Create app.py

app.py

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