Delete main.py

main.py

@@ -1,62 +0,0 @@
-import os
-import cv2
-import numpy as np
-import gradio as gr
-from PIL import Image
-
-# Define path the model
-PATH_PROTOTXT   = os.path.join('saved_model/MobileNetSSD_deploy.prototxt')
-PATH_MODEL      = os.path.join('saved_model/MobileNetSSD_deploy.caffemodel')
-# Define clasess model
-CLASSES = [
-	'background', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle',
-	'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'hourse',
-	'motorbike', 'person', 'porredplant', 'sheep', 'sofa', 'train', 'tvmonitor'
-]
-
-# Load model
-NET = cv2.dnn.readNetFromCaffe(PATH_PROTOTXT, PATH_MODEL)
-
-def person_counting(image, threshold=0.7):
-    '''
-    Counting the number of people in the image
-    Args:
-        image: image to be processed
-        threshold: threshold to filter out the objects
-    Returns:
-        image: image with rectangles people detected
-        counting: count of people
-    '''
-    
-    counting = 0
-    W, H = image.shape[1], image.shape[0]
-    blob = cv2.dnn.blobFromImage(image, 0.007843, (W, H), 127.5)
-    NET.setInput(blob); detections = NET.forward()
-
-    for i in np.arange(0, detections.shape[2]):
-        conf = detections[0, 0, i, 2]
-        idx = int(detections[0, 0, i, 1])
-        if CLASSES[idx] == 'person' and conf > threshold:
-            box = detections[0, 0, i, 3:7] * np.array([W, H, W, H])
-            x_min, y_min, x_max, y_max = box.astype('int')
-            counting += 1
-            cv2.rectangle(image, pt1=(x_min,y_min), pt2=(x_max,y_max), color=(255,0,0), thickness=1)
-    return image, counting
-
-title = 'People counting'
-css = ".image-preview {height: auto !important;}"
-
-inputs = [gr.inputs.Image(source='upload'), gr.Slider(0, 1, value=0.5, label='threshold')]
-outputs = [gr.outputs.Image(label='image output'), gr.Number(label='counting')]
-examples = [[f'images/{i}', 0.5] for i in os.listdir('images')]
-
-iface = gr.Interface(
-    title   = title,
-    fn      = person_counting, 
-    inputs  = inputs, 
-    outputs = outputs,
-    examples= examples,
-    css=css
-)
-
-iface.launch()