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seg_cv01.py

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+import cv2
+from cvzone.HandTrackingModule import HandDetector
+from cvzone.ClassificationModule import Classifier
+import numpy as np
+import math
+
+cap = cv2.VideoCapture(0)
+detector = HandDetector(maxHands=1)
+classifier = Classifier('keras_model_segmentation.h5', "labels_seg.txt")
+
+offset = 20
+imgSize = 300
+counter=0
+pred=0
+labels = ['blank', 'fist', 'five', 'ok', 'thumbsdown', 'thumbsup']
+
+
+
+while True:
+    success, img = cap.read()
+    imgOutput = img.copy()
+    hands, img = detector.findHands(img, draw=False)
+    if hands:
+        hand = hands[0]
+        x, y, w, h = hand['bbox']
+
+        imgWhite = np.ones((imgSize, imgSize, 3), np.uint8) * 255
+        imgCrop = img[y - offset:y + h + offset, x - offset:x + w + offset]
+
+        imgCropShape = imgCrop.shape
+
+        aspectRatio = h / w
+
+        if aspectRatio > 1:
+            k = imgSize / h
+            wCal = math.ceil(k * w)
+            imgResize = cv2.resize(imgCrop, (wCal, imgSize))
+            imgResizeShape = imgResize.shape
+            wGap = math.ceil((imgSize - wCal) / 2)
+            imgWhite[:, wGap:wCal + wGap] = imgResize
+
+        else:
+            k = imgSize / w
+            hCal = math.ceil(k * h)
+            imgResize = cv2.resize(imgCrop, (imgSize, hCal))
+            imgResizeShape = imgResize.shape
+            hGap = math.ceil((imgSize - hCal) / 2)
+            imgWhite[hGap:hCal + hGap, :] = imgResize
+
+        modelImg = imgWhite.copy()
+        gray = cv2.cvtColor(modelImg, cv2.COLOR_BGR2GRAY)
+        gray_inverted = cv2.bitwise_not(gray)
+        contours, hierarchy = cv2.findContours(gray_inverted, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
+        _, binary = cv2.threshold(gray_inverted, 100, 255, cv2.THRESH_BINARY)
+
+        # Convert binary image to 3-channel grayscale
+        binary_rgb = cv2.cvtColor(binary, cv2.COLOR_GRAY2BGR)
+
+        # Resize the image to (224, 224)
+        resized_img = cv2.resize(binary_rgb, (224, 224))
+
+        prediction, index = classifier.getPrediction(resized_img)
+        print(prediction, index)
+
+        pred +=1
+        if index == 1:
+            counter+=1
+
+        cv2.rectangle(imgOutput, (x - offset, y - offset - 50),
+                      (x - offset + 90, y - offset - 50 + 50), (255, 0, 255), cv2.FILLED)
+        cv2.putText(imgOutput, labels[index], (x, y - 26), cv2.FONT_HERSHEY_COMPLEX, 1.7, (255, 255, 255), 2)
+        cv2.rectangle(imgOutput, (x - offset, y - offset),
+                      (x + w + offset, y + h + offset), (255, 0, 255), 4)
+
+        cv2.imshow("Segmentation", resized_img)
+        cv2.imshow("ImageCrop", imgCrop)
+        cv2.imshow("ImageWhite", imgWhite)
+
+    cv2.imshow("Image", imgOutput)
+    key = cv2.waitKey(1)
+    # Exit when 'q' is pressed
+    if key & 0xFF == ord('q'):
+        break
+
+acc = float(counter/pred)
+print(acc)
+# Release the webcam and close all windows
+cap.release()
+cv2.destroyAllWindows()