | |
| logger.info(f"initialize groundingdino model...") | |
| self.groundingdino_model = self.load_model_hf(config_file, ckpt_repo_id, ckpt_filename, device=device) #'cpu') | |
| logger.info(f"initialize groundingdino model...{type(self.groundingdino_model)}") | |
| def show_mask(self, mask, random_color=False): | |
| if random_color: | |
| color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0) | |
| else: | |
| color = np.array([30/255, 144/255, 255/255, 0.6]) | |
| color = np.array([1.0, 0, 0, 1.0]) | |
| h, w = mask.shape[-2:] | |
| mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1) | |
| return mask_image | |
| def process(self, np_image, text_prompt): | |
| results = [] | |
| results.append(np_image) | |
| #results.append(np_image) | |
| sam_predictor = self.sam_predictor | |
| groundingdino_model = self.groundingdino_model | |
| image = np_image | |
| #text_prompt = text_prompt.strip() | |
| box_threshold = 0.3 | |
| text_threshold = 0.25 | |
| size = image.shape | |
| H, W = size[1], size[0] | |
| # RUN grounding dino model | |
| groundingdino_device = 'cpu' | |
| #image_dino = torch.from_numpy(image) | |
| image_dino = Image.fromarray(image) | |
| transform = T.Compose( | |
| [ | |
| T.RandomResize([800], max_size=1333), | |
| T.ToTensor(), | |
| T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), | |
| ] | |
| ) | |
| print(image.shape) | |
| image_dino, _ = transform(image_dino, None) # 3, h, w | |
| boxes_filt, pred_phrases =self.get_grounding_output( | |
| groundingdino_model, image_dino, text_prompt, box_threshold, text_threshold, device=groundingdino_device | |
| ) | |
| if sam_predictor: | |
| sam_predictor.set_image(image) | |
| if sam_predictor: | |
| for i in range(boxes_filt.size(0)): | |
| boxes_filt[i] = boxes_filt[i] * torch.Tensor([W, H, W, H]) | |
| boxes_filt[i][:2] -= boxes_filt[i][2:] / 2 | |
| boxes_filt[i][2:] += boxes_filt[i][:2] | |
| transformed_boxes = sam_predictor.transform.apply_boxes_torch(boxes_filt, image.shape[:2]) | |
| masks, _, _, _ = sam_predictor.predict_torch( | |
| point_coords = None, | |
| point_labels = None, | |
| boxes = transformed_boxes, | |
| multimask_output = False, | |
| ) | |
| print("RESULTS*************") | |
| print(len(masks)) | |
| # results = [] | |
| for mask in masks: | |
| print(type(mask)) | |
| print(mask.shape) | |
| #mask_img = mask.cpu().data.numpy() | |
| mask_img =self.show_mask(mask.cpu().numpy()) | |
| print(type(mask_img)) | |
| print(mask_img.shape) | |
| results.append(mask_img) | |
| # results.append(mask.cpu().numpy()) | |
| return results | |
| #assert sam_checkpoint, 'sam_checkpoint is not found!' | |
| return None |