Initial commit

imagecount.py

+
+from yolov5processor.infer import ExecuteInference
+import glob
+import numpy as np
+
+yp = ExecuteInference(weight="E:\buds\best.pt")
+
+
+faulty_count = 0
+good_count = 0
+rejected_count = 0
+image = cv2.imread("E:\buds\original-predicted images\IMG_20220305_223916_jpg.rf.522aaff60984faaa45ddf1b14bc5bb9f.jpg")
+
+pred = yp.predict(image)
+print(pred)
+for det in pred:
+    conf = "{:.2f}".format(int(det['conf'] * 100))
+    if int(det['conf'] * 100) > 50:
+        # print(det['class'])
+        label = det['class']  # print(label.item())
+        det = det['points']
+        if label == "faulty":
+            cv2.rectangle(image, (int(det[0]), int(det[1])), (int(det[2]), int(det[3])), (0, 0, 255), 2)
+
+            cv2.putText(img=image, text=conf + label, org=(int(det[0]), int(det[1]) - 10), color=(0, 0, 225),
+                        thickness=1,
+                        fontScale=0.5, fontFace=cv2.LINE_AA)
+            faulty_count += 1
+        elif label =="good" :
+            cv2.rectangle(image, (int(det[0]), int(det[1])), (int(det[2]), int(det[3])), (0, 0, 255), 2)
+
+            cv2.putText(img=image, text=conf + label, org=(int(det[0]), int(det[1]) - 10), color=(0, 0, 225),
+                        thickness=1,
+                        fontScale=0.5, fontFace=cv2.LINE_AA)
+            good_count += 1
+        elif label =="rejected" :
+            cv2.rectangle(image, (int(det[0]), int(det[1])), (int(det[2]), int(det[3])), (0, 0, 255), 2)
+
+            cv2.putText(img=image, text=conf + label, org=(int(det[0]), int(det[1]) - 10), color=(0, 0, 225),
+                        thickness=1,
+                        fontScale=0.5, fontFace=cv2.LINE_AA)
+            rejected_count += 1
+print(f"good-{good_count}, faulty-{faulty_count}, rejected-{rejected_count}")
+#from google.colab.patches import cv2_imshow
+
+cv2.imshow("image",image)
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