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Browse files- README.md +3 -9
- app.py +138 -0
- best (5).pt +3 -0
- image.png +0 -0
- requirements.txt +17 -0
README.md
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---
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title:
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emoji: 🏢
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colorFrom: gray
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colorTo: green
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sdk: gradio
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sdk_version: 4.19.2
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app_file: app.py
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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title: plants_yolo
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app_file: app.py
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sdk: gradio
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sdk_version: 3.44.4
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---
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app.py
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# from ultralytics import YOLO
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# import cv2
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# import matplotlib.pyplot as plt
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# import matplotlib.patches as patches
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# import numpy as np
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# import requests
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# model = YOLO('best (5).pt')
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# img_url = 'https://www.greendna.in/cdn/shop/products/1296x728_Holy_Basil_1155x.jpg?v=1591462900'
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# response = requests.get(img_url, stream=True)
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# img_array = np.asarray(bytearray(response.content), dtype=np.uint8)
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# img = cv2.imdecode(img_array, cv2.IMREAD_COLOR)
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# classes_ = {0: 'anthurium', 1: 'clivia', 2: 'dieffenbachia', 3: 'dracaena', 4: 'gloxinia', 5: 'kalanchoe', 6: 'orchid', 7: 'sansevieria', 8: 'violet', 9: 'zamioculcas'}
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# results = model.predict(source=img, conf = 0.4)
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# # results = model.predict('api/default_1280-720-screenshot.webp', confidence=40, overlap=30).json()
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# boxes = results[0].boxes.xyxy.tolist()
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# classes = results[0].boxes.cls.tolist()
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# names = results[0].names
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# confidences = results[0].boxes.conf.tolist()
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# print(boxes)
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# print(classes)
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# print(names)
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# print(confidences)
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# # Iterate through the results
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# for box, cls, conf in zip(boxes, classes, confidences):
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# x1, y1, x2, y2 = box
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# confidence = conf
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# detected_class = cls
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# name = names[int(cls)]
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# def plot_img_bbox(img, target):
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# fig, a = plt.subplots(1,1)
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# fig.set_size_inches(10, 10)
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# a.imshow(img)
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# for i, box in enumerate(target):
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# #print(target['boxes'])
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# x, y, width, height = box[0], box[1], box[2]-box[0], box[3]-box[1]
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# # if arr[target['labels'][i]] == 'ad':
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# rect = patches.Rectangle((x, y),
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# width, height,
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# linewidth = 2,
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# edgecolor = 'r',
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# facecolor = 'none')
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# a.text(x, y-20, classes_[classes[i]], color='b', verticalalignment='top')
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# a.add_patch(rect)
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# plt.show()
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# # if length of boxes is zero that means no deceptive popups were found
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# plot_img_bbox(img, boxes)
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import requests
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from ultralytics import YOLO
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import cv2
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import matplotlib.pyplot as plt
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import matplotlib.patches as patches
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import numpy as np
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import gradio as gr
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model = YOLO('best (5).pt')
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def plot_img_bbox(img, target, save_path, classes):
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fig, a = plt.subplots(1, 1)
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fig.set_size_inches(10, 10)
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classes_ = {0: 'anthurium', 1: 'clivia', 2: 'dieffenbachia', 3: 'dracaena', 4: 'gloxinia', 5: 'kalanchoe', 6: 'orchid', 7: 'sansevieria', 8: 'violet', 9: 'zamioculcas'}
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a.imshow(img)
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for i, box in enumerate(target):
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x, y, width, height = box[0], box[1], box[2] - box[0], box[3] - box[1]
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rect = patches.Rectangle((x, y), width, height, linewidth=2, edgecolor='r', facecolor='none')
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a.text(x, y - 20, classes_[classes[i]], color='b', verticalalignment='top')
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a.add_patch(rect)
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plt.savefig(save_path)
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plt.close()
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upload_url = upload_to_cloudinary(save_path)
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return upload_url
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def upload_to_cloudinary(local_file_path):
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upload_url = 'https://api.cloudinary.com/v1_1/ddvajyjou/image/upload'
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files = {'file': open(local_file_path, 'rb')}
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params = {'upload_preset': 'nb6tvi1b'}
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response = requests.post(upload_url, files=files, params=params)
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if response.status_code == 200:
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return response.json()['secure_url']
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else:
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print(f"Error uploading to Cloudinary: {response.status_code}")
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return None
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def index(img_url):
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response = requests.get(img_url, stream=True)
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img_array = np.asarray(bytearray(response.content), dtype=np.uint8)
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img = cv2.imdecode(img_array, cv2.IMREAD_COLOR)
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print(img_url)
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results = model.predict(source=img, conf = 0.4)
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boxes = results[0].boxes.xyxy.tolist()
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classes = results[0].boxes.cls.tolist()
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names = results[0].names
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confidences = results[0].boxes.conf.tolist()
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print(boxes)
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print(classes)
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print(names)
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print(confidences)
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final_url = plot_img_bbox(img, boxes, 'image.png', classes)
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return final_url
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inputs_image_url = [
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gr.Textbox(type="text", label="Image URL"),
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]
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outputs_result_dict = [
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gr.Textbox(type="text", label="Result Dictionary"),
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]
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interface_image_url = gr.Interface(
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fn=index,
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inputs=inputs_image_url,
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outputs=outputs_result_dict,
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title="Popup detection",
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cache_examples=False,
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)
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gr.TabbedInterface(
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[interface_image_url],
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tab_names=['Image inference']
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).queue().launch()
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best (5).pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:249b37f0e31c33af4fd10aeb87a8e730ef1200b5966ac3ddf9e761d4e3e1f002
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size 134273570
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image.png
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requirements.txt
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flask-mongoengine @ git+https://github.com/idoshr/flask-mongoengine.git@e244408acf440c4208f7ddcd6e5d819cb472e4da
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flask
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requests
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datetime
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pandas
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numpy
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gensim
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requests
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bs4
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tensorflow
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ultralytics
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opencv-python
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matplotlib
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gunicorn
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gevent
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streamlit
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gradio
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