Spaces:
Runtime error
Runtime error
File size: 4,268 Bytes
79f9dfb aedefaa 8a88ada aedefaa 8a88ada aedefaa |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 |
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][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)
return (seg,
"車牌： " + '; '.join(lns) + "\n\n" \
+ "類型： "+ check_solarplant_installed_by_image(img, True) + "\n\n" \
+ "狀態：" + check_solarplant_broken(img))
return (img, "空地。。。")
iface = gr.Interface(fn=greet, inputs="image", outputs=["image", "text"])
iface.launch() |