Spaces:
Runtime error
Runtime error
import os | |
# os.system('wget https://huggingface.co/spaces/An-619/FastSAM/resolve/main/weights/FastSAM.pt') | |
import yolov5 | |
# 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 | |
# set image | |
def license_plate_detect(img): | |
# perform inference | |
results = model(img, size=640) | |
# inference with test time augmentation | |
results = model(img, augment=True) | |
# parse results | |
if len(results.pred): | |
predictions = results.pred[0] | |
boxes = predictions[:, :4] # x1, y1, x2, y2 | |
scores = predictions[:, 4] | |
categories = predictions[:, 5] | |
return boxes | |
from PIL import Image | |
# image = Image.open(img) | |
import pytesseract | |
def read_license_number(img): | |
boxes = license_plate_detect(img) | |
if boxes: | |
return [pytesseract.image_to_string( | |
image.crop(bbox.tolist())) | |
for bbox in boxes] | |
from transformers import CLIPProcessor, CLIPModel | |
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") | |
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") | |
def zero_shot_classification(image, labels): | |
inputs = processor(text=labels, | |
images=image, | |
return_tensors="pt", | |
padding=True) | |
outputs = 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): | |
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() | |
return zero_shot_class_labels[idx].split(" ")[1-idx] | |
# from fastsam import FastSAM, FastSAMPrompt | |
# model = FastSAM('./FastSAM.pt') | |
# DEVICE = 'cpu' | |
# def segment_solar_panel(img): | |
# # os.system('python Inference.py --model_path FastSAM.pt --img_path bus.jpg --text_prompt "solar panel grids"') | |
# img = img.convert("RGB") | |
# everything_results = model(img, device=DEVICE, retina_masks=True, imgsz=1024, conf=0.4, iou=0.9,) | |
# prompt_process = FastSAMPrompt(img, everything_results, device=DEVICE) | |
# # everything prompt | |
# ann = prompt_process.everything_prompt() | |
# # bbox default shape [0,0,0,0] -> [x1,y1,x2,y2] | |
# ann = prompt_process.box_prompt(bbox=[[200, 200, 300, 300]]) | |
# # text prompt | |
# ann = prompt_process.text_prompt(text='solar panel grids') | |
# # point prompt | |
# # points default [[0,0]] [[x1,y1],[x2,y2]] | |
# # point_label default [0] [1,0] 0:background, 1:foreground | |
# ann = prompt_process.point_prompt(points=[[620, 360]], pointlabel=[1]) | |
# prompt_process.plot(annotations=ann,output_path='./bus.jpg',) | |
# return Image.Open('./bus.jpg') | |
import gradio as gr | |
def greet(img): | |
lns = read_license_number(img) | |
if len(lns): | |
# seg = segment_solar_panel(img) | |
seg = img | |
return (seg, | |
"θ»ηοΌ " + '; '.join(lns) + "\n\n" \ | |
+ "ι‘εοΌ "+ check_solarplant_installed_by_image(img, True) + "\n\n" \ | |
+ "ηζ οΌ" + check_solarplant_broken(img)) | |
return (img, "η©Ίε°γγγ") | |
iface = gr.Interface(fn=greet, inputs="image", outputs=["image", "text"]) | |
iface.launch() |