Create app.py

app.py

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+import gradio as gr
+from PIL import Image, ImageDraw, ImageFont
+import scipy.io.wavfile as wavfile
+
+
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+
+# used in local
+# model_path = "../models/models--facebook--detr-resnet-50/snapshots/1d5f47bd3bdd2c4bbfa585418ffe6da5028b4c0b"
+
+# tts_model_path = ("../Models/models--kakao-enterprise--vits-ljs/snapshots"
+#                   "/3bcb8321394f671bd948ebf0d086d694dda95464")
+
+
+narrator = pipeline("text-to-speech",
+                    model="kakao-enterprise/vits-ljs")
+
+object_detector = pipeline("object-detection",
+                model="facebook/detr-resnet-50")
+
+# object_detector = pipeline("object-detection",
+#                 model=model_path)
+#
+# narrator = pipeline("text-to-speech",
+#                     model=tts_model_path)
+
+# Define the function to generate audio from text
+def generate_audio(text):
+    # Generate the narrated text
+    narrated_text = narrator(text)
+
+    # Save the audio to a WAV file
+    wavfile.write("output.wav", rate=narrated_text["sampling_rate"],
+                  data=narrated_text["audio"][0])
+
+    # Return the path to the saved audio file
+    return "output.wav"
+
+
+def read_objects(detection_objects):
+    # Initialize counters for each object label
+    object_counts = {}
+
+    # Count the occurrences of each label
+    for detection in detection_objects:
+        label = detection['label']
+        if label in object_counts:
+            object_counts[label] += 1
+        else:
+            object_counts[label] = 1
+
+    # Generate the response string
+    response = "This picture contains"
+    labels = list(object_counts.keys())
+    for i, label in enumerate(labels):
+        response += f" {object_counts[label]} {label}"
+        if object_counts[label] > 1:
+            response += "s"
+        if i < len(labels) - 2:
+            response += ","
+        elif i == len(labels) - 2:
+            response += " and"
+
+    response += "."
+
+    return response
+
+
+
+def draw_bounding_boxes(image, detections, font_path=None, font_size=20):
+    """
+    Draws bounding boxes on the given image based on the detections.
+    :param image: PIL.Image object
+    :param detections: List of detection results, where each result is a dictionary containing
+                       'score', 'label', and 'box' keys. 'box' itself is a dictionary with 'xmin',
+                       'ymin', 'xmax', 'ymax'.
+    :param font_path: Path to the TrueType font file to use for text.
+    :param font_size: Size of the font to use for text.
+    :return: PIL.Image object with bounding boxes drawn.
+    """
+    # Make a copy of the image to draw on
+    draw_image = image.copy()
+    draw = ImageDraw.Draw(draw_image)
+
+    # Load custom font or default font if path not provided
+    if font_path:
+        font = ImageFont.truetype(font_path, font_size)
+    else:
+        # When font_path is not provided, load default font but it's size is fixed
+        font = ImageFont.load_default()
+        # Increase font size workaround by using a TTF font file, if needed, can download and specify the path
+
+    for detection in detections:
+        box = detection['box']
+        xmin = box['xmin']
+        ymin = box['ymin']
+        xmax = box['xmax']
+        ymax = box['ymax']
+
+        # Draw the bounding box
+        draw.rectangle([(xmin, ymin), (xmax, ymax)], outline="red", width=3)
+
+        # Optionally, you can also draw the label and score
+        label = detection['label']
+        score = detection['score']
+        text = f"{label} {score:.2f}"
+
+        # Draw text with background rectangle for visibility
+        if font_path:  # Use the custom font with increased size
+            text_size = draw.textbbox((xmin, ymin), text, font=font)
+        else:
+            # Calculate text size using the default font
+            text_size = draw.textbbox((xmin, ymin), text)
+
+        draw.rectangle([(text_size[0], text_size[1]), (text_size[2], text_size[3])], fill="red")
+        draw.text((xmin, ymin), text, fill="white", font=font)
+
+    return draw_image
+
+
+def detect_object(image):
+    raw_image = image
+    output = object_detector(raw_image)
+    processed_image = draw_bounding_boxes(raw_image, output)
+    natural_text = read_objects(output)
+    processed_audio = generate_audio(natural_text)
+    return processed_image, processed_audio
+
+
+demo = gr.Interface(fn=detect_object,
+                    inputs=[gr.Image(label="Select Image",type="pil")],
+                    outputs=[gr.Image(label="Processed Image", type="pil"), gr.Audio(label="Generated Audio")],
+                    title="@cygon: Object Detector with Audio",
+                    description="THIS APPLICATION WILL BE USED TO HIGHLIGHT OBJECTS AND GIVES AUDIO DESCRIPTION FOR THE PROVIDED INPUT IMAGE.")
+demo.launch()
+
+# print(output)