Spaces:
Sleeping
Sleeping
Samariddin Kushmuratov
commited on
Commit
·
adaf02d
1
Parent(s):
5029f8d
yangi qoida qoshildi
Browse files- app.py +10 -6
- assets/result_imgs/00028.jpg +0 -0
- classificatsion_video_demo.py +9 -5
- models/classification/128_1_90/args.yaml +95 -0
- models/classification/128_1_90/confusion_matrix.png +0 -0
- models/classification/128_1_90/confusion_matrix_normalized.png +0 -0
- models/classification/128_1_90/results.csv +101 -0
- models/classification/128_1_90/results.png +0 -0
- models/classification/128_1_90/train_batch0.jpg +0 -0
- models/classification/128_1_90/train_batch1.jpg +0 -0
- models/classification/128_1_90/train_batch2.jpg +0 -0
- models/classification/128_1_90/val_batch0_labels.jpg +0 -0
- models/classification/128_1_90/val_batch0_pred.jpg +0 -0
- models/classification/128_1_90/val_batch1_labels.jpg +0 -0
- models/classification/128_1_90/val_batch1_pred.jpg +0 -0
- models/classification/128_1_90/val_batch2_labels.jpg +0 -0
- models/classification/128_1_90/val_batch2_pred.jpg +0 -0
- models/classification/128_1_90/weights/best.pt +3 -0
app.py
CHANGED
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@@ -2,7 +2,7 @@ import os
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import subprocess
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import glob
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import gradio as gr
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-
from gradio.components import Gallery, Video, Textbox
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from classificatsion_video_demo import process
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@@ -25,7 +25,7 @@ def get_car_numbers(result_folder):
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os.chdir(current_dir)
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return 0
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-
def predict(video_path):
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"""
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Gradio interface orqali yuklab olingan videodan problem framelarni ajratib olinadi va resultat sifatida Galleryga chiqariladi
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:param video_path:
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@@ -33,6 +33,10 @@ def predict(video_path):
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"""
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# Your image processing code here
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# print(video_path)
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_path_RES = "assets/result_imgs"
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images_res = glob.glob(f'{_path_RES}/*.jpg')
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for image in images_res:
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@@ -41,7 +45,7 @@ def predict(video_path):
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images_res = glob.glob(f'{_path}/*.jpg')
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for image in images_res:
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os.remove(image)
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problem, good, result = process(video_path)
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get_car_numbers(_path)
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images = glob.glob(f'{_path_RES}/*.jpg')
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@@ -63,12 +67,12 @@ my_description = """
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all_frame = Textbox(label="Topilgan kadrlar / Umumiy kadrlar")
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problem_frames = Gallery(label="Muammoli kadrlar", elem_id="gallery").style(
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-
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)
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input_video = Video(label="Kiruvchi video") # Create input video component
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-
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gr.Interface(fn=predict,
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inputs=input_video,
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outputs=[all_frame,problem_frames],
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title=my_title,
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examples=my_example,
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import subprocess
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import glob
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import gradio as gr
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+
from gradio.components import Gallery, Video, Textbox, Radio
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from classificatsion_video_demo import process
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os.chdir(current_dir)
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return 0
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+
def predict(video_path, radio):
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"""
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Gradio interface orqali yuklab olingan videodan problem framelarni ajratib olinadi va resultat sifatida Galleryga chiqariladi
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:param video_path:
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"""
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# Your image processing code here
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# print(video_path)
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print("radio: ",radio,type(radio))
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which_model=True
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if radio =="128-1":
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which_model=False
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_path_RES = "assets/result_imgs"
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images_res = glob.glob(f'{_path_RES}/*.jpg')
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for image in images_res:
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images_res = glob.glob(f'{_path}/*.jpg')
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for image in images_res:
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os.remove(image)
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problem, good, result = process(video_path,which_model)
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get_car_numbers(_path)
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images = glob.glob(f'{_path_RES}/*.jpg')
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all_frame = Textbox(label="Topilgan kadrlar / Umumiy kadrlar")
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problem_frames = Gallery(label="Muammoli kadrlar", elem_id="gallery").style(
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grid_cols=[3], height="auto"
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)
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input_video = Video(label="Kiruvchi video") # Create input video component
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radio = Radio(choices=["128-1", "128-4"], label="Qoida buzilishi modda boyicha")
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gr.Interface(fn=predict,
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inputs=[input_video, radio],
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outputs=[all_frame,problem_frames],
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title=my_title,
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examples=my_example,
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assets/result_imgs/00028.jpg
CHANGED
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classificatsion_video_demo.py
CHANGED
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@@ -25,7 +25,7 @@ def train():
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metrics = model.val()
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print(metrics.top1) # top1 aniqligi
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-
def tekshirish(path2):
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"""
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test qilish, model va rasmni berishimiz kerak
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"""
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"tl-14",
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"weights/best.pt"
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)
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test_rasm_joyi =(path2)
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model_custom = YOLO("models/classification/
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natijalar = model_custom(test_rasm_joyi) # predict on an image
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natija = natijalar[0].names[np.argmax(natijalar[0].probs.cpu().numpy().data)]
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return (f"Label natija: {natija}")
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-
def process(video_path):
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# saqlash_path = video_path.split('/')[-1].split(".")[0]
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saqlash_path = "assets/images"
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if ret==True:
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#frame = cv2.flip(frame,1)
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# print(tekshirish(frame))
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-
if tekshirish(frame) == "Label natija: good":
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font = cv2.FONT_HERSHEY_COMPLEX
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cv2.putText(frame, 'good', (0, 100), font, 2, (255, 255, 255), 3)
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good_frame += 1
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elif tekshirish(frame) == "Label natija: problem":
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font = cv2.FONT_HERSHEY_COMPLEX
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cv2.putText(frame, 'problem', (0, 100), font, 2, (255, 255, 255), 3)
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# out.write(frame)
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metrics = model.val()
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print(metrics.top1) # top1 aniqligi
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def tekshirish(path2,which_mdel=True):
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"""
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test qilish, model va rasmni berishimiz kerak
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"""
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"tl-14",
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"weights/best.pt"
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)
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my_model="tl-14"
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if not which_mdel:
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my_model="128_1_90"
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test_rasm_joyi =(path2)
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model_custom = YOLO("models/classification/"+my_model+"/weights/best.pt")
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natijalar = model_custom(test_rasm_joyi) # predict on an image
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natija = natijalar[0].names[np.argmax(natijalar[0].probs.cpu().numpy().data)]
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return (f"Label natija: {natija}")
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+
def process(video_path,which_model):
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# saqlash_path = video_path.split('/')[-1].split(".")[0]
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saqlash_path = "assets/images"
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if ret==True:
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#frame = cv2.flip(frame,1)
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# print(tekshirish(frame))
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if tekshirish(frame,which_model) == "Label natija: good":
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font = cv2.FONT_HERSHEY_COMPLEX
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cv2.putText(frame, 'good', (0, 100), font, 2, (255, 255, 255), 3)
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good_frame += 1
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elif tekshirish(frame, which_model) == "Label natija: problem":
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font = cv2.FONT_HERSHEY_COMPLEX
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cv2.putText(frame, 'problem', (0, 100), font, 2, (255, 255, 255), 3)
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# out.write(frame)
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models/classification/128_1_90/args.yaml
ADDED
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task: classify
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mode: train
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model: yolov8n-cls.pt
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data: /home/cradle/work/git/traffic_laws/scripts/splitted_128_1_90
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epochs: 100
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patience: 50
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batch: 512
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imgsz: 224
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save: true
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save_period: 10
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cache: false
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device: cuda:0
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workers: 8
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project: null
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name: null
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exist_ok: false
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pretrained: false
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optimizer: SGD
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verbose: true
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seed: 0
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deterministic: true
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single_cls: false
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rect: false
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cos_lr: false
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close_mosaic: 0
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resume: false
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amp: true
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+
overlap_mask: true
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mask_ratio: 4
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dropout: 0.0
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val: true
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split: val
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save_json: false
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save_hybrid: false
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conf: null
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iou: 0.7
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max_det: 300
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half: false
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dnn: false
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plots: true
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source: null
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show: false
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save_txt: false
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save_conf: false
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save_crop: false
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show_labels: true
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show_conf: true
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vid_stride: 1
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line_width: null
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visualize: false
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augment: true
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agnostic_nms: false
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classes: null
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retina_masks: false
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boxes: true
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format: torchscript
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keras: false
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optimize: false
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int8: false
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dynamic: false
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simplify: false
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opset: null
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workspace: 4
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nms: false
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lr0: 0.01
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lrf: 0.01
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momentum: 0.937
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weight_decay: 0.0005
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warmup_epochs: 3.0
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warmup_momentum: 0.8
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warmup_bias_lr: 0.1
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box: 7.5
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cls: 0.5
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dfl: 1.5
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pose: 12.0
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kobj: 1.0
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label_smoothing: 0.0
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nbs: 64
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hsv_h: 0.015
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hsv_s: 0.7
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hsv_v: 0.4
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degrees: 0.0
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translate: 0.1
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scale: 0.5
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shear: 0.0
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perspective: 0.0
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flipud: 0.0
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fliplr: 0.5
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mosaic: 1.0
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mixup: 0.0
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copy_paste: 0.0
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cfg: null
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v5loader: false
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tracker: botsort.yaml
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save_dir: /home/cradle/work/git/traffic_laws/runs/classify/train15
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models/classification/128_1_90/confusion_matrix.png
ADDED
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models/classification/128_1_90/confusion_matrix_normalized.png
ADDED
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models/classification/128_1_90/results.csv
ADDED
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@@ -0,0 +1,101 @@
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| 1 |
+
epoch, train/loss, metrics/accuracy_top1, metrics/accuracy_top5, val/loss, lr/pg0, lr/pg1, lr/pg2
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| 2 |
+
0, 5.3959, 0.58578, 1, 8.6169, 0.070194, 0.0033118, 0.0033118
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| 3 |
+
1, 5.2537, 0.63671, 1, 8.6121, 0.040128, 0.0065794, 0.0065794
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| 4 |
+
2, 5.1909, 0.58121, 1, 8.5891, 0.009996, 0.0097809, 0.0097809
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| 5 |
+
3, 5.0829, 0.60452, 1, 8.527, 0.009703, 0.009703, 0.009703
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| 6 |
+
4, 4.919, 0.7112, 1, 7.7585, 0.009703, 0.009703, 0.009703
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| 7 |
+
5, 4.4664, 0.76862, 1, 7.5757, 0.009604, 0.009604, 0.009604
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| 8 |
+
6, 4.0698, 0.8186, 1, 6.6486, 0.009505, 0.009505, 0.009505
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| 9 |
+
7, 3.8222, 0.83013, 1, 6.3911, 0.009406, 0.009406, 0.009406
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+
8, 3.6811, 0.84623, 1, 6.2779, 0.009307, 0.009307, 0.009307
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+
9, 3.5939, 0.84791, 1, 6.1614, 0.009208, 0.009208, 0.009208
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| 12 |
+
10, 3.5058, 0.85536, 1, 6.0693, 0.009109, 0.009109, 0.009109
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| 13 |
+
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models/classification/128_1_90/weights/best.pt
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