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import os
import gradio as gr
import pytesseract
import yolov5
from transformers import CLIPProcessor, CLIPModel
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 = 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]
def greet(img):
print(type(img))
# lns = read_license_number(img)
lns = [1,2,3]
if len(lns):
# return (seg,
return ("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=gr.Image(type="pil"), outputs="text")
iface.launch() |