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Update app.py
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app.py
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import streamlit as st
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import os
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import numpy as np
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import cv2
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from PIL import Image
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import tempfile
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# TensorFlow imports
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from tensorflow.lite.python.interpreter import Interpreter
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if use_TPU:
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from tensorflow.lite.python.interpreter import load_delegate
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#
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GRAPH_NAME = 'detect.tflite'
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LABELMAP_NAME = 'labelmap.txt'
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min_conf_threshold = 0.5
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use_TPU = False # Change this based on your needs
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# Load
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with open(PATH_TO_LABELS, 'r') as f:
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labels = [line.strip() for line in f.readlines()]
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if labels[0] == '???':
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del(labels[0])
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# Load model
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interpreter = Interpreter(model_path=PATH_TO_CKPT)
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interpreter.allocate_tensors()
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output_details = interpreter.get_output_details()
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height = input_details[0]['shape'][1]
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width = input_details[0]['shape'][2]
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def
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image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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image_resized = cv2.resize(image_rgb, (width, height))
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input_data = np.expand_dims(image_resized, axis=0)
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input_data = (np.float32(input_data) - 127.5) / 127.5 # Normalize
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interpreter.set_tensor(input_details[0]['index'], input_data)
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interpreter.invoke()
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scores = interpreter.get_tensor(output_details[2]['index'])[0] # Confidence of detected objects
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for i in range(len(scores)):
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if scores[i] >
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object_name = labels[int(classes[i])]
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label = '%s: %d%%' % (object_name, int(scores[i]*100))
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return image
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import os
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import cv2
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import numpy as np
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import importlib.util
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import gradio as gr
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from PIL import Image
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# Load the TensorFlow Lite model
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MODEL_DIR = 'model'
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GRAPH_NAME = 'detect.tflite'
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LABELMAP_NAME = 'labelmap.txt'
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pkg = importlib.util.find_spec('tflite_runtime')
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if pkg:
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from tflite_runtime.interpreter import Interpreter
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from tflite_runtime.interpreter import load_delegate
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else:
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from tensorflow.lite.python.interpreter import Interpreter
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from tensorflow.lite.python.interpreter import load_delegate
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PATH_TO_CKPT = os.path.join(MODEL_DIR, GRAPH_NAME)
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PATH_TO_LABELS = os.path.join(MODEL_DIR, LABELMAP_NAME)
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# Load the label map
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with open(PATH_TO_LABELS, 'r') as f:
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labels = [line.strip() for line in f.readlines()]
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if labels[0] == '???':
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del(labels[0])
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# Load the TensorFlow Lite model
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interpreter = Interpreter(model_path=PATH_TO_CKPT)
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interpreter.allocate_tensors()
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output_details = interpreter.get_output_details()
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height = input_details[0]['shape'][1]
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width = input_details[0]['shape'][2]
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floating_model = (input_details[0]['dtype'] == np.float32)
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input_mean = 127.5
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input_std = 127.5
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outname = output_details[0]['name']
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if ('StatefulPartitionedCall' in outname):
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boxes_idx, classes_idx, scores_idx = 1, 3, 0
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else:
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boxes_idx, classes_idx, scores_idx = 0, 1, 2
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def perform_detection(image, interpreter, labels):
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imH, imW, _ = image.shape
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image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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image_resized = cv2.resize(image_rgb, (width, height))
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input_data = np.expand_dims(image_resized, axis=0)
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if floating_model:
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input_data = (np.float32(input_data) - input_mean) / input_std
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interpreter.set_tensor(input_details[0]['index'], input_data)
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interpreter.invoke()
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boxes = interpreter.get_tensor(output_details[boxes_idx]['index'])[0]
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classes = interpreter.get_tensor(output_details[classes_idx]['index'])[0]
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scores = interpreter.get_tensor(output_details[scores_idx]['index'])[0]
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detections = []
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for i in range(len(scores)):
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if ((scores[i] > 0.5) and (scores[i] <= 1.0)):
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ymin = int(max(1, (boxes[i][0] * imH)))
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xmin = int(max(1, (boxes[i][1] * imW)))
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ymax = int(min(imH, (boxes[i][2] * imH)))
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xmax = int(min(imW, (boxes[i][3] * imW)))
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cv2.rectangle(image, (xmin, ymin), (xmax, ymax), (10, 255, 0), 2)
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object_name = labels[int(classes[i])]
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label = '%s: %d%%' % (object_name, int(scores[i] * 100))
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labelSize, baseLine = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 2)
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label_ymin = max(ymin, labelSize[1] + 10)
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cv2.rectangle(image, (xmin, label_ymin - labelSize[1] - 10), (xmin + labelSize[0], label_ymin + baseLine - 10), (255, 255, 255), cv2.FILLED)
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cv2.putText(image, label, (xmin, label_ymin - 7), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0), 2)
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detections.append([object_name, scores[i], xmin, ymin, xmax, ymax])
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return image
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def detect_image(input_image):
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image = np.array(input_image)
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result_image = perform_detection(image, interpreter, labels)
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return Image.fromarray(result_image)
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def detect_video(input_video):
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cap = cv2.VideoCapture(input_video.name)
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frames = []
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while cap.isOpened():
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ret, frame = cap.read()
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if not ret:
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break
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result_frame = perform_detection(frame, interpreter, labels)
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frames.append(result_frame)
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cap.release()
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height, width, layers = frames[0].shape
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size = (width, height)
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output_video_path = "result_" + input_video.name
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out = cv2.VideoWriter(output_video_path, cv2.VideoWriter_fourcc(*'DIVX'), 15, size)
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for frame in frames:
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out.write(frame)
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out.release()
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return output_video_path
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image_input = gr.inputs.Image(type="pil", label="Upload an image")
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image_output = gr.outputs.Image(type="pil", label="Detection Result")
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video_input = gr.inputs.Video(type="file", label="Upload a video")
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video_output = gr.outputs.Video(label="Detection Result")
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app = gr.Interface(
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fn=detect_image,
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inputs=image_input,
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outputs=image_output,
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live=True,
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description="Object Detection on Images"
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)
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app_video = gr.Interface(
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fn=detect_video,
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inputs=video_input,
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outputs=video_output,
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live=True,
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description="Object Detection on Videos"
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)
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gr.TabbedInterface([app, app_video], ["Image Detection", "Video Detection"]).launch()
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