Create app.py

app.py

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+haar-cascade-frontal-face-detection
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+haar-cascade-frontal-face-detection
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+app.py
+
+sagemaker
+remove exmples
+e427213
+over 3 years ago
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+2.1 kB
+import cv2
+import imutils
+import gradio as gr
+import numpy as np
+
+face_detector = cv2.CascadeClassifier("haar_cascades/haarcascade_frontalface_default.xml")
+
+def detect_faces(img, size, neighbours, scale):
+    frame = np.array(img) 
+    frame = frame[:, :, ::-1].copy()
+    frame = imutils.resize(frame, width=500)
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+    faceRects = face_detector.detectMultiScale(
+        gray, scaleFactor=scale, minNeighbors=neighbours, minSize=(size, size),
+        flags=cv2.CASCADE_SCALE_IMAGE)
+
+    box_data = []
+
+    class_labels = {
+        0: "face"
+    }
+
+    for (x,y,w,h) in faceRects:
+        frame = cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),2)
+
+        midX = int(x+w/2)
+        midY = int(y+h/2) 
+        box = {
+                    "position": {
+                        "middle": [midX, midY],
+                        "width": float(w),
+                        "height": float(h)
+                    },
+                    "domain" : "pixel",
+                    "class_id" : 0
+                }
+        box_data.append(box)
+
+    predictions = {"predictions": {
+            "box_data": box_data,
+            "class_labels": class_labels
+        }
+        }
+
+    re_im =cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    return re_im
+    
+image = gr.components.Image()
+out_im = gr.components.Image()
+
+size_slider = gr.components.Slider(minimum=5, maximum=50, value=30, step=5, label="MinSize in Pixel")
+neighbour_slider = gr.components.Slider(minimum=1, maximum=20, value=5, step=1, label="Min Number of Neighbours")
+scale_slider = gr.components.Slider(minimum=1.1, maximum=2.0, value=1.3, step=0.1, label="Scale Factor")
+
+description = """Face Detection with Haar Cascades using OpenCV"""
+
+
+Iface = gr.Interface(
+    fn=detect_faces,
+    inputs=[image, size_slider, neighbour_slider, scale_slider],
+    outputs=out_im,
+    #examples=[["data/9_Press_Conference_Press_Conference_9_86.jpg"], ["data/12_Group_Group_12_Group_Group_12_39.jpg"], ["data/31_Waiter_Waitress_Waiter_Waitress_31_55.jpg"]],
+    title="Haar Cascade Object Detection",
+).launch()