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update app.py
Browse files
app.py
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import cv2
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import numpy as np
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import gradio as gr
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import matplotlib.pyplot as plt
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from transformers import pipeline
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model = pipeline("object-detection", "facebook/detr-resnet-50") #loading model
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#render function
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def render_results(raw_image, model_output):
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raw_image = np.array(raw_image)
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for detection in model_output:
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label = detection['label']
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score = detection['score']
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box = detection['box']
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xmin, ymin, xmax, ymax = box['xmin'], box['ymin'], box['xmax'], box['ymax']
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#Drawing the bounding box
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cv2.rectangle(raw_image, (xmin, ymin), (xmax, ymax), (0, 255, 0), 2)
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#Puting label and score near the bounding box
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cv2.putText(raw_image, f"{label}: {score:.2f}", (xmin, ymin - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
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return raw_image
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def get_object_counts(detections): ##to get count of object detected in the image
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object_counts = {}
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for detection in detections:
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label = detection['label']
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if label in object_counts:
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object_counts[label] += 1
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else:
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object_counts[label] = 1
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return object_counts
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def generate_output_text(object_counts): ##to get the output string
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output_text = "In this image there are"
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for label, count in object_counts.items():
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output_text += f" {count} {label},"
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output_text = output_text.rstrip(',') + "."
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return output_text
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def main(pil_image):
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pipeline_output = model(pil_image) #model output
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processed_image = render_results(pil_image, pipeline_output) ##process image by drawing bounding boxes
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output_text = generate_output_text(get_object_counts(pipeline_output)) ##output string
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return processed_image, output_text
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demo = gr.Interface(
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fn = main,
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inputs = gr.Image(label = "Input Image", type = "pil"),
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outputs = [gr.Image(label = "Modle output Predictions", type = "numpy"), gr.Text(label="Output Text")]
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)
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demo.launch()
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