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Update app.py
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app.py
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import os
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import yolov5
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# load model
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model = yolov5.load('keremberke/yolov5m-license-plate')
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# set model parameters
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model.conf = 0.5 # NMS confidence threshold
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model.iou = 0.25 # NMS IoU threshold
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model.agnostic = False # NMS class-agnostic
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model.multi_label = False # NMS multiple labels per box
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model.max_det = 1000 # maximum number of detections per image
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# set image
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def license_plate_detect(img):
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from PIL import Image
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# image = Image.open(img)
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import pytesseract
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def read_license_number(img):
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from transformers import CLIPProcessor, CLIPModel
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vit_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
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processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
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def zero_shot_classification(image, labels):
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installed_list = []
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# image = Image.open(requests.get(url, stream=True).raw)
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def check_solarplant_installed_by_license(license_number_list):
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def check_solarplant_installed_by_image(image, output_label=False):
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def check_solarplant_broken(image):
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from fastsam import FastSAM, FastSAMPrompt
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os.system('wget https://huggingface.co/spaces/An-619/FastSAM/resolve/main/weights/FastSAM.pt')
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@@ -109,10 +109,11 @@ def greet(img):
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lns = read_license_number(img)
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if len(lns):
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seg = segment_solar_panel(img)
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return (seg,
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return (img, "η©Ίε°γγγ")
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iface = gr.Interface(fn=greet, inputs="image", outputs=["image", "text"])
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# import os
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# import yolov5
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# # load model
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# model = yolov5.load('keremberke/yolov5m-license-plate')
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# # set model parameters
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# model.conf = 0.5 # NMS confidence threshold
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# model.iou = 0.25 # NMS IoU threshold
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# model.agnostic = False # NMS class-agnostic
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# model.multi_label = False # NMS multiple labels per box
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# model.max_det = 1000 # maximum number of detections per image
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# # set image
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# def license_plate_detect(img):
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# # perform inference
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# results = model(img, size=640)
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# # inference with test time augmentation
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# results = model(img, augment=True)
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# # parse results
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# if len(results.pred):
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# predictions = results.pred[0]
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# boxes = predictions[:, :4] # x1, y1, x2, y2
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# scores = predictions[:, 4]
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# categories = predictions[:, 5]
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# return boxes
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# from PIL import Image
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# # image = Image.open(img)
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# import pytesseract
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# def read_license_number(img):
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# boxes = license_plate_detect(img)
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# if boxes:
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# return [pytesseract.image_to_string(
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# image.crop(bbox.tolist()))
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# for bbox in boxes]
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# from transformers import CLIPProcessor, CLIPModel
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# vit_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
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# processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
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# def zero_shot_classification(image, labels):
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# inputs = processor(text=labels,
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# images=image,
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# return_tensors="pt",
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# padding=True)
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# outputs = vit_model(**inputs)
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# logits_per_image = outputs.logits_per_image # this is the image-text similarity score
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# return logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
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# installed_list = []
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# # image = Image.open(requests.get(url, stream=True).raw)
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# def check_solarplant_installed_by_license(license_number_list):
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# if len(installed_list):
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# return [license_number in installed_list
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# for license_number in license_number_list]
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# def check_solarplant_installed_by_image(image, output_label=False):
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# zero_shot_class_labels = ["bus with solar panel grids",
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# "bus without solar panel grids"]
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# probs = zero_shot_classification(image, zero_shot_class_labels)
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# if output_label:
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# return zero_shot_class_labels[probs.argmax().item()]
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# return probs.argmax().item() == 0
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# def check_solarplant_broken(image):
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# zero_shot_class_labels = ["white broken solar panel",
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# "normal black solar panel grids"]
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# probs = zero_shot_classification(image, zero_shot_class_labels)
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# idx = probs.argmax().item()
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# return zero_shot_class_labels[idx].split(" ")[1-idx]
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from fastsam import FastSAM, FastSAMPrompt
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os.system('wget https://huggingface.co/spaces/An-619/FastSAM/resolve/main/weights/FastSAM.pt')
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lns = read_license_number(img)
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if len(lns):
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seg = segment_solar_panel(img)
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return (seg, '')
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# return (seg,
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# "θ»ηοΌ " + '; '.join(lns) + "\n\n" \
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# + "ι‘εοΌ "+ check_solarplant_installed_by_image(img, True) + "\n\n" \
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# + "ηζ
οΌ" + check_solarplant_broken(img))
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return (img, "η©Ίε°γγγ")
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iface = gr.Interface(fn=greet, inputs="image", outputs=["image", "text"])
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