anais_test

shapeDetectionImageCrop.py

@@ -0,0 +1,43 @@
+import cv2
+
+# Load the image
+image = cv2.imread("mid_image.jpg")
+
+# Convert image to grayscale
+gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+# Resize the image to half its original size for display (pop up window)
+scale_percent = 20 # percent of original size
+width = int(gray.shape[1] * scale_percent / 100)
+height = int(gray.shape[0] * scale_percent / 100)
+dim = (width, height)
+
+resized_gray = cv2.resize(gray, dim, interpolation=cv2.INTER_AREA)
+
+# Show resized image
+cv2.imshow("Detected Rectangle", resized_gray)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
+
+
+"""# Apply Gaussian blur
+blur = cv2.GaussianBlur(gray, (5, 5), 0)
+
+# Run Canny edge detector
+edges = cv2.Canny(blur, 50, 150)
+
+# Find contours
+contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+# Select the largest contour (presumed to be the rectangle)
+contour = max(contours, key=cv2.contourArea)
+
+# Draw the detected contour on a copy of the original image
+output = image.copy()
+cv2.drawContours(output, [contour], -1, (0, 255, 0), 3)
+
+# Save or show the result
+cv2.imwrite("rectangle_edges.jpg", output)
+cv2.imshow("Detected Rectangle", output)
+cv2.waitKey(0)
+cv2.destroyAllWindows()"""

stream_logo.py

@@ -0,0 +1,44 @@
+import cv2
+from ultralytics import YOLO
+
+model = YOLO('yolov5s.pt')  # Replace with your trained logo detection model path
+cap = cv2.VideoCapture(0)  # Webcam input
+
+if not cap.isOpened():
+    print("Error: Could not open video stream.")
+    exit()
+
+while True:
+    ret, frame = cap.read()
+    if not ret:
+        break
+
+    results = model(frame)
+
+    for result in results:
+        boxes = result.boxes.xyxy.cpu().numpy()
+        scores = result.boxes.conf.cpu().numpy()
+        class_ids = result.boxes.cls.cpu().numpy()
+
+        for box, score, cls_id in zip(boxes, scores, class_ids):
+            if score < 0.3:
+                continue
+
+            x1, y1, x2, y2 = map(int, box)
+            label = model.names[int(cls_id)]
+
+            # Draw bounding box and label
+            cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
+            cv2.putText(frame, f'{label} {score:.2f}', (x1, y1 - 10),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+
+            # Log detection to console
+            print(f"Detected: {label} | Confidence: {score:.2f} | Box: ({x1}, {y1}), ({x2}, {y2})")
+
+    cv2.imshow('Logo Detection', frame)
+
+    if cv2.waitKey(1) & 0xFF == 27:
+        break
+
+cap.release()
+cv2.destroyAllWindows()

video.py

@@ -1,41 +0,0 @@
-import cv2
-from pyzbar import pyzbar
-import subprocess
-
-def detect_qr_code(frame):
-    detected_barcodes = pyzbar.decode(frame)
-    for barcode in detected_barcodes:
-        if barcode.type == 'QRCODE':
-            return barcode.data.decode('utf-8')
-    return None
-
-def main():
-    cap = cv2.VideoCapture(0)  # open default USB camera (index 0)
-    if not cap.isOpened():
-        print('Error: Cannot access camera')
-        return
-
-    print('Starting camera. Press q to quit.')
-
-    while True:
-        ret, frame = cap.read()
-        if not ret:
-            print('Error: Cannot read frame')
-            break
-
-        qr_data = detect_qr_code(frame)
-        if qr_data:
-            print(f'Detected QR Code: {qr_data}')
-            # Call the play script with this QR code data as argument
-            subprocess.Popen(['python3', 'play_spotify.py', qr_data])
-            break
-
-        cv2.imshow('QR Code Detector', frame)
-        if cv2.waitKey(1) & 0xFF == ord('q'):
-            break
-
-    cap.release()
-    cv2.destroyAllWindows()
-
-if __name__ == '__main__':
-    main()