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| import os | |
| import gradio as gr | |
| import pytesseract | |
| import yolov5 | |
| from transformers import CLIPProcessor, CLIPModel | |
| vit_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") | |
| processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") | |
| # load model | |
| model = yolov5.load('keremberke/yolov5m-license-plate') | |
| # set model parameters | |
| model.conf = 0.5 # NMS confidence threshold | |
| model.iou = 0.25 # NMS IoU threshold | |
| model.agnostic = False # NMS class-agnostic | |
| model.multi_label = False # NMS multiple labels per box | |
| model.max_det = 1000 # maximum number of detections per image | |
| def license_plate_detect(img): | |
| results = model(img, size=640) | |
| # parse results | |
| predictions = results.pred[0] | |
| if len(predictions): | |
| boxes = predictions[:, :4] # x1, y1, x2, y2 | |
| return boxes | |
| def read_license_number(img): | |
| boxes = license_plate_detect(img) | |
| if boxes is not None: | |
| return [pytesseract.image_to_string( | |
| img.crop(bbox.tolist())) | |
| for bbox in boxes] | |
| def zero_shot_classification(image, labels): | |
| print(type(image)) | |
| inputs = processor(text=labels, | |
| images=image, | |
| return_tensors="pt", | |
| padding=True) | |
| print(type(inputs)) | |
| print(inputs) | |
| outputs = vit_model(**inputs) | |
| logits_per_image = outputs.logits_per_image # this is the image-text similarity score | |
| return logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities | |
| installed_list = [] | |
| # image = Image.open(requests.get(url, stream=True).raw) | |
| def check_solarplant_installed_by_license(license_number_list): | |
| if len(installed_list): | |
| return [license_number in installed_list | |
| for license_number in license_number_list] | |
| def check_solarplant_installed_by_image(image, output_label=False): | |
| zero_shot_class_labels = ["bus with solar panel grids", | |
| "bus without solar panel grids"] | |
| probs = zero_shot_classification(image, zero_shot_class_labels) | |
| if output_label: | |
| return zero_shot_class_labels[probs.argmax().item()] | |
| return probs.argmax().item() == 0 | |
| def check_solarplant_broken(image, output_label=False): | |
| zero_shot_class_labels = ["white broken solar panel", | |
| "normal black solar panel grids"] | |
| probs = zero_shot_classification(image, zero_shot_class_labels) | |
| idx = probs.argmax().item() | |
| if output_label: | |
| return zero_shot_class_labels[idx].split(" ")[1-idx] | |
| return idx == 0 | |
| def greet(img): | |
| print(type(img)) | |
| lns = read_license_number(img) | |
| if len(lns): | |
| planttype = check_solarplant_installed_by_image(img, True) | |
| # return (seg, | |
| return ("θ»ηοΌ " + '; '.join(lns) + "\n\n" \ | |
| + "ι‘εοΌ "+ planttype + "\n\n" \ | |
| + "ηζ οΌ" + (check_solarplant_broken(img, True) | |
| if 'with' in planttype else 'normal')) | |
| return (img, "η©Ίε°γγγ") | |
| iface = gr.Interface(fn=greet, inputs=gr.Image(type="pil"), outputs="text") | |
| iface.launch() |